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Bombay Beach Swarm Triggering Probabilities for Larger Earthquakes (On the San Andreas and Elsewhere)

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Bombay Beach Swarm Triggering Probabilities for Larger Earthquakes (On the San Andreas and Elsewhere) The 2016 Bombay Beach Swarm Triggering Probabilities for Larger Earthquakes (on the San Andreas and Elsewhere) Morgan Page USGS Pasadena The Basics of Earthquake Forecasting The Statistical Seismologist’s Approach M 104 ≥ Gutenberg-Richter Magnitude Scaling 102 Earthquakes in California, 1984-2011 b=1 100 -2 Number of earthquakes per year 10 345678 Magnitude (M) The Basics of Earthquake Forecasting The Statistical Seismologist’s Approach M 104 ≥ Gutenberg-Richter Magnitude Scaling 102 Earthquakes in California, 1984-2011 b=1 103 100 Omori Decay of 102 -2 Number of earthquakes per year 10 345678 Aftershock 101 Magnitude (M) Rate aftershock rate Daily 100 10-1 10-2 10-1 100 101 Time since Mainshock (days) The Basics of Earthquake Forecasting The Statistical Seismologist’s Approach M 104 ≥ Gutenberg-Richter Magnitude Scaling 102 Earthquakes in California, 1984-2011 b=1 103 100 Omori Decay of 102 -2 Number of earthquakes per year 10 345678 Aftershock 101 Magnitude (M) Rate aftershock rate Daily 100 10-1 10-2 10-1 100 101 Big earthquakes trigger more aftershocks Time since Mainshock (days) The Basics of Earthquake Forecasting The Statistical Seismologist’s Approach M 104 ≥ Gutenberg-Richter Magnitude Scaling 102 Earthquakes in California, 1984-2011 b=1 103 100 Omori Decay of 102 -2 Number of earthquakes per year 10 345678 Aftershock 101 Magnitude (M) Rate aftershock rate Daily 100 10-1 10-2 10-1 100 101 Big earthquakes trigger more aftershocks Time since Mainshock (days) Aftershock rates decay with distance from the mainshock The Basics of Earthquake Forecasting The Statistical Seismologist’s Approach M 104 ≥ Gutenberg-Richter Magnitude Scaling 102 Earthquakes in California, 1984-2011 b=1 103 100 Omori Decay of 102 -2 Number of earthquakes per year 10 345678 Aftershock 101 Magnitude (M) Rate aftershock rate Daily 100 10-1 10-2 10-1 100 101 Big earthquakes trigger more aftershocks Time since Mainshock (days) Aftershock rates decay with distance from the mainshock These scaling laws are used in short-term forecasting models like ETAS (Ogata, 1988) and STEP (Gerstenberger et al., 2005) ETAS Predictions The foreshock rate and the aftershock rate follow the same trend Given the aftershock rate, you can predict the foreshock rate, suggesting they represent the same process California data Felzer et al. (2004) ETAS Predictions No significant correlation between number of foreshocks and mainshock size size of mainshocks dashed lines - aftershock rates for different mainshock sizes solid lines - foreshock rates for different mainshock sizes Southern California data Helmstetter and Sornette (2003) Inverse Omori Acceleration ETAS models can match the acceleration seen in Bouchon et al. (2014) dataset A) Normalized 150-Day Stack B) Normalized 5-Day Stack 1 1 ETAS Mean & 95% Confidence ETAS Mean & 95% Confidence 0.9 0.9 0.8 Mainshock Data 0.8 Mainshock Data 0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 Cumulative number of earthquakes (normalized) Cumulative number of earthquakes (normalized) 150 100 50 0 5 4 3 2 1 0 Time before mainshocks (days) Time before mainshocks (days) Felzer, Page and Michael (2015) Foreshocks are not predictive of mainshock size Moment rate functions all start out the same, which suggests the earthquake doesn’t “know” its final size What do changing earthquake probabilities look like in time? M ≥ 8 M ≥ 7 M ≥ 6 M ≥6 Event times M ≥ 2.5 rate = proxy for large event probability 104 103 2.5 rate ≥ M 102 Monthly ETAS Rate Average Rate 101 0 5 10 15 20 2 5 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Time (years) Field and Milner (2018) Even though foreshocks are not predictive of earthquake size, foreshock/ aftershock statistics can give orders of magnitude changes in the probabilities for future earthquakes of all sizes. The 2016 Bombay Beach Swarm McBride, Llenos, Page, and van der Elst (submitted) Major forecast uncertainties What is the appropriate magnitude How long will increased rate of distribution for the Southern San earthquakes last? Andreas? Major forecast uncertainties Swarm duration in Salton trough are well-fit by a Poisson model 1 1 (a) 0.8 0.8 0.6 0.6 ending today ending today / / 0.4 0.4 Swarm catalog Swarm catalog 0.2 Poisson model 0.2 Poisson model Swarm catalog Swarm catalog Poisson model Poisson model 0 0 Probability over Probability over 051015202530 05101520253035 Duration (days) Duration (days) 15% chance of terminating each day Average length of 7 days How long will increased rate of earthquakes last? Llenos and van der Elst, 2019 Major forecast uncertainties Schwartz and Coppersmith (1984) What is the appropriate magnitude 20 km from South-Central SAF distribution for the Southern San (Aftershocks of 1952 Kern country and 1971 San Fernando earthquakes removed) Andreas? Major forecast uncertainties Page and Felzer (2015) Schwartz and Coppersmith (1984) What is the appropriate magnitude 20 km from South-Central SAF distribution for the Southern San (Aftershocks of 1952 Kern country and 1971 San Fernando earthquakes removed) Andreas? Major forecast uncertainties effect of proximity to San Andreas distance from CFM fault distance from In the instrumental catalog, we do not see that earthquakes near major faults are larger. We also do not see that earthquakes near faults produce more aftershocks or are more likely to be a foreshock to a larger event. Page and van der Elst