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  • richardmitnick 11:28 am on October 31, 2017 Permalink | Reply
    Tags: AGU, , Asthenosphere, , Lithosphere, , Volcanic activity causes the seafloor to spread along oceanic ridges forming new areas of crust and mantle   

    From Eos: “Seafloor Activity Sheds Light on Plate Tectonics” 

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    27 October 2017
    Sarah Witman

    1
    Seafloor topography under the Atlantic Ocean. Credit: ETOPO1/NOAA

    Much like the way humans constantly generate new skin cells, the bottom of the ocean regularly forms fresh layers of seafloor. Volcanic activity causes the seafloor to spread along oceanic ridges, forming new areas of crust and mantle. After being generated, this new oceanic lithosphere cools down and contracts by up to 3% of its own volume. This contraction can trigger oceanic earthquakes.

    The basic mechanics of tectonic plates—the massive, constantly shifting puzzle pieces that make up the Earth’s surface—are fairly well understood.

    The tectonic plates of the world were mapped in 1996, USGS.

    However, scientists cannot accurately predict how much the oceanic lithosphere will contract horizontally during the process described above.

    Sasajima and Ito studied this thermal contraction [Tectonics]by examining stress released by oceanic earthquakes over the past 55 years in newly formed sections of oceanic lithosphere (approximately 5–15 million years old). They also simulated this activity using mathematical models.

    The team found a distinct difference in two components of the released stress: one parallel to the ridge and another perpendicular to the ridge (i.e., in the seafloor spreading direction). Namely, the ridge-parallel components experienced 6 times as much extensional stress release, whereas the spreading components endured 8 times as much compressional stress release.

    In their numerical simulation, the researchers found that young oceanic lithosphere hardly ever contracts in the ridge-parallel direction. At most, it would do so only a quarter of the times that it would contract in the spreading direction. They concluded that because the layer of mantle underneath the lithosphere (the asthenosphere) is weak (low viscosity) and also because oceanic ridges are relatively weak, the young oceanic lithosphere is able to contract more freely in the spreading direction.

    This study provides critical insight into the driving and resisting forces underlying plate tectonics, one of the greatest physical phenomena in our world. (Tectonics, https://doi.org/10.1002/2017TC004680, 2017)

    See the full article here .

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  • richardmitnick 12:03 pm on October 20, 2017 Permalink | Reply
    Tags: AGU, , , , How to Trigger a Massive Earthquake   

    From Eos: “How to Trigger a Massive Earthquake” 

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    19 October 2017
    Lucas Joel

    Humans may be to blame for California’s second-largest 20th century earthquake, and a team of seismologists has now proposed how that could have happened.

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    A school in Kern County in California destroyed by the 1952 earthquake. A new study suggests that this earthquake could have been set off by nearby oil drilling activities, and it explains how that might have happened. Credit: NOAA National Geophysical Data Center

    A Los Angeles Times article published on 11 June 1952 tells of a successful new oil well at Wheeler Ridge in Kern County in California. The well operated for 98 days, but then, on 21 July at 4:52 a.m. local time, a 7.5-magnitude earthquake let loose beneath the well along the White Wolf fault. It was the second-largest earthquake in California in the 20th century, and it killed 12 people. A team of seismologists, reporting new research, thinks the oil drilling triggered the event. The work is the first to give a detailed explanation for how industrial activity could cause such a big earthquake, the researchers said.

    Taking oil out of the ground likely destabilized the White Wolf fault, triggering the Kern County quake, explained Susan Hough, a seismologist at the U.S. Geological Survey in Pasadena, Calif., and lead author of a study published this month in the Journal of Seismology.

    The work follows a 2016 Bulletin of the Seismological Society of America study in which Hough and a colleague suggest that oil drilling played a role in other historic southern California earthquakes, like the deadly 1933 6.4-magnitude Long Beach earthquake that killed 120 people. That study, however, lacked an explanation for how drilling could trigger such large quakes when modern experience shows that induced quakes rarely exceed a magnitude of even 5. This time, Hough and her colleagues propose a mechanism.

    Putting the Pieces Together

    Hough told Eos how she stumbled across old California state reports that give detailed accounts of oil drilling activity in southern California. The reports revealed evidence for a spatial and temporal association between oil industry activity and earthquakes. “From the industry data for the [oil] production volumes and the location of the well and the location of the [White Wolf] fault, we can show that the stress change on the fault would’ve been potentially significant,” she said.

    The stress change Hough refers to happened as the well pumped oil out of the ground. This, Hough explained, likely triggered the quake by “unclamping” the underlying fault. In this case, picture the fault as a fracture along an inclined plane where crustal blocks on opposite sides stall as they try to move past one another. “The fault is locked because there’s friction on the fault, and part of the reason for that is there’s the weight of the overlying crust on the fault plane,” said Hough. “But if you take some of that weight off, it shifts; it’s going to reduce the confining pressure…depending on the faults that are there, that could just destabilize what had been a locked fault.”

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    Oil wells line the Huntington Beach shoreline in southern California in 1926. In 1933, the 6.3-magnitude Long Beach earthquake struck, and according to seismologists, the temblor was likely due to oil drilling in the Huntington Beach region. Credit: Photo courtesy of Orange County Archives

    Liquids like oil, however, typically lubricate faults, making them more prone to slipping. So how could removing oil help trigger an earthquake? The answer lies in the structure of the rock layers beneath the well, which, Hough explained, prevented the oil’s lubricating effects from reaching the White Wolf fault. This means it was only a matter of removing the oily overburden that led to the fault destabilization.

    According to the team’s calculations, the amount of oil removed from above the fault generated a stress change of about 1 bar of pressure, a value that seismologists generally think of as the amount of stress change required to set an earthquake in motion, Hough explained. “After 80 days of drilling, the stress change was right at and exceeding that magic number that we think is significant,” she said.

    “They’ve developed a very plausible geologic scenario for how the Kern County earthquake could’ve been induced,” said Gillian Foulger, a geophysicist at Durham University in the United Kingdom, who was not involved in the work. “They’re really putting flesh on the bones for this particular earthquake.”

    Foulger also agrees that a modest change in the overlying weight could have been enough to set off the quake. “Earthquakes are a little bit like snow avalanches,” she said. “You can have a massive amount of snow pile up on a mountainside, and then you have a skier who skis across it and that’s just enough to trigger the disturbance that causes the whole lot to fall off.”

    Unlikely Recurrence

    Hough presents a model for initiating a large earthquake based on just one case example, although she thinks her work can apply to induced earthquakes in general: “It highlights the possibility that inducing any initial [earthquake] nucleation in proximity to a major fault could be the spark that detonates a larger rupture,” she said.

    “Nucleation” refers to the small change in stress needed to destabilize a fault—a stress change that could happen in oil-producing regions today. But the chances of producing another temblor in the manner of the Kern County earthquake are slim, according to Hough, mostly because oil fields tend not to sit above major fault lines. In addition, oil producers long ago changed to a standard practice of injecting water into the ground after oil removal, something that was not done at the Wheeler Ridge oil field and that could have restored much of the otherwise lost weight locking the fault.

    Most induced earthquakes are small—usually no bigger than a 4 magnitude—although there is no reason to suspect that humans cannot induce a big quake, explained Hough. The reason most induced quakes tend to be relatively small, she added, is that most earthquakes, in general, tend to be small. “One school of thought argues that the size distribution is the same for induced and natural earthquakes,” she said. But whether there is a maximum size limit for induced earthquakes, seismologists still do not know, she added.

    An important aspect of the new work, Foulger said, is that Hough presents a model that other scientists can test, which is a first for a large induced event like the Kern County earthquake. For Seth Stein, a geophysicist at Northwestern University in Evanston, Ill., who also had no part in the study, “the take-home is that for one of the largest earthquakes that we know of in the last hundred years, a reasonable case can be made that it was induced.”

    See the full article here .

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  • richardmitnick 1:06 pm on October 16, 2017 Permalink | Reply
    Tags: AGU, , , , Volcanic Unrest at Mauna Loa Earth’s Largest Active Volcano,   

    From Eos: “Volcanic Unrest at Mauna Loa, Earth’s Largest Active Volcano” 

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    Weston Albert Thelen
    Asta Miklius
    Christina Neal

    Mauna Loa is stirring—is a major eruption imminent? Comparisons with previous eruptions paint a complicated picture.

    1
    Oblique aerial view looking north-northeast toward the summit area of Mauna Loa volcano (elevation 4,169 meters) on 15 January 1976. The summit caldera (Moku’āweoweo) is 6 kilometers long and 2.5 kilometers wide. The pit crater in the foreground marks the start of the Southwest Rift Zone. Mauna Kea volcano is on the skyline in the distance. Credit: D. Peterson, USGS

    Mauna Loa is showing persistent signs of volcanic unrest. Since 2014, increased seismicity and deformation indicate that Mauna Loa, the volcano that dominates more than half of the island of Hawaiʻi, may be building toward its first eruption since 1984.

    Thousands of residents and key infrastructure are potentially at risk from lava flows, so a critical question is whether the volcano will follow patterns of previous eruptions or return to its now historically unprecedented 33-year slumber.

    Mauna Loa has erupted 33 times since 1843, an average of one eruption every 5 years [Trusdell, 2012]. Typical of shield-building Hawaiian volcanoes, Mauna Loa hosts a summit caldera and two rift zones, the Northeast Rift Zone (NERZ) and the Southwest Rift Zone (SWRZ; Figure 1, inset).

    Since the two most recent eruptions, in 1975 and 1984, monitoring by the U.S. Geological Survey’s Hawaiian Volcano Observatory has changed dramatically. Ground-based instruments continuously record signals from global navigation satellite systems (GNSS, of which GPS is one example), measuring the changing shape of the ground surface in near-real time, and interferometric synthetic aperture radar (InSAR) provides extensive spatial coverage of deformation. Seismic monitoring has also improved with the addition of more stations, increased data fidelity, and improved data analysis.

    More people live on the slopes of Mauna Loa now than in the 1970s and 1980s, so improvements in monitoring technology are of more than just academic interest.

    How does this recent period of unrest compare with the periods just before previous eruptions? How reliable are these comparisons in predicting the next eruption?

