From temblor: “A M=7 aftershock is 300 times more likely in the week after a M=7 mainshock in new study”



July 11, 2017
David Jacobson
Ross Stein

In a new study which will be published tomorrow, the effects of aftershocks in the week following a M=7.0 earthquake are shown to elevate the potential for a second M=7.0 by up to 300 times. This photo shows the San Andreas Fault, which takes center stage in this expansion on the already existing California earthquake hazard model.

Tomorrow, a new study on the California earthquake hazard model will be published, which shows, through computer simulations, that in the week following a M=7.0 earthquake, the likelihood of another M=7.0 quake is up to 300 times greater than the week beforehand. This dramatic jump in likelihood is due to the inclusion of the short-term probabilities associated with aftershock sequences, a factor never before used in statewide comprehensive model like this one.

The California earthquake hazard model, the Uniform California Earthquake Rupture Forecast Version 3 (UCERF3), was first published in 2015, and quantifies the hazard posed by tectonic forces unleashed on faults. The 2015 model was the first to include the possibility that an earthquake on one fault could jump to another fault in a matter of seconds, thereby creating multi-fault ruptures. In doing so, over 250,000 rupture scenarios were created for the state of California, vastly more than in the previous model. The reality of such falling-domino or ‘cascading’ ruptures was demonstrated spectacularly in the 2002 M=7.9 Denali, Alaska, and the 2016 M=7.8 Kaikoura, New Zealand earthquakes.

It the study released tomorrow, dubiously dubbed UCERF3-ETAS (ETAS refers to ‘epidemic-type aftershock sequence’ a concept borrowed from medical research and successfully applied to earthquakes by Yoshihito Ogata of Japan) takes it a step further by attempting to capture the role of aftershocks of all sizes following a mainshock. In a nutshell, for short times after a mainshocks, aftershocks matter.

These figures shows the simulated potential M=2.5+ aftershocks in the week after both a M=7.0 Southern San Andreas earthquake, and a M=7.1 quake on the Hayward Fault running through the East Bay. This figure highlights the potential domino-effect seen by a large earthquake. (Figure from: Field et al., 2017)

Mainshocks, by changing the stress on surrounding faults, trigger aftershocks; if you like, the mainshocks are the ‘parents.’ But these aftershock ‘daughters’ can in turn trigger ‘grand-daughters’ aftershocks of their own, ad infinitum. While most aftershocks of any generation will be smaller than their mainshock, occasionally they will be the same size as their mainshock, and rarely they will be larger. This is contrary to the popular belief that for a M=7 quake, the largest aftershock will be less than M=6. By including this triggering potential and running hundreds of thousands of simulations, what you see as Dr. Field described it, are faults as “conduits of hazard” which appear like blood pumping through veins. It is important to point out that what we are seeing in these figures are not dynamically-triggered remote earthquakes, but rather the potential for one large earthquake to trigger another, and then perhaps another.

The study’s team had to choose an arbitrary time period for consideration. Because aftershocks rapidly decay in time, shorter the period, the greater the gain in triggering likelihood. They chose a week as being societally relevant. Nonetheless, the effects are also given for longer time periods such as a month and a year following a mainshock to illustrate the decayed effects over time. The figure above shows the simulated potential M=2.5+ aftershocks in the week after both a M=7.0 earthquake on the southern San Andreas, and a M=7.1 quake on the Hayward Fault running through the East Bay.

We consider their result an important advance, but there is still room for improvement. The figure below shows the simulated potential M=2.5+ aftershocks in the week after a M=6.1 earthquake along the Parkfield section of the San Andreas, shown in two ways. Such M~6.1 earthquakes have struck this portion of the San Andreas every 20-40 years, the most recent in 2004. The image on the right does not take faults into account, resulting in an idealized halo of simulated aftershocks. But it looks nothing like the actual Parkfield aftershock zones, or any other, which are never halos and often illuminate triggering lobes. Coulomb stress transfer gives this spatial pattern, and and so controls which faults are more likely, and which are less likely, to rupture next. That, in our judgement, is what is missing from this approach. While Dr. Field said in an interview that fault characteristics, such as magnitude-frequency distributions, are incorporated, it is still largely lacks Coulomb stress transfer, and that could come next.

This figure shows how by incorporating the effect an earthquake has on nearby faults, you go from the idealized halo not representative of aftershock sequences (left), to a model which has the appearance of blood being pumped through veins. While this model is a step in the right direction, it still does not incorporate which faults are brought closer to failure, which is required for fault rupture. (Figure from: Field et al., 2017)

The Field et al. study takes a great step forward in quantifying the short-term consequences of a large earthquake. In the week following a large magnitude earthquake the potential for a second large quake can increase 300 times; in the year after the mainshock, the gains are typically a factor of 10 according to Dr. Field.

