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Earthquake Rate Changes: Mechanisms and Impacts on Forecasting: Andrea L. Llenos


Project Title: Earthquake Rate Changes: Mechanisms and Impacts on Forecasting
Mendenhall Fellow: Andrea L. Llenos, (650) 329-5562, allenos@usgs.gov
Duty Station: Menlo Park, CA
Start Date: March 31, 2011
Education: Ph.D. (2010), Geophysics, MIT/Woods Hole Oceanographic Institution Joint Program in Oceanography
Research Advisors: Andrew Michael, (650) 329-4777, michael@usgs.gov; Jeanne Hardebeck, (650) 329-4711, jhardebeck@usgs.gov; Jessica Murray-Moraleda, (650) 329-4864, jrmurray@usgs.gov; Fred Pollitz, (650) 329-4821, fpollitz@usgs.gov; Thomas Parsons, (650) 329-5074, tparsons@usgs.gov
Project Description: Current seismic hazard assessment and forecasting models are based on the idea that long-term strain rates in a region are constant, and therefore variations in seismicity rate are typically due to earthquake clusters (that is, foreshocks and aftershocks). However, the strain rate can vary on timescales of weeks to years due to transient physical processes such as fluid flow, aseismic creep on a fault, and magmatic intrusion. These transient deformation processes often trigger spatial and temporal variations in regional earthquake rates that are not well accounted for in the statistical models currently used in earthquake forecasting. Determining what physical mechanisms can trigger earthquake rate changes, the conditions under which this triggering occurs, and how these changes can affect earthquake forecasts, is a critical step towards both understanding earthquake occurrence and improving seismic hazard assessments.

This Mendenhall research project will be focused on two primary themes: (1) identifying physical mechanisms that trigger earthquake rate changes in earthquake catalogs and (2) determining how these rate changes affect probabilistic forecasts. To address the first theme, we will begin by searching for earthquake rate changes potentially triggered by aseismic transients, which can then be compared with available GPS or strainmeter data to verify that the transient is tectonic in origin. One region of interest is Mammoth Mountain in the Long Valley Caldera in California. Long Valley seismicity over the past several decades includes earthquake swarms triggered by magma intrusion and degassing (for example, Hil land others, 1990), dynamically triggered rate changes from distant large earthquakes such as the 1992 Landers earthquake (Hill and others, 1993) and the 2002 Denali earthquake (Prejean and others, 2004), as well as mainshock-aftershock sequences (Hill and others, 2003). The variety of potential triggering mechanisms makes this region a fascinating place to not only investigate earthquake rate changes but also to examine whether it is possible to distinguish between different triggering mechanisms from catalog data (that is, magnitude-time-location history).

Although strain transients have been observed to trigger changes in seismicity rate, it is still unclear how these changes would affect short-term (on the order of days) and intermediate-term (on the order of years) earthquake probabilities. Current earthquake probability models, such as the Short Term Earthquake Probability (STEP) algorithm (Gerstenberger and others, 2005) used by the U.S. Geological Survey to provide 24-hour aftershock forecasts in California, or the Epidemic Type Aftershock Sequence (ETAS) model (Ogata, 1988), typically assume that the background rate is constant over time. This assumption implies that the background stress/strain rate is also constant at the long-term plate tectonic loading rate. However, aseismic transients can occur at timescales of weeks to months and have been shown to cause detectable variations in seismicity rate. It is becoming increasingly clear that time-dependent background rates are necessary to adequately model earthquake rates observed in catalogs where external processes are occurring (for example, Hainzl and Ogata, 2005; Lombardi and others, 2010; Daniel and others, 2011). Therefore, we will also explore how short-term forecasting models that incorporate time-dependent background rates compare with more traditional forecasting models.

Ultimately, this project will provide some insight into the physical processes that trigger earthquakes and help provide information to constrain the impact that stress/strain rate changes may have on short- and intermediate-term earthquake forecasts. These findings could lead to significant improvements in short-term probabilistic forecasting as well as regional and national scale hazard assessments.

References

Daniel, G., Prono, E., Renard, F., Thouvenot, F., Hainzl, F., Marsan, D., Helmstetter, A., Traversa, P., Got, J.L., Jenatton, L., and Guiguet, R., 2011, Changes in effective stress during the 2003–2004 Ubaye seismic swarm, France: Journal of Geophysical Research, v. 116, B01309, doi:10.1029/2010JB007551.

Gerstenberger, M.C., Wiemer, S., Jones, L.M., and Reasenberg, P.A., 2005, Real-time forecasts of tomorrow’s earthquakes in California: Journal of Geophysical Research, v. 435, p. 328–331.

Hainzl, S., and Y. Ogata 2005, Detecting fluid signals in seismicity data through statistical earthquake modeling: Journal of Geophysical Research, v. 110, doi:10.1029/2004JB003247.

Hill, D.P., Ellsworth, W.L., Johnston, M.J.S., Langbein, J.O., Oppenheimer, D.H., Pitt, A.M., Reasenberg, P.A., Sorey, M.L., and McNutt, S.R., 1990, The 1989 earthquake swarm beneath Mammoth Mountain, California: An initial look at the 4 May through 30 September activity: Bulletin of the Seismological Society of America, v. 80, p. 325–339.

Hill, D.P., Langbein, J.O., and Prejean, S., 2003, Relations between seismicity and deformation during unrest in Long Valley Caldera, California, from 1995 through 1999: Journal of. Volcanological and Geothermermal Research, v. 127, p. 175–193.

Lombardi, A.M., Cocco, M., and Marzocchi, W., 2010, On the increase of background seismicity rate during the 1997–1998 Umbria-Marche, Central Italy, sequence: Apparent variation or fluid-driven triggering?: Bulletin of the Seismological Society of America, v. 100, p. 1138–1152, doi:10.1785/0120090077.

Ogata, Y., 1988, Statistical models for earthquake occurrences and residual analysis for point processes: Journal of the American Statistical Association, v. 83, p. 9–27.

Prejean, S.G., Hill, D.P., Brodsky, E.E. Hough, S.E., Johnston, M.J.S., Malone, S.D., Oppenheimer, D.H., Pitt, A.M., and Richards-Dinger, K.B., 2004, Observations of remotely triggered seismicity on the United States West Coast following the M7.9 Denali Fault earthquake: Bulletin of the Seismological Society of America, v. 94, p. S348–S359.


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