    2
    Fig. 1. The Italian satellite system COSMO-SkyMed acquired radar images of Mauna Loa on 1 January 2013 and 30 April 2017 to produce this ascending mode interferogram. Each fringe represents 1.5 centimeters of motion in the line-of-sight direction to the satellite. The butterfly pattern of fringes suggests an inflating tabular body beneath the caldera and uppermost Southwest Rift Zone (see inset map). The sizes of the white dots represent the magnitudes of earthquakes that occurred during this period. The arrow at the bottom left shows the direction of the satellite’s motion. The satellite’s interferometric synthetic aperture radar (InSAR) antenna looks to the right of the satellite track, and the radar contacts the land surface at about 35° off vertical. The inset is a digital elevation map of Mauna Loa showing lava flows since 1843 in red. The box shows the approximate extent of the interferogram image. COSMO-SkyMed data were provided by the Agenzia Spaziale Italiana via the Hawaiʻi Supersite.

    The Current Unrest

    Several periods of unrest have occurred at Mauna Loa since the 1984 eruption. The shallow magma storage complex started refilling (inflating) immediately following the eruption, but inflation soon slowed, and stopped altogether in the mid-1990s (Figure 2). A short-lived inflation episode began in 2002 [Miklius and Cervelli, 2003], and another began in 2004. By 2009, inflation had largely ceased. Unlike the current unrest, these previous two inflation episodes were not associated with significant numbers of shallow earthquakes; rather, they started with brief periods of deep seismicity approximately 45 kilometers beneath the surface [Okubo and Wolfe, 2008].

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    Fig. 2. Changes in distance across Moku’āweoweo, Mauna Loa’s summit caldera, and earthquakes shallower than 15 kilometers from 1973 through April 2017 in the same area as Figure 1. Because today’s sensitive instruments can detect earthquakes that previous instruments would have missed, only earthquakes greater than M1.7 are plotted. Large, abrupt extensions are associated with the formation of volcanic dikes during the 1975 and 1984 eruptions; other extensions are mostly due to accumulation of magma in shallow reservoirs. Note that this distance change is not sensitive to extension across the upper SWRZ, where most of the magma accumulation occurred between October 2015 and mid-2016. (EDM is electronic distance measuring, and MOKP and MLSP are GPS instrument sites.)

    The current unrest started in earnest in 2014 (Figure 2). Seismicity rates began to rise above background levels as early as March 2013, and by summer 2014, both seismicity and deformation rates had increased significantly. The pattern of ground deformation indicated inflation of a magma storage complex beneath the caldera and uppermost SWRZ, areas that were also the most seismically active (Figure 3).

    Beneath the caldera, seismicity consists of mostly small earthquakes (magnitude M of less than 2.5) at depths of 2–3 kilometers. These earthquakes occur in swarms lasting days to weeks, separated by months of minor activity. Event rates have been as high as 15 earthquakes per hour, with most earthquakes too small to be formally located.

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    Fig. 3. Blue arrows (with gray 95% confidence error ellipses) show the average horizontal velocities of GNSS stations on Mauna Loa from mid-2014 through 2016. Red arrows represent velocities predicted by a model of a horizontally opening tabular body extending from about 3 to 6 kilometers beneath the summit and upper Southwest Rift Zone and a radially expanding body at about 3 kilometers beneath the southeastern wall of the caldera. The surface projections of these magma reservoirs are indicated by the black line and black circle. The average rate of magma accumulation in these shallow reservoirs is on the order of 13 million cubic meters per year.

    The uppermost SWRZ has been the most seismically active region during the current unrest, in terms of overall energy release and number of earthquakes. These earthquakes are typically 3–4 kilometers below the surface. Another area of seismicity has been high on the west flank of the volcano, where swarms of small earthquakes (mostly less than M2.5) at an average depth of about 7 kilometers typically last days to a week.

    In addition to shallow seismicity, there have been several deep (greater than 20 kilometers), long-period earthquakes loosely scattered beneath the summit area. During previous periods of inflation, earthquakes with similar characteristics have been associated with magma ascent [Okubo and Wolfe, 2008].

    Short-term rates of seismicity and deformation have varied in magnitude, with weeklong to monthlong periods of relative quiescence interspersed within longer-term trends of heightened activity. Although there is general long-term correlation between deformation and seismicity rates, there is no obvious relationship between them in the short term.

    The spatial pattern of deformation and seismicity has also varied. In fall 2015, after several months of decreased inflation at the summit, seismicity beneath the caldera largely ceased, and inflation in the upper SWRZ increased (Figure 4). In May 2016, inflation and seismicity beneath the caldera slowly resumed, but as of mid-2017, rates are low compared with those seen prior to fall 2015.

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    Fig. 4. COSMO-SkyMed ascending mode interferograms show the shift in locus of inflation toward the upper Southwest Rift Zone in October 2015. Each image covers about the same length of time: (left) 18 March 2015 to 9 August 2015 and (right) 24 July 2015 to 31 December 2015. Each full-color cycle represents 1.5 centimeters of motion in the line-of-sight direction toward the satellite. Arrow shows direction of motion of the satellite. The SAR antenna looks to the right of the satellite track; the incidence angle is about 35° off vertical. COSMO-SkyMed data were provided by the Agenzia Spaziale Italiana via the Hawaii Supersite.

    Comparison with Past Eruptions

    Deformation monitoring networks in place before the 1975 and 1984 eruptions were sufficient to provide long-term indications of inflation that along with increased seismicity, led to a general forecast for the 1984 eruption [Decker et al., 1983]. However, measurements were not frequent enough to evaluate whether there were precursory changes in extension or uplift in the summit area just prior to eruption.

    Direct comparison of magma storage geometries and volumes derived from deformation patterns is also not possible because of the limited spatial and temporal extent of the early geodetic monitoring networks. Pre-1984 measurements are consistent with, but cannot confirm, the existence of a large-volume tabular storage complex (a vertical, dikelike body) beneath the summit and upper SWRZ, similar to what we currently model from GNSS and InSAR data.

    Similarly, differences in seismic network sensitivity and data processing preclude direct comparison of current seismicity rates with pre-1975 and pre-1984 rates. Patterns in the locations of earthquakes stronger than about M1.7, however, are comparable, and these patterns show a clear coincidence between the locations of seismicity during the current unrest and previous preeruption patterns (Figure 5).

    Another approach to comparing precursory seismicity is to evaluate cumulative seismic energy release, which mainly reflects energy released by larger-magnitude earthquakes (energy release increases logarithmically with respect to earthquake magnitude). Between 1 May 2013 and 30 April 2017, energy release on the west flank was equivalent to an M4.1 earthquake. For the same region, energy releases during the 4 years prior to the 1975 and 1984 eruptions were M4.2 and M4.5, respectively. In the caldera and uppermost SWRZ, the current energy release sums to M4.4, compared with M4.9 and M4.4 for the 1975 and 1984 precursory periods.

    Thus, the energy released during the current 4 or so years of unrest is approaching that released during the 4 years prior to the 1975 and 1984 eruptions. In some volcanic systems, the amount of energy release compared with previous eruptions may be an indicator of whether a period of unrest results in an eruption [Thelen et al., 2010], but this relationship has not been established on shield volcanoes such as Mauna Loa.

    One to 2 years prior to the 1975 and 1984 eruptions, swarms of small earthquakes increased in intensity. The strongest swarms included hundreds of small earthquakes per day for weeks. Bursts, as they were called, were separated by 3–6 months of relative quiet [Koyanagi et al., 1975]. Recently, swarms on the west flank have increased in number and size, but the durations of the swarms are less than pre-1975 and 1984 levels. Similarly, swarms of tiny earthquakes beneath the caldera have not occurred at rates seen in the months prior to the 1975 and 1984 eruptions.

    Interestingly, during the days to weeks prior to the past two eruptions, the number of small earthquakes fluctuated instead of building up steadily, even reaching relatively low rates for short periods prior to eruption [Koyanagi, 1987; Lockwood et al., 1987]. However, both eruptions had distinct short-term seismic precursors. The 1975 eruption was preceded by less than an hour of strong tremor in the summit caldera area [Lockwood et al., 1987]. In 1984, small (less than M0.1) earthquakes increased in frequency, shaking the ground two or three times per minute about 2.5 hours before the eruption [Koyanagi, 1987]. Harmonic tremor began about 2 hours prior to eruption, with a large increase in tremor amplitude and a swarm of earthquakes 30 minutes prior to eruption. Seven earthquakes larger than M3 occurred during a period from 30 minutes before the 1984 eruption until just over 1 hour after the onset of the eruption.

    5
    Fig. 5. Earthquake epicenters for (a) the 4 years prior to the 1975 eruption, (b) the 4 years prior to the 1984 eruption, and (c) the latest 4 years of unrest (1 May 2013 to 30 April 2017). Earthquake symbol size is based on magnitude, and color is based on depth. Only earthquakes above M1.7 are included, in an attempt to compensate for differences in network sensitivity since 1975. All earthquakes are analyst reviewed. Because the analysis of earthquakes above M1.7 is only partially complete for the current episode of unrest, event rates since 2013 may actually be slightly higher than shown here.

    Is an Eruption in Our Near Future?

    Mauna Loa’s long history of observed activity aids in forecasting another eruption, but at present, any forecast still contains a high degree of uncertainty. Some aspects of the current unrest are similar to unrest prior to eruptions in 1975 and 1984. Earthquake locations, temporal behavior, and energy release suggest that the volcano may be following a similar pattern. Other aspects, however, differ from the periods prior to the 1975 and 1984 eruptions.

    During the current unrest period, we have not observed the kind of moderate to large flank earthquakes that preceded many historical eruptions [Walter and Amelung, 2006], including the 1975 and 1984 eruptions. Also, as of fall 2017, we have not seen the high rates of small earthquakes observed about 7–14 months prior to the 1975 and 1984 eruptions, even though our ability to detect them has improved. Thus, if current unrest follows previous patterns of seismicity, we may expect that the volcano is still many months from eruption.

    We must also consider that current unrest might not follow previous patterns, and an eruption could occur without months of elevated microseismicity. It is possible that after years of intermittent inflation, shallow magma storage is exerting pressures already near the breaking point of the overlying rock.