The message is this: Unfortunately, after a large quake, it may not be over. Instead, there is a chance that the sequence has just begun, and could spawn daughters greater than their parents—as all children aim to be.

References [Sorry, no links provided]

Edward H. Field, Thomas H. Jordan, Morgan T. Page, Kevin R. Milner, Bruce E. Shaw, Timothy E. Dawson, Glenn P. Biasi, Tom Parsons, Jeanne L. Hardebeck, Andrew J. Michael, Ray J. Weldon II, Peter M. Powers, Kaj M. Johnson, Yuehua Zeng, Karen R. Felzer, Nicholas van der Elst, Christopher Madden, Ramon Arrowsmith, Maximilian J. Werner, and Wayne R. Thatcher, A Synoptic View of the Third Uniform California Earthquake Rupture Forecast (UCERF3), Seismological Research Letters Volume 88, Number 5, doi: 10.1785/0220170045

Field, E. H., K. R. Milner, J. L. Hardebeck, M. T. Page, N. van der Elst, T. H. Jordan, A. J. Michael, B. E. Shaw, and M. J. Werner (2017). A spatiotemporal clustering model for the Third Uniform California Earthquake Rupture Forecast (UCERF3-ETAS): Toward an operational earthquake forecast, Bull. Seismol. Soc. Am. 107, doi: 10.1785/0120160173.

Personal correspondence with Dr. Edward (Ned) Field (USGS – Golden, Colorado)

See the full article here .

Please help promote STEM in your local schools.


Stem Education Coalition

You can help many citizen scientists in detecting earthquakes and getting the data to emergency services people in affected area.
QCN bloc

Quake-Catcher Network

The Quake-Catcher Network is a collaborative initiative for developing the world’s largest, low-cost strong-motion seismic network by utilizing sensors in and attached to internet-connected computers. With your help, the Quake-Catcher Network can provide better understanding of earthquakes, give early warning to schools, emergency response systems, and others. The Quake-Catcher Network also provides educational software designed to help teach about earthquakes and earthquake hazards.

After almost eight years at Stanford, and a year at CalTech, the QCN project is moving to the University of Southern California Dept. of Earth Sciences. QCN will be sponsored by the Incorporated Research Institutions for Seismology (IRIS) and the Southern California Earthquake Center (SCEC).

The Quake-Catcher Network is a distributed computing network that links volunteer hosted computers into a real-time motion sensing network. QCN is one of many scientific computing projects that runs on the world-renowned distributed computing platform Berkeley Open Infrastructure for Network Computing (BOINC).


BOINC WallPaper

The volunteer computers monitor vibrational sensors called MEMS accelerometers, and digitally transmit “triggers” to QCN’s servers whenever strong new motions are observed. QCN’s servers sift through these signals, and determine which ones represent earthquakes, and which ones represent cultural noise (like doors slamming, or trucks driving by).

There are two categories of sensors used by QCN: 1) internal mobile device sensors, and 2) external USB sensors.

Mobile Devices: MEMS sensors are often included in laptops, games, cell phones, and other electronic devices for hardware protection, navigation, and game control. When these devices are still and connected to QCN, QCN software monitors the internal accelerometer for strong new shaking. Unfortunately, these devices are rarely secured to the floor, so they may bounce around when a large earthquake occurs. While this is less than ideal for characterizing the regional ground shaking, many such sensors can still provide useful information about earthquake locations and magnitudes.

USB Sensors: MEMS sensors can be mounted to the floor and connected to a desktop computer via a USB cable. These sensors have several advantages over mobile device sensors. 1) By mounting them to the floor, they measure more reliable shaking than mobile devices. 2) These sensors typically have lower noise and better resolution of 3D motion. 3) Desktops are often left on and do not move. 4) The USB sensor is physically removed from the game, phone, or laptop, so human interaction with the device doesn’t reduce the sensors’ performance. 5) USB sensors can be aligned to North, so we know what direction the horizontal “X” and “Y” axes correspond to.

If you are a science teacher at a K-12 school, please apply for a free USB sensor and accompanying QCN software. QCN has been able to purchase sensors to donate to schools in need. If you are interested in donating to the program or requesting a sensor, click here.

BOINC is a leader in the field(s) of Distributed Computing, Grid Computing and Citizen Cyberscience.BOINC is more properly the Berkeley Open Infrastructure for Network Computing, developed at UC Berkeley.

Earthquake safety is a responsibility shared by billions worldwide. The Quake-Catcher Network (QCN) provides software so that individuals can join together to improve earthquake monitoring, earthquake awareness, and the science of earthquakes. The Quake-Catcher Network (QCN) links existing networked laptops and desktops in hopes to form the worlds largest strong-motion seismic network.

Below, the QCN Quake Catcher Network map
QCN Quake Catcher Network map