    We can’t say for certain whether there will be a precursory months-long increase in microseismicity before the next Mauna Loa eruption. However, an eruption will likely be immediately preceded by an hours-long, dramatic increase in small earthquakes (at least one earthquake per minute), strong tremor, and the occurrence of several M3 or stronger earthquakes, similar to the lead-up to the 1975 and 1984 eruptions. Real-time deformation data from tiltmeters and GNSS stations will show large anomalies as magma moves from storage reservoirs toward the surface to the eventual eruption site in the summit area and/or along one of the rift zones or (less likely) from radial vents on the west flank.

    It is also possible that current elevated rates of seismicity and deformation may not culminate in eruption anytime soon; rather, this could be yet another episode of unrest that gradually diminishes. During the 25-year repose between the 1950 and 1975 eruptions, seismic unrest in 1962, 1967, and 1970 did not lead to eruption, although in hindsight, each is considered a long-term precursor to the 1975 eruption [Koyanagi et al., 1975].

    The high rate of volcanic activity at neighboring Kīlauea volcano complicates assessing the likelihood of a Mauna Loa eruption in the coming months or years. Klein [1982] noted that longer repose intervals at Mauna Loa were statistically correlated with eruptive activity at Kīlauea. Indeed, the current long repose time at Mauna Loa is occurring at the same time as the long-lived Puʻu ʻŌʻō eruption at Kīlauea, which began in 1983 and continues today. Even so, the most recent eruption of Mauna Loa in 1984 occurred during this eruption at Kīlauea, so the impact of nearby volcanic activity on Mauna Loa’s behavior over short timescales is unknown.

    We can make one forecast with relative certainty: On the basis of nearly 200 years of documented activity, it is highly likely that the next eruption will begin in the summit region and then, within days to years, migrate into one of the two primary rift zones [Lockwood et al., 1987].

    It is important to note that seismicity and inflation beneath the uppermost SWRZ do not imply an increased likelihood of eruption along the SWRZ. Similar patterns of seismicity prior to the 1975 and 1984 eruptions did not result in sustained activity in the SWRZ. In 1984, the eruption began at the summit and migrated to the upper SWRZ before activity focused along the NERZ, suggesting that a magma body extending into the uppermost SWRZ—similar to that inferred from current data—was also active prior to that eruption.

    Communicating the Hazards

    In response to more than a year of persistently elevated rates of seismicity and deformation, the Hawaiian Volcano Observatory (HVO) elevated the Volcano Alert Level and Aviation Color Code for Mauna Loa to advisory/yellow on 17 September 2015, indicating that the volcano was restless and that monitoring parameters were above the long-term background levels.

    Since then, HVO has continued public education efforts and engaged agency partners, including Hawaiʻi County Civil Defense and the National Park Service, to discuss preparedness and response planning. In 2016, HVO installed new web cameras and upgraded real-time gas and temperature sensors in the summit caldera. Alarms have been set to alert scientists to significant changes in several data streams, including real-time seismic amplitude (a measure of seismic energy release), ground tilt, and satellite- and ground-based thermal imagery. Revised maps showing potential inundation zones and likely lava flow paths based on topography derived from digital elevation maps have been prepared.

    As with any precursory volcanic eruption sequence, it will be challenging to choose the correct time to alert authorities and elevate public concern about a possible eruption. Once an eruption has commenced, pinpointing the exact location of the outbreak—especially at night or in cloudy conditions—may not be straightforward and may require the use of new tools such as infrasound. Vent location determines which downslope areas are at greatest risk, so addressing this capability gap is a high priority.

    As of this writing, elevated rates of seismicity and deformation continue. Improvements in monitoring networks and alarming systems since 1984 put HVO in a better position to provide early warning and, once an eruption has commenced, help guide emergency response. Additional efforts to inform and prepare the public for the eventual eruption are an important step in minimizing impacts to life and property.

    References

    Decker, R. W., et al. (1983), Seismicity and surface deformation of Mauna Loa volcano, Hawaii, Eos Trans. AGU, 64(37), 545–547, https://doi.org/10.1029/EO064i037p00545-01.

    Klein, F. W. (1982), Patterns of historical eruptions at Hawaiian volcanoes, J. Volcanol. Geotherm. Res., 12, 1–35, https://doi.org/10.1016/0377-0273(82)90002-6.

    Koyanagi, R. Y. (1987), Seismicity associated with volcanism in Hawaii: Application to the 1984 eruption of Mauna Loa volcano, U.S. Geol. Surv. Open File Rep., 87-277, 76 pp.

    Koyanagi, R. Y., E. T. Endo, and J. S. Ebisu (1975), Reawakening of Mauna Loa volcano, Hawaii: A preliminary evaluation of seismic evidence, Geophys. Res. Lett., 2(9), 405–408, https://doi.org/10.1029/GL002i009p00405.

    Lockwood, J. P., et al. (1987), Mauna Loa 1974–1984: A decade of intrusive and extrusive activity, in Volcanism in Hawaii, chap. 19, U.S. Geol. Surv. Prof. Pap., 1350, 537–570.

    Miklius, A., and P. Cervelli (2003), Interaction between Kīlauea and Mauna Loa, Nature, 421, 229, https://doi.org/10.1038/421229a.

    Okubo, P. G., and C. J. Wolfe (2008), Swarms of similar long-period earthquakes in the mantle beneath Mauna Loa volcano, J. Volcanol. Geotherm. Res., 178, 787–794, https://doi.org/10.1016/j.jvolgeores.2008.09.007.

    Thelen, W. A., S. D. Malone, and M. E. West (2010), Repose time and cumulative moment magnitude: A new tool for forecasting eruptions?, Geophys. Res. Lett., 37, L18301, https://doi.org/10.1029/2010GL044194.

    Trusdell, F. A. (2012), Mauna Loa—History, hazards, and risk of living with the world’s largest volcano, U.S. Geol. Surv. Fact Sheet, 2012-3104, 4 pp., https://pubs.usgs.gov/fs/2012/3104/.

    Walter, T. R., and F. Amelung (2006), Volcano-earthquake interaction at Mauna Loa volcano, Hawaii, J. Geophys. Res., 111, B05204, https://doi.org/10.1029/2005JB003861.

    Author Information

    Weston Albert Thelen (email: wthelen@usgs.gov), Cascade Volcano Observatory, U.S Geological Survey, Vancouver, Wash.; and Asta Miklius and Christina Neal, Hawaiian Volcano Observatory, U.S. Geological Survey, Hawaiʻi National Park, Hawaii

    See the full article here .

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  • richardmitnick 10:10 am on September 27, 2017 Permalink | Reply
    Tags: AGU, Drone Peers into Open Volcanic Vents, , Fortunately robotic technology can go where humans cannot, , UAVs-Unmanned aerial vehicles,   

    From Eos: “Drone Peers into Open Volcanic Vents” 

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    9.27.17
    Nicolas Turner, Bruce Houghton, Jacopo Taddeucci, Jost von der Lieth, Ullrich Kueppers, Damien Gaudin, Tullio Ricci, Karl Kim, and Piergiorgio Scalato

    1
    Stromboli, one of the world’s most active volcanoes, ejects large, hot volcanic bombs in this long-exposure image of the northeastern region of the summit crater terrace. During a May 2016 pilot project, the authors sent unmanned aerial vehicles where humans couldn’t go to capture images and gather data on the locations and characteristics of Stromboli’s craters and vents. Credit: Rainer Albiez/Shutterstock.com

    Volcanoes that erupt frequently give researchers special opportunities to make repeated high-resolution observations of rapid eruption processes. At explosive volcanoes like Stromboli, situated just off the southwestern coast of Italy, “normal” explosive eruptions take place every few minutes to tens of minutes. However, the rainout of hot volcanic bombs, some as large as a meter across, makes it hazardous for scientists and their instruments to get close enough to Stromboli’s active vents to collect some forms of essential data.

    At volcanoes like Stromboli, we can make many key observations from safe locations, hundreds of meters from the erupting vents. Other key observations must be made from locations beside or immediately above the vents. These underrecorded observations include the exact locations of vents, their dimensions, and the depth to the magma’s free surface. These observations have generally not been feasible because explosions occur at irregular frequencies, and they seldom provide warning; our inability to make these observations has been a major impediment to our models of the eruption process.

    Fortunately, robotic technology can go where humans cannot. Unmanned aerial vehicles (UAVs) have become cheaper and more accessible, and they now have the ability to carry lightweight optical sensors for mapping and aerial observations of volcanic activity.

    2
    Fig. 1. (top) Digital point cloud model of Stromboli crater terrace showing the two vent areas of the crater terrace. (middle) Digital elevation models (DEMs) of the northeast and southwest vent areas showing morphology. (bottom) Classification maps of active vents, inactive vents, and fumaroles from the high-resolution DEMs, aerial imagery, and low-altitude video observations of activity for active vents and fumaroles. Inactive vents were identified by examining the DEMs for features with morphology similar to that of the active vents.

    We used a UAV in a May 2016 pilot survey campaign at Stromboli to map detailed features of the active crater terrace and produce a high-resolution digital elevation model, with details as small as about 5 centimeters. Different vent areas within this crater terrace host active and inactive vents as well as fumaroles, and the UAV helped us determine their locations and dimensions.

    What We Do Know

    There are many things scientists can observe from volcanoes without drones. For example, remote observations from closely positioned cameras and sensors can record initial velocities for the erupted particles (bombs and ash) [Patrick et al., 2007; Gaudin et al., 2016], event durations and mass eruption rates [Taddeucci et al., 2012; Rosi et al., 2013; Gaudin et al., 2014], temperature and flux of gas species [Burton et al., 2007], and the sizes of the ejected bombs [Gurioli et al., 2013; Bombrun et al., 2015 (sorry, no links)].

    3
    Fig. 2. A sudden shift in wind direction momentarily sweeps away a gas cloud, giving the UAV a clear view of the interior of the crater and revealing an active vent (2) and an inactive vent (3). Active vent 1 is obscured, but it lies directly behind the bluish gas plume to the left of vent 2.

    Through recent field campaigns at Stromboli, scientists have gathered excellent time-synchronized databases of geophysical data and observations of eruption timings, pulsations, and mass flux derived from seismometers, high-speed cameras, webcams, Doppler radar, and infrasound sensors [e.g., Scarlato et al., 2014]. MultiGAS instruments (which combine optical and electrochemical sensors), Fourier transform infrared spectrometers, and ultraviolet and thermal infrared cameras have captured the nature and flux of key magmatic gases (water, carbon dioxide, and sulfur gases) associated with the activity.

    These sensors, however, have their limits. UAVs, however, can push data collection beyond these limits.

    Soaring Above the Danger

    Over the past 2 decades, Stromboli has been surveyed with lidar technology, but because of the high costs involved with lidar surveys, they are not conducted frequently enough to capture rapid changes at the summit of Stromboli.

    5
    Fig. 3. The UAV captured this visible-light still frame of an expanding ash-rich explosion from the dominant vent 4. The ash plume is more than 300 meters high.

    UAV-based mapping can supplement or replace lidar surveys. UAVs, popularly known as drones, were first used at Stromboli in 2007 to collect ash samples [Taddeucci et al., 2007]. Since that time, they have become cheaper, smarter, and able to carry higher-quality imaging sensors, such as the X5 camera mounted on the DJI Inspire-1 quadcopter for our 2016 study.

    Advances in the field of computer vision have also yielded software capable of extracting 3-D topography from multiple 2-D images in a process called structure from motion. Pairing this practical technique with UAVs has resulted in widespread adoption across multiple fields in science, and volcanology is no exception [James and Robson, 2012].

    UAVs, in combination with the live webcams deployed around the crater terrace [Fornaciai et al., 2010; Calvari et al., 2016], provide an unprecedented level of monitoring via imaging and sampling eruption plumes. UAVs could even be used to deploy sensors near or inside the vent.

    What’s more, with an extended range, UAVs could allow observations when human access to the summit is forbidden and dangerous. This capability would be especially useful, for example, during the rare paroxysmal phases, which can last several months.

    Given this potential, we decided to put the utility of UAVs to the test.

    First Results

    Even UAVs face challenges like constant gas emissions, high and gusting winds, and unpredictable explosions when mapping volcanic environments like Stromboli. In the worst case, UAVs may be severely damaged, or their data may be lost.

    During our 2016 campaign, explosions proved the most challenging to cope with: On more than one occasion, the UAV was almost engulfed by a rising ash plume while it was mapping directly above an active vent. Active fumaroles constantly sent up clouds of gas, condensed water vapor, and ash particles, making it difficult for the onboard camera to get clear images of these vents for use in mapping the interior of the source craters. Fortunately, shifts in wind would briefly clear the craters, providing the camera with an occasional clear view, as seen in the video below.


    During a May 2016 mapping survey, an unmanned aerial vehicle (drone) captured this close-up view of several active vents on the slopes of Stromboli, one of the world’s most active volcanoes, situated just off the southwestern coast of Italy. Stromboli often produces explosive eruptions every few minutes, making it too hazardous for researchers to approach these vents to take data. Credit: Nicolas Turner and coauthors.

    We constructed the final maps of the crater terrace (Figure 1) by selecting the highest-quality images from more than a dozen mapping flights over 2 days. We processed the images with structure from motion software to construct a relatively gas free orthomosaic (aerial map corrected for distortions) and accompanying digital elevation model.

    6
    Fig. 4. The UAV captured this image of vents 2 and 5 in the northeast and southwest regions, respectively, between explosions.

    We mapped a total of 4 active vents, 11 inactive vents, and 33 fumaroles in the southwest and northeast vent areas present in May 2016 (Figure 1). There is no clear pattern to vent distribution within the larger structures on the crater terrace. Inactive vents, particularly fumaroles, tend to occur in clusters without a single, consistent orientation. The two principal northeast vents are aligned approximately east–west and are 69 meters apart. The two principal southwest vents are 55 meters apart and aligned roughly north to south.

    Overall, the northeastern active vents were similar to each other in depth and diameter (Figure 2), but the southwestern active vents were significantly deeper. Inactive vents were much shallower than active vents. During a week of observations, the southwestern vents produced the larger and more powerful explosions. Explosions from vent 4 (see Figure 1) were typically ash charged, and the free surface was generally covered by debris between successive explosions. These plumes often reached heights of several hundred meters (Figure 3).

    In contrast, an incandescent free surface was often visible in vent 5 and the active northeastern vents (see Figure 4), and in Figure 5, spattering and outgassing are clearly visible in vent 2 during a repose interval.

    7
    Fig. 5. Close-up view of vent 2 showing weak discontinuous spattering activity that continued between explosions.

    Implications for Eruption Processes and Volcano Monitoring

    This pilot survey yielded useful data, and it serves as a guide for future more ambitious and repeated deployments. The data we gathered—the precise source locations of explosions, for example—can potentially help us reduce the uncertainty in geophysical model inputs for seismic and acoustic arrays and gravity measurements.

    We hope to establish precise correlations of changing eruption style and intensity with time for single vents, along with synchronous observations of depths to the free surface of magma in the parent vents. This and future deployments will help us to monitor abrupt and progressive changes in the diameter of single vents over time and the influence of these changes on eruptive behavior.

    8
    Near-infrared picture of a bomb-bearing explosion from vent 5. Light tones denote high (incandescent) temperatures. The vertical field of view is 20.5 meters.

    Our data will assist in making comparisons of how differences in vent width and depth to the free surface, the orientation and inclination of the conduit, and the extent of debris covering the free surface all influence contrasting eruption behavior at adjacent vents.

    The ability to capture such detail at an active volcano offers the opportunity to greatly enhance programs for short-term as well as long-term volcano monitoring. Scientists modeling Strombolian eruptions can use these new data to reduce the uncertainty in numerical model input parameters (e.g., conduit and acoustic modeling).

    The low cost and safety of UAV operations allow small-scale changes to be captured and UAV surveys to be launched as frequently as necessary. These benefits make UAVs a critical complement to other remote sensing and geophysical techniques.

    Acknowledgments

    This study was supported by funding from the National Science Foundation (NSF EAR14-27357) and the VERTIGO Marie Curie ITN, funded through the European Seventh Framework Programme (FP7 2007-2013) under grant agreement 607905. We acknowledge numerous companions during the 2016 campaign at Stromboli, especially Elisabetta del Bello, Valeria Cigala, Bianca Mintz, and Pierre-Yves Tournigand.

    References

    Bombrun, M., et al. (2015), Anatomy of a Strombolian eruption: Inferences from particle data recorded with thermal video, J. Geophys. Res. Solid Earth, 120, 2367–2387, https://doi.org/10.1002/2014JB011556.

    Burton, M., et al. (2007), Magmatic gas composition reveals the source depth of slug-driven Strombolian explosive activity, Science, 317, 227–230, https://doi.org/10.1126/science.1141900.

    Calvari, S., et al. (2016), Monitoring crater-wall collapse at active volcanoes: A study of the 12 January 2013 event at Stromboli, Bull. Volcanol., 78, 39, https://doi.org/10.1007/s00445-016-1033-4.

    Fornaciai, A., et al. (2010), A lidar survey of Stromboli volcano (Italy): Digital elevation model-based geomorphology and intensity analysis, Int. J. Remote Sens., 31, 3177–3194, https://doi.org/10.1080/01431160903154416.

    Gaudin, D., et al. (2014), Pyroclast tracking velocimetry illuminates bomb ejection and explosion dynamics at Stromboli (Italy) and Yasur (Vanuatu) volcanoes, J. Geophys. Res. Solid Earth, 119, 5384–5397, https://doi.org/10.1002/2014JB011096.

    Gaudin, D., et al. (2016), 3‐D high‐speed imaging of volcanic bomb trajectory in basaltic explosive eruptions, Geochem. Geophys. Geosyst., 17, 4268–4275, https://doi.org/10.1002/2016GC006560.

    Gurioli, L., et al. (2013), Classification, landing distribution, and associated flight parameters for a bomb field emplaced during a single major explosion at Stromboli, Italy, Geology, 41, 559–562, https://doi.org/10.1130/G33967.1.

    Harris, A. J. L., et al. (2013), Volcanic plume and bomb field masses from thermal infrared camera imagery, Earth Planet. Sci. Lett., 365, 77–85, https://doi.org/10.1016/j.epsl.2013.01.004.

    James, M. R., and S. Robson (2012), Straightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application, J. Geophys. Res., 117, F03017, https://doi.org/10.1029/2011JF002289.

    Patrick, M. R., et al. (2007), Strombolian explosive styles and source conditions: Insights from thermal (FLIR) video, Bull. Volcanol., 69, 769–784, https://doi.org/10.1007/s00445-006-0107-0.

    Rosi, M., et al. (2013), Stromboli volcano, Aeolian Islands (Italy): Present eruptive activity and hazards, Geol. Soc. London Mem., 37, 473–490, https://doi.org/10.1144/M37.14.

    Scarlato, P., et al. (2014), The 2014 Broadband Acquisition and Imaging Operation (BAcIO) at Stromboli Volcano (Italy), Abstract V41B-4813 presented at the 2014 Fall Meeting, AGU, San Francisco, Calif.

    Taddeucci, J., et al. (2007), Advances in the study of volcanic ash, Eos Trans. AGU, 88, 253, https://doi.org/10.1029/2007EO240001.

    Taddeucci, J., et al. (2012), High-speed imaging of Strombolian explosions: The ejection velocity of pyroclasts, Geophys. Res. Lett., 39, L02301, https://doi.org/10.1029/2011GL050404.

    Author Information

    Nicolas Turner (email: nrturner@hawaii.edu; @nicolasrturner) and Bruce Houghton, Department of Geology and Geophysics, University of Hawai‘i at Mānoa, Honolulu; Jacopo Taddeucci, Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy; Jost von der Lieth, Department of Earth Sciences, Universität Hamburg, Germany; Ullrich Kueppers and Damien Gaudin, Ludwig-Maximilians-Universität München, Munich, Germany; Tullio Ricci, Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy; Karl Kim, National Disaster Preparedness Training Center, University of Hawai‘i at Mānoa, Honolulu; and Piergiorgio Scalato, Istituto Nazionale di Geofisica e Vulcanologia, Rome, Italy
    Citation: Turner, N., B. Houghton, J. Taddeucci, J. von der Lieth, U. Kueppers, D. Gaudin, T. Ricci, K. Kim, and P. Scalato (2017), Drone peers into open volcanic vents, Eos, 98, https://doi.org/10.1029/2017EO082751. Published on 27 September 2017.

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  • richardmitnick 3:06 pm on September 1, 2017 Permalink | Reply
    Tags: A Grand Tour of the Ocean Basins, AGU, ,   

    From Eos: “A Grand Tour of the Ocean Basins” 

    AGU bloc

    AGU
    Eos news bloc

    Eos

    9/1/17
    Declan G. De Paor

    A new teaching resource facilitates plate tectonic studies using a Google Earth virtual guided tour of ocean basins around the world.

    1
    Google Earth images provide detailed views of Earth’s continents and oceans. Custom overlays enhance the images, turning them into resources for instructors and students studying plate tectonic theory and other topics. A new online teaching resource takes advantage of Google Earth to offer a virtual tour of the world’s ocean basins, providing insights into the processes that shape oceans and continents. This Google Earth image displays data overlays showing ages of the ocean floor, together with tectonic plate boundaries. Credit: Models: Age of the Lithosphere for Google Earth and Using Google Earth to Explore Plate Tectonics. All figures showing Google Earth are ©2017, Google Inc. Images: PGC/NASA, Landsat/Copernicus, USGS. Data: SIO, NOAA, U.S. Navy, NGA, GEBC, USGS.

    Students, especially those at the beginner levels, are often presented with simplistic visualizations of plate tectonics that lack the rich detail and recent science available to researchers. Yet plate tectonics’ ability to explain fine details of the continental and oceanic lithosphere is the strongest available verification of this theory. Presenting more of this detail in a real-world setting can help motivate students to study the processes that mold Earth’s oceans and continents.

    Google Earth allows instructors and students to explore Earth’s oceans and continents in considerable detail. The images in this open access, online resource provide a striking portrait of the planet’s continents and oceans. A user can browse this virtual globe’s features and explore in fine detail mountain ranges, geological faults, ocean basins, and much more.

    Properly annotated, Google Earth can also provide insights into the geophysical processes that created the world as we see it today. It can serve as an informative tool for students and instructors in their study of tectonic plates, bringing to life the geological significance of features such as the famous Ring of Fire that girdles the Pacific.

    Our project, Google Earth for Onsite and Distance Education (GEODE), has now added a Grand Tour of the Ocean Basins to its website to provide such help. This tour gives instructors a way to become familiar with details of Earth’s tectonic story and to stay up to date about new insights into tectonic processes. They can then better respond to, and provide context for, on-the-spot questions from students as they become caught up in the images they view on Google Earth.

    The tour was designed for geoscience majors, but an instructor could edit it to suit general education or high school courses. Students can use the documentation as a self-study tool, even if they do not have extensive prior knowledge of tectonic processes.

    A Teaching Sequence

    The tour is organized in a teaching sequence, beginning with the East African Rift, continuing through the Red Sea and Gulf of Aden into the Arabian Sea. The tour proceeds to the passive margins of Antarctica, which lead tourists to the South Atlantic, North Atlantic, and Arctic oceans. En route, students visit thinned continental shelves and abandoned ocean basins (where seafloor spreading no longer occurs). The Lesser Antilles Arc and Scotia Arc serve as an introduction to Pacific continental arcs, transform boundaries, island arcs, and marginal basins. The tour ends with ophiolites—slivers of ocean thrust onto land—in Oman.

    The tour uses a series of Google Earth placemarks (map pin icons), with descriptions and illustrations in a separate Portable Document Format (PDF) file. We provide plate tectonic context by combining two superlative resources: ocean floor ages from the Age of the Lithosphere for Google Earth website (based on Müller et al. [2008]) and the plate boundary model from Laurel Goodell’s Science Education Resource Center page (based on Bird [2003]).

    Not Your Grandmother’s Plate Tectonics

    Our virtual tour of ocean basins includes lots of up-to-date local details, thanks largely to recent research that takes advantage of precise data provided by satellite-based GPS. Just as your car’s GPS receiver tells you how fast you are traveling and in what direction, highly sensitive GPS devices record plate velocities, even though plates move only at about the rate your fingernails grow. Researchers no longer regard plates as absolutely rigid: Internal plate deformation was first documented in the Indian Ocean [Wiens et al., 1985].

    GPS surveys and seismic records reveal large regions of deformation along diffuse boundaries between tectonic plates, where the movement is not along one well-defined plane. Instead, movement involves microplates: relatively rigid parts of plates that move with significantly differing velocities. For example, tour stop 9, the eastern Indian Ocean, shows the presence of widespread diffuse deformation in the Indian, Australian, and Capricorn plates (Figure 1). For mechanical reasons, these microplates tend to pivot about points separating regions of diffuse extension from compression, represented by white circle icons in the Google Earth tour.

    Beyond Atlantic Style and Pacific Style

    Our Google Earth tour also allows us to address misconceptions about the boundaries between tectonic plates and between oceans and continents. Some of the most persistent misconceptions concern the differences between active plate boundaries and passive continental margins.

    A bit of background first: Active plate boundaries can be divergent (mid-ocean ridges), convergent (subduction and collision zones), or transform (e.g., the San Andreas Fault). At passive continental margins, oceanic lithosphere and continental lithosphere are welded together along the fossilized line of initial continental rifting. A person in our Google Earth tour will encounter numerous examples of both active plate boundaries and passive continental margins.

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    Fig. 1. This image from the grand tour illustrates diffuse deformation on the Indian, Australian, and Capricorn plates. Areas of extension are shaded gray; areas of contraction are yellow. Thick dashed lines mark the median lines of the zones of diffuse deformation. They define a diffuse triple junction. Open circles are poles of relative rotation of pairs of plates (a third pole may already be subducted under the Sunda Plate). These poles occupy regions of little deformation between the extensional and contractional zones. Purple dotted lines outline continental shelves. Credit: Based on data from Royer and Gordon [1997]

    Misconceptions arise from the introductory level on, where teachers present students with two basic cross sections of ocean basins: Atlantic style with two passive continental margins and Pacific style with two active plate boundaries. Students commonly draw cross sections with two symmetrical active convergent plate boundaries even though there is no such ocean basin on Earth.

    Symmetrical passive margins do exist, however: They border large regions of oceanic crust, including, for example, the North and South Atlantic oceans, the western portion of the Indian Ocean within the Arabian Sea, and the Southern Ocean between Australia and Antarctica as well as between Africa and Antarctica. But active basins are always asymmetrical, with ridges often far from the middle of the ocean basin. Seafloor spreading is generally symmetrical about ocean ridges (except for local instances of ridge jump), but there is no reason for subduction to occur at the same rate on either side of an ocean basin; hence, ridges migrate as they spread, and in places, they reach a trench and are subducted.

    Our grand tour presents lithospheric cross sections of the Pacific crust to scale, with its eastern 4,000-kilometer-wide Nazca Plate and western 12,000-kilometer-wide Pacific Plate. It also highlights the eastern Indian Ocean, with its passive margin against Madagascar and active plate boundary against Burma-Sumatra, the scene of the devastating, tsunami-generating earthquake of 26 December 2004 (Figure 2).

    3
    Fig. 2. An ocean can be bounded by a passive continental margin on one side and an active plate boundary on the other. In such cases, the spreading ridge is never in the middle of the ocean. A traverse from Madagascar in the west to Sumatra in the east serves as a modern-day analogue for times during the evolution of the Iapetus Ocean that was consumed in the Appalachian-Caledonian Orogeny.

    This combination of passive continental margin and active plate boundary serves as a good modern analogue for the Iapetus Ocean, the ocean that separated paleo–North America from paleo-Europe and paleo-Africa before the collisions that created the Appalachians, Caledonides, and associated mountains. Models of those mountain-building events involve a collision of active and passive sides of the ocean basin at times as Iapetus was consumed.

    Sampling Diversity in Ocean Basins

    The grand tour also visits many of the diverse features of Earth’s ocean basins. A significant amount of oceanic crust resides in failed or abandoned basins bounded by passive margins. Such regions include the Gulf of Mexico, the Labrador Sea and Baffin Bay between Canada and Greenland, the Bay of Biscay between France and Spain, the western Mediterranean, and the Tasman and Coral seas east of Australia, all of which are visited on the tour.

    Many offshore regions are underlain by oceanic crust that developed in marginal basins behind island arcs such as Japan and the Mariana Islands, and the tour visits these regions as well. Because the west side of the Pacific’s oceanic crust is so much older than the east, it is colder and denser and subducts steeply and rapidly. Consequently, trenches marking the initiation of subduction roll back eastward, like a Michael Jackson moonwalk. The resultant “trench suction” forces open multiple back-arc basins to the west of the main Pacific basin, with their own miniature spreading ridges.

    A third type of minor ocean basin is created by side-stepping transform fault arrays as in the Gulf of California and on the northern border of the Caribbean Plate. In such locations, transform faults are long, and spreading ridge segments are short.

    Finally, there are numerous oceanic plateaus with relatively thick crust derived from large igneous provinces or small submerged continental fragments. Examples of all of the above are included in our tour.

    Triple Junctions and Hot Spots

    The tour makes stops at triple junctions, where three major plates meet. At some locations, triangular microplates without any bounding continental margins grow, as exemplified by the Galápagos Microplate (Figure 3, tour stop 38). Researchers have found strong evidence that one such paleomicroplate grew to become the Pacific Plate (Figure 4) [Boschman and van Hinsbergen, 2016]. The Pacific oceanic crust never had passive continental margins. It was born at sea!

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    Fig. 3. Stop 38 on the Grand Tour of the Ocean Basins focuses on the Galápagos Microplate (designated µ in the image) on the East Pacific Rise. It sits at a triple junction where three large plates meet. The Galápagos hot spot to the east was probably instrumental in the location of the triple junction. Credit: Based on data from Schouten et al. [2008]

    Oceans are also home to mantle hot spot trails unrelated to plate boundaries. The grand tour visits the well-known Hawaiian Islands–Emperor Seamount trail. Numerous other trails are easily recognizable in Google Earth.

    File Formats and System Requirements

    The tour is presented in two file formats: Keyhole Markup Language (KML)—the format of Google Earth custom content—and an associated PDF file. Google Earth puts descriptive text and imagery into placemark balloons, which can obscure the surface of the map. Because these balloons cannot be dragged to one side, simultaneous viewing of KML maps and PDF descriptive documents is the solution. Dual monitors, twin projectors, or pairs of laptops make for the best viewing for personal study, lecture presentation, and student collaboration.

    The PDF document is laid out in frames suited to reading on digital devices. Each frame contains a block of text and associated imagery. Instructors may omit or rearrange tour stops to suit the needs of their courses. Because KML is human-readable, such rearrangements can be done in a text editor. Note that the KML file must be viewed on a desktop or laptop computer (Mac, Windows, or Linux) because Google Earth for mobile devices is highly limited.

    5
    Fig. 4. Stop 39 on the Grand Tour of the Ocean Basins looks at the formation of the Pacific Plate. The oldest isochrons are not seen at the western subduction zone with the Eurasian Plate and associated marginal basins; rather, the oldest oceanic crust forms a Russian doll–style set of nested triangles, suggesting that the Pacific Plate started as a triangular microplate growing from a triple junction, just like the Galápagos Plate today.

    Trying It Out for Yourself

    The KML and PDF files are available for download. The KML download contains a simple network link to an online KML document so that updates occur automatically whenever the document is opened in Google Earth.

    The author invites suggestions for continuously improving this resource.

    Acknowledgments

    Development was supported by the National Science Foundation under grant NSF DUE 1323419, “Google Earth for Onsite and Distance Education (GEODE).” Any opinions, findings, and conclusions or recommendations are those of the author and do not necessarily reflect the views of the National Science Foundation. Thanks are owed to the Eos editors and to two anonymous reviewers for very helpful suggestions that improved the submitted manuscript.

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  • richardmitnick 4:17 pm on June 9, 2017 Permalink | Reply
    Tags: Active volcanic lake research and monitoring, AGU, An Autonomous Boat to Investigate Acidic Crater Lakes, , Bathymetric data, Determining the depth profile of these lakes, , Poás volcano in Costa Rica,   

    From Eos: “An Autonomous Boat to Investigate Acidic Crater Lakes” 

    AGU bloc

    AGU
    Eos news bloc

    Eos

    5 June 2017
    Donald A. McFarlane
    dmcfarlane@kecksci.claremont.edu
    Joyce Lundberg
    Guy van Rentergem
    Carlos J. Ramírez

    A novel aquatic drone ventured into highly acidic waters to test the feasibility of remotely exploring and surveying hazardous volcanic lakes.

    1
    An autonomous, sonar-equipped boat is carried to the edge of the highly acidic water of Laguna Caliente, located in the crater of Poás volcano in Costa Rica, to test the craft’s ability to collect bathymetric data. Credit: Donald McFarlane

    In 1986, Lake Nyos in Cameroon exploded, jetting water more than 100 meters into the air as roughly 1.2 cubic kilometers of carbon dioxide suddenly belched from the waters. This enormous wave of gas smothered the surrounding countryside, killing more than 1700 people.

    The deadly eruption focused attention on the dangers posed by active volcanic crater lakes and the importance of monitoring such lakes for changes in volume and other factors. About 35 such lakes dot the Earth, but monitoring active volcanic lakes can be problematic, especially when they undergo frequent eruptions of steam and other gases. These eruptions can make them too dangerous for human inspection by inflatable boat or raft.

    However, the recent burgeoning interest in autonomous aerial drones presents researchers with an opportunity. So we asked ourselves, Could a small, inexpensive, and easily transportable autonomous boat, equipped with sonar, do the job?

    2
    The drone boat speeds off to survey the acid crater lake Laguna Caliente in Costa Rica’s Poás volcano. The lake has a pH of 0.53 and a temperature of 55°C. Credit: Donald McFarlane

    A Need to Determine Lake Volume

    A key aspect of active volcanic lake research and monitoring is the determination of lake volume, something that can change significantly as geothermal heating and evaporation, steam and gas eruptions, and sedimentation progress. As a result, considerable attention has been paid to determining the depth profile of these lakes.

    We designed and built an autonomous boat with that function in mind, using readily available and relatively inexpensive aerial drone components, open-source software, and a retail sonar unit. In total, the equipment used to create the boat cost roughly US$700

    Drone Boat Specs

    Our drone boat, which we dubbed a sonar-ASV (autonomous surface vehicle), has a hull with a catamaran design. Made from acrylonitrile butadiene styrene (ABS) plastic, it measures just 54 × 38 × 22 centimeters and weighs less than 10 kilograms. The ASV is easily carried in airline baggage or in a backpack across challenging terrain.

    Because the ASV has to work in highly acidic environments, we couldn’t use a conventional propulsion system with a drive shaft connected to a propeller in the water. The metal shaft would not survive the acidity. Instead, our ASV has an air propeller powered by a battery-driven electric motor.

    The boat is surprisingly agile and has a cruise speed of 0.8 meter per second (3 kilometers per hour), balancing high sonar resolution against minimal mission time.

    At cruise speed, the motor draws only 1.6–3.5 amps, depending on wind direction. Keeping the electric current low is important because high currents could lead to excess heating, which could be problematic because the ASV already has to operate in water temperatures of 55°C and higher.

    Craft Autonomy

    From the beginning of the design process, it was clear that the craft needed to be fully autonomous. For one thing, clouds of fog often obscure the hot lake surface, so the ASV needs to find its way on its own. Autonomous navigation also allows us to repeat the same track on subsequent missions if we want to focus on particular features of the lake bottom.

    We ended up choosing the open-source ArduPilot system for autopilot navigation. For planning the ASV forays, we used Windows-based Mission Planner software. An Arduino data logger board captured and stored data from the GPS, sonar, and additional sensors (such as temperature).

    Costa Rica Test Site

    Poás volcano in Costa Rica provided us with an ideal test site. At 2708 meters in elevation, Poás is topped by two crater lakes. The older lake, Laguna Botos, is an inactive, cold-water lake with near-normal chemistry. The other, Laguna Caliente, is a hyperacidic, hot, and very active lake with chemistry that is anything but normal.

    4
    Scientists hike down to Laguna Caliente. Credit: Joyce Lundberg

    We deployed the ASV into Laguna Caliente on 30 and 31 July 2016. During these runs, we recorded a pH of 0.53, roughly 3 times the acidity of battery acid, and a water temperature of 55°C. Moreover, during the course of our fieldwork, the lake experienced small- to medium-sized steam eruptions every 35–45 minutes.

    Visibility was also poor. The ASV was rarely visible through clouds of condensation during its Laguna Caliente mission. In total, the device made two runs into the lake, each for about 45 minutes. Fieldwork at the lake took place over 3 days, stretching to 1 August, with the third day reserved for recovery of the vehicle.

    5
    On 31 July 2016, a nearby steam eruption caused waves that inundated the drone boat as it crossed Laguna Caliente. The ASV was recovered but was coated with elemental sulfur and electrically crippled, as seen here. Nonetheless, it still delivered its data. Credit: Joyce Lundberg

    Surviving an Acid Bath

    Our boat proved remarkably rugged. On 31 July, a nearby steam eruption inundated the roving ASV with hot, highly acidic water. Although the acid penetrated a poorly sealed cable connection and shorted out the telemetry system, the ASV survived, was recovered, and delivered its data.

    The eruption took place at the end of the vehicle’s second mission. The vehicle was not operable after its acid bath but has since been rebuilt.

    A Valuable Tool

    In the end, the ASV missions provided the bathymetric data we needed to map the bottom of Laguna Caliente and thus calculate its volume.

    On 13 April, Laguna Caliente erupted, spewing water, steam, and sediment as high as 1 kilometer into the air. As the eruption developed, new ash and incandescent pyroclastics were ejected until at least 26 April, when the activity destroyed a camera operating at the site. These eruption events have completely restructured the lake, and our team hopes to return in the near future for another series of lake surveys.

    Such rapid mobilization is possible with inexpensive and easily portable equipment like our ASV. Through custom-built ASVs equipped with sonar and other sensors, scientists can gain a valuable new tool for the exploration and monitoring of remote and hazardous volcanic lakes.

    6
    Bathymetric map of Laguna Caliente on Poás volcano, August 2016. Credit: Guy van Rentergem

    Acknowledgment

    Partial funding was provided by the Keck Science Department of the Claremont Colleges.

    Author Information

    Donald A. McFarlane (email: dmcfarlane@kecksci.claremont.edu), W. M. Keck Science Department, The Claremont Colleges, Claremont, Calif.; Joyce Lundberg, Department of Geography and Environmental Studies, Carleton University, Ottawa, Ont., Canada; Guy van Rentergem, Koningin Astridstraat, Deinze, Belgium; and Carlos J. Ramírez, Centro de Investigaciones Geofísicas, Universidad de Costa Rica, San Jose

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  • richardmitnick 12:23 pm on May 23, 2017 Permalink | Reply
    Tags: AGU, Cosmic Muons Reveal the Land Hidden Under Ice, , ,   

    From Eos: “Cosmic Muons Reveal the Land Hidden Under Ice” 

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    5.23.17
    Jenny Lunn
    jlunn@agu.org

    Scientists accurately map the shape of the bedrock beneath a glacier using a new technique.

    1
    Aletsch glacier seen from Jungfraujoch. A tunnel runs through the bedrock below this glacier; researchers placed sensors within this tunnel to help map the shape of the bedrock under the ice. Credit: Alessandro Lechmann

    The land surface under a glacier is sculpted and shaped by the ice passing over it. Data about the shape of the bedrock yield information crucial to understanding erosional processes underneath a glacier. However, the inaccessibility of sites where glacial erosion currently occurs presents big challenges for advancing this understanding.

    A range of techniques has been used to map the bedrock beneath glaciers, including drilling, seismic surveys, multibeam bathymetry, gravity measurements, and radio-echo soundings. The accuracy of results has been limited, so Nishiyama et al [Geophysical Research Letters]. tested a different technique: emulsion film muon radiography.

    2
    A muon detector in the Jungfrau railway tunnel awaiting arrival of the cosmic ray muons. Credit: Nishiyama et al.

    Muons are formed when cosmic rays collide with atoms in Earth’s upper atmosphere. They descend toward Earth, with about 10,000 muons reaching each square meter of Earth’s surface every minute. One of their significant properties is that they can pass through matter, even dense and solid objects on Earth.

    Particle detectors can be used to measure the quantity of muons and their trajectories, which can reveal information about the materials that they have passed through.

    Because cosmic muons travel only downward, detectors need to be located below the objects to be surveyed. This technique has been used by geophysicists to scan the interior architecture of volcanoes, seismic faults, and caves and to detect carbon leaks, but it has posed a challenge for surveying the bedrock beneath glaciers.

    The team of researchers found a solution in the central Swiss Alps: the Jungfrau railway tunnel, which runs through the bedrock beneath the Aletsch glacier. They set up three particle detectors in the tunnel that are oriented upward with a view of the bedrock beneath the base of the largest glacier of Europe.

    3
    Three-dimensional reconstructed bedrock shape (blue) under the uppermost part of the Aletsch glacier. The shape of the interface was determined from the cosmic ray muon measurement performed at three muon detectors (D1, D2, and D3) along the railway tunnel (gray line). Bedrock that pokes through ice is in gray tones. Jungfraufirn is a small glacier that feeds the Aletsch glacier. Blue dots on the gray line represent points where scientists sampled rocks within the tunnel. The image is Figure 5b in Nishiyama et al.; dashed lines outline a cross section of this 3-D map that can be found in Figure 5c. Credit: Nishiyama et al.; base map from SWISSIMAGE, reproduced by permission of swisstopo (BA17061)

    Different types of particle detectors are available for muon radiography, but the team selected emulsion films, a special type of photographic film that can be used in remote and harsh environments because it does not require any electric power or computers for operation.

    Because of the density contrast between ice and rock, the patterns of muons captured on the film over a 47-day period could be used to accurately map the shape of the bedrock below the glacier.

    Using this technique, the researchers were able to map the bedrock-ice interface beneath the glacier over a 4000-square-meter area. They were also able to infer the glacier’s response to global warming. In particular, the team predicts a larger frequency of rock avalanches as the ice shrinks, exacerbated by reconstructed bedrock geometry beneath the glacier. This increase is of particular concern because buildings are situated on top of the bedrock. These include tourist facilities, a research station, and communications infrastructure, as well as the railway tunnel itself, which cuts through the bedrock.

    The use of cosmic muon radiography is spreading in various fields, including geophysics and civil engineering. This first application of the technique in glacial geology complements data collected by other methods and has the potential to be applied in other glacial locations underlain by a tunnel. (Geophysical Research Letters, https://doi.org/10.1002/2017GL073599, 2017)

    See the full article here .

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  • richardmitnick 1:59 pm on March 17, 2017 Permalink | Reply
    Tags: AGU, , , , Mapping the Topographic Fingerprints of Humanity Across Earth   

    From Eos: “Mapping the Topographic Fingerprints of Humanity Across Earth” 

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    AGU
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    Eos

    16 March 2017
    Paolo Tarolli
    Giulia Sofia
    Erle Ellis

    1
    Fig. 1. Three-dimensional view of Bingham Canyon Mine, Utah, a human-made topographic signature, based on a free, open-access high-resolution data set. Credit: Data from Utah AGRC

    Since geologic time began, Earth’s surface has been evolving through natural processes of tectonic uplift, volcanism, erosion, and the movement of sediment. Now a new force of global change is altering Earth’s surface and morphology in unprecedented ways: humanity.

    Human activities are leaving their fingerprints across Earth (Figure 1), driven by increasing populations, technological capacities, and societal demands [e.g., Ellis, 2015; Brown et al., 2017; Waters et al., 2016]. We have altered flood patterns, created barriers to runoff and erosion, funneled sedimentation into specific areas, flattened mountains, piled hills, dredged land from the sea, and even triggered seismic activity [Tarolli and Sofia, 2016]. These and other changes can pose broad threats to the sustainability of human societies and environments.

    If increasingly globalized societies are to make better land management decisions, the geosciences must globally evaluate how humans are reshaping Earth’s surface. A comprehensive mapping of human topographic signatures on a planet-wide scale is required if we are to understand, model, and forecast the geological hazards of the future.

    Understanding and addressing the causes and consequences of anthropogenic landform modifications are a worldwide challenge. But this challenge also poses an opportunity to better manage environmental resources and protect environmental values [DeFries et al., 2012].

    The Challenge of Three Dimensions

    “If life happens in three dimensions, why doesn’t science?” This question, posed more than a decade ago in Nature [Butler, 2006], resonates when assessing human reshaping of Earth’s landscapes.

    Landforms are shaped in three dimensions by natural processes and societal demands [e.g., Sidle and Ziegler, 2012; Guthrie, 2015]; societies in turn are shaped by the landscapes they alter. Understanding and modeling these interacting forces across Earth are no small challenge.

    For example, observing and modeling the direct effects of some of the most widespread forms of human topographic modification, such as soil tillage and terracing [Tarolli et al., 2014], are possible only with very fine spatial resolutions (i.e., ≤1 meter). Yet these features are common all over the world. High-resolution three-dimensional topographic data at global scales are needed to observe and appraise them.

    The Need for a Unified, Global Topographic Data Set

    High-resolution terrain data such as lidar [Tarolli, 2014], aerial photogrammetry [Eltner et al., 2016], and satellite observations [Famiglietti et al., 2015] are increasingly available to the scientific community. These data sets are also becoming available to land planners and the public, as governments, academic institutions, and others in the remote sensing community seize the opportunity for high-resolution topographic data sharing (Figure 2) [Wulder and Coops, 2014; Verburg et al., 2015]

    2
    Fig. 2. High-resolution geodata reveal the topographic fingerprints of humanity: (a) terraces in the Philippines, (b) agricultural practices in Germany, and (c) roads in Antarctica. The bottom images are lidar images of the same landscapes. Credit: Data from University of the Philippines TCAGP/Freie und Hansestadt Hamburg/Noh and Howat [2015]. Top row: © Google, DigitalGlobe

    Thanks to these geodata, anthropogenic signatures are widely observable across the globe, under vegetation cover (Figure 2a), at very fine spatial scales (e.g., agricultural practices and plowing; Figure 2b) and at large spatial scales (e.g., major open pit mines; Figure 3), and far from contemporary human settlements (Figure 2c). So the potential to assess the global topographic fingerprints of humanity using high-resolution terrain data is a tantalizing prospect.

    However, despite a growing number of local projects at fine scales, a global data set remains nonetheless elusive. This lack of global data is largely the result of technical challenges to sharing very large data sets and issues of data ownership and permissions.

    But once a global database exists, advances in the technical capacity to handle and analyze large data sets could be utilized to map anthropogenic signatures in detail (e.g., using a close-range terrestrial laser scanner) and across larger areas (e.g., using satellite data). Together with geomorphic analyses, the potential is clear for an innovative, transformative, and global-scale assessment of the extent to which humans shape Earth’s landscapes.

    For example, a fine-scale analysis of terrain data can detect specific anthropogenic configurations in the organization of surface features (Figure 3b) [Sofia et al., 2014], revealing modifications that humans make across landscapes (Figure 3c). Such fine-scale geomorphic changes are generally invisible to coarser scales of observation and analysis, making it appear that natural landforms and natural hydrological and sedimentary processes are unaltered. Failure to observe such changes misrepresents the true extent and form of human modifications of terrain, with huge consequences when inaccurate data are used to assess risks from runoff, landslides, and other geologic hazards to society [Tarolli, 2014].

    3
    Fig. 3. This potential detection of anthropogenic topographic signatures has been derived from satellite data. (a) This satellite image shows an open-pit mine in North Korea. (b) That image has been processed in an autocorrelation analysis, a measure of the organization of the topography (slope local length of autocorrelation, SLLAC [Sofia et al., 2014]). The variation in the natural landscape is noisy (e.g., top right corner), whereas anthropogenic structures are more organized and leave a clear topographic signature. (c) The degree of landscape organization can be empirically related to the amount of human-made alterations to the terrain, as demonstrated by Sofia et al. [2014]. Credit: Data from CNES© Distribution Airbus DS

    Topography for Society

    A global map of the topographic signatures of humanity would create an unparalleled opportunity to change both scientific and public perspectives on the human role in reshaping Earth’s land surface. A worldwide inventory of anthropogenic geomorphologies would enable geoscientists to assess the extent to which human societies have reshaped geomorphic processes globally and provide a tool for monitoring these changes over time.

    Such monitoring would facilitate unprecedented insights into the dynamics and sensitivity of landscapes and their responses to human forcings at global scale. In turn, these insights would help cities, resource managers, and the public better understand and mediate their social and environmental actions.

    As we move deeper into the Anthropocene, a comprehensive mapping of human topographic signatures will be increasingly necessary to understand, model, and forecast the geological hazards of the future. These hazards will likely be manifold.

    4
    Fig. 4. (a) This road, in the HJ Andrews Experimental Forest in Oregon’s Cascade Range, was constructed in 1952. A landslide occurred in 1964, and its scar was still visible in 1994, when the image was acquired. The landslide starts from the road and flows toward the top right corner of the image. (b) An index called the relative path impact index (RPII) [Tarolli et al., 2013] is evaluated here using a lidar data set from 2008. The RPII analyzes the potential water surface flow accumulation based on the lidar digital terrain model, and the index is highest where the flows are increased because of the presence of anthropogenic features. High values beyond one standard deviation (σ) highlight potential road-induced erosion. Credit: Data from NSF LTER, USFS Research, OSU; background image © Google, USGS.

    For example, landscapes across the world face altered flooding regimes in densely populated floodplains, erosion rates associated with road networks, altered runoff and erosion due to agricultural practices, and sediment release and seismic activity from mining [Tarolli and Sofia, 2016]. Modifications in land use (e.g., urbanization and changes in agricultural practices) alter water infiltration and runoff production, increasing flooding risks in floodplains. Increases in road density cause land degradation and erosion (Figure 4), especially when roads are poorly planned and constructed without well-designed drainage systems, leading to destabilized hillslopes and landslides. Erosion from agricultural fields can exceed rates of soil production, causing soil degradation and reducing crop yields, water quality, and food production. Mining areas, even years after reclamation, can induce seismicity, landslides, soil erosion, and terrain collapse, damaging environments and surface structures.

    Without accurate data on anthropogenic topography, communities will find it difficult to develop and implement strategies and practices aimed at reducing or mitigating the social and environmental impacts of anthropogenic geomorphic change.

    Earth Science Community’s Perspective Needed

    Technological advances in Earth observation have made possible what might have been inconceivable just a few years ago. A global map and inventory of human topographic signatures in three dimensions at high spatial resolution can now become a reality.

    Collecting and broadening access to high spatial resolution (meter to submeter scale), Earth science–oriented topography data acquired with lidar and other technologies would promote scientific discovery while fostering international interactions and knowledge exchange across the Earth science community. At the same time, enlarging the search for humanity’s topographical fingerprints to the full spectrum of environmental and cultural settings across Earth’s surface will require a more generalized methodology for discovering and assessing these signatures.

    These two parallel needs are where scientific efforts should focus. It is time for the Earth science community to come together and bring the topographic fingerprints of humanity to the eyes and minds of the current and future stewards, shapers, curators, and managers of Earth’s land surface.
    Acknowledgments

    Data sets for Figure 1 are from Utah Automated Geographic Reference Center (AGRC), Geospatial Information Office. Data sets for Figures 2(a)–2(c) are from the University of the Philippines Training Center for Applied Geodesy and Photogrammetry (TCAGP), Noh and Howat [2015], and Freie und Hansestadt Hamburg (from 2014), respectively. Data sets for Figure 3 are from Centre National d’Études Spatiales (CNES©), France, Distribution Airbus DS. Data sets for Figure 4 are from the HJ Andrews Experimental Forest research program, National Science Foundation’s Long-Term Ecological Research Program (NSF LTER, DEB 08-23380), U.S. Forest Service (USFS) Pacific Northwest Research Station, and Oregon State University (OSU).
    References

    Butler, D. (2006), Virtual globes: The web-wide world, Nature, 439, 776–778, https://doi.org/10.1038/439776a.

    Brown, A. G., et al. (2017), The geomorphology of the Anthropocene: Emergence, status and implications, Earth Surf. Processes Landforms, 42, 71–90, https://doi.org/10.1002/esp.3943.

    DeFries, R. S., et al. (2012), Planetary opportunities: A social contract for global change science to contribute to a sustainable future, BioScience, 62, 603–606, https://doi.org/10.1525/bio.2012.62.6.11.

    Ellis, E. C. (2015), Ecology in an anthropogenic biosphere, Ecol. Monogr., 85, 287–331, https://doi.org/10.1890/14-2274.1.

    Eltner, A., et al. (2016), Image-based surface reconstruction in geomorphometry—Merits, limits and developments, Earth Surf. Dyn., 4, 359–389, https://doi.org/10.5194/esurf-4-359-2016.

    Famiglietti, J. S., et al. (2015), Satellites provide the big picture, Science, 349, 684–685, https://doi.org/10.1126/science.aac9238.

    Guthrie, R. (2015), The catastrophic nature of humans, Nat. Geosci. 8, 421–422, https://doi.org/10.1038/ngeo2455.

    Noh, M. J., and I. M. Howat (2015), Automated stereo-photogrammetric DEM generation at high latitudes: Surface Extraction with TIN-based Search-space Minimization (SETSM) validation and demonstration over glaciated regions, GIScience Remote Sens., 52(2), 198–217, https://doi.org/10.1080/15481603.2015.1008621.

    Sidle, R. C., and A. D. Ziegler (2012), The dilemma of mountain roads, Nat. Geosci, 5, 437–438, https://doi.org/10.1038/ngeo1512.

    Sofia, G., F. Marinello, and P. Tarolli (2014), A new landscape metric for the identification of terraced sites: The slope local length of auto-correlation (SLLAC), ISPRS J. Photogramm. Remote Sens., 96, 123–133, https://doi.org/10.1016/j.isprsjprs.2014.06.018.

    Tarolli, P. (2014), High-resolution topography for understanding Earth surface processes: Opportunities and challenges, Geomorphology, 216, 295–312, https://doi.org/10.1016/j.geomorph.2014.03.008.

    Tarolli, P., and G. Sofia (2016), Human topographic signatures and derived geomorphic processes across landscapes, Geomorphology, 255, 140–161, https://doi.org/10.1016/j.geomorph.2015.12.007.

    Tarolli, P., et al. (2013), Recognition of surface flow processes influenced by roads and trails in mountain areas using high-resolution topography, Eur. J. Remote Sens., 46, 176–197.

    Tarolli, P., F. Preti, and N. Romano (2014), Terraced landscapes: From an old best practice to a potential hazard for soil degradation due to land abandonment, Anthropocene, 6, 10–25, https://doi.org/10.1016/j.ancene.2014.03.002.

    Verburg, P. H., et al. (2015), Land system science and sustainable development of the Earth system: A global land project perspective, Anthropocene, 12, 29–41, https://doi.org/10.1016/j.ancene.2015.09.004.

    Waters, C. N., et al. (2016), The Anthropocene is functionally and stratigraphically distinct from the Holocene, Science, 351, aad2622, https://doi.org/10.1126/science.aad2622.

    Wulder, M. A., and N. C. Coops (2014), Satellites: Make Earth observations open access, Nature, 513, 30–31, https://doi.org/10.1038/513030a.

    —Paolo Tarolli (email: paolo.tarolli@unipd.it; @TarolliP) and Giulia Sofia (@jubermensch2), Department of Land, Environment, Agriculture, and Forestry, University of Padova, Legnaro, Italy; and Erle Ellis (@erleellis), Department of Geography and Environmental Systems, University of Maryland, Baltimore County, Baltimore
    Citation: Tarolli, P., G. Sofia, and E. Ellis (2017), Mapping the topographic fingerprints of humanity across Earth, Eos, 98, https://doi.org/10.1029/2017EO069637. Published on 16 March 2017.
    © 2017. The authors. CC BY-NC-ND 3.0

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  • richardmitnick 10:46 am on March 8, 2017 Permalink | Reply
    Tags: AGU, , California Fault System Could Produce Magnitude 7.3 Quake, , , Newport-Inglewood/Rose Canyon fault mostly offshore but never more than four miles from the San Diego Orange County and Los Angeles County coast,   

    From Eos: “California Fault System Could Produce Magnitude 7.3 Quake” 

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    Mar 7, 2017

    A new study finds rupture of the offshore Newport-Inglewood/Rose Canyon fault that runs from San Diego to Los Angeles is possible.

    1
    A Scripps research vessel tows a hydrophone array used to collect high-resolution bathymetric to better understand offshore California faults. Credit: Scripps Institution of Oceanography, UC San Diego

    A fault system that runs from San Diego to Los Angeles is capable of producing up to magnitude 7.3 earthquakes if the offshore segments rupture and a 7.4 if the southern onshore segment also ruptures, according to a new study led by Scripps Institution of Oceanography at the University of California San Diego.

    The Newport-Inglewood and Rose Canyon faults had been considered separate systems but the study shows that they are actually one continuous fault system running from San Diego Bay to Seal Beach in Orange County, then on land through the Los Angeles basin.

    “This system is mostly offshore but never more than four miles from the San Diego, Orange County, and Los Angeles County coast,” said study lead author Valerie Sahakian, who performed the work during her doctorate at Scripps and is now a postdoctoral fellow with the U.S. Geological Survey in Menlo Park, California. “Even if you have a high 5- or low 6-magnitude earthquake, it can still have a major impact on those regions which are some of the most densely populated in California.”

    The new study was accepted for publication in the Journal of Geophysical Research: Solid Earth, a journal of the American Geophysical Union.

    In the new study, researchers processed data from previous seismic surveys and supplemented it with high-resolution bathymetric data gathered offshore by Scripps researchers between 2006 and 2009 and seismic surveys conducted aboard former Scripps research vessels New Horizon and Melville in 2013. The disparate data have different resolution scales and depth of penetration providing a “nested survey” of the region. This nested approach allowed the scientists to define the fault architecture at an unprecedented scale and thus to create magnitude estimates with more certainty.

    2
    Locations of NIRC fault zone as observed in seismic profiles. Credit: AGU/Journal of Geophysical Research: Solid Earth

    They identified four segments of the strike-slip fault that are broken up by what geoscientists call stepovers, points where the fault is horizontally offset. Scientists generally consider stepovers wider than three kilometers more likely to inhibit ruptures along entire faults and instead contain them to individual segments—creating smaller earthquakes. Because the stepovers in the Newport-Inglewood/Rose Canyon (NIRC) fault are two kilometers wide or less, the Scripps-led team considers a rupture of all the offshore segments is possible, said Neal Driscoll, a geophysicist at Scripps and co-author of the new study.

    The team used two estimation methods to derive the maximum potential a rupture of the entire fault, including one onshore and offshore portions. Both methods yielded estimates between magnitude 6.7 and magnitude 7.3 to 7.4.

    The fault system most famously hosted a 6.4-magnitude quake in Long Beach, California that killed 115 people in 1933. Researchers have found evidence of earlier earthquakes of indeterminate size on onshore portions of the fault, finding that at the northern end of the fault system, there have been between three and five ruptures in the last 11,000 years. At the southern end, there is evidence of a quake that took place roughly 400 years ago and little significant activity for 5,000 years before that.

    Driscoll has recently collected long sediment cores along the offshore portion of the fault to date previous ruptures along the offshore segments, but the work was not part of this study.

    “Further study is warranted to improve the current understanding of hazard and potential ground shaking posed to urban coastal areas from Tijuana to Los Angeles from the NIRC fault,” the study concludes.

    See the full article here .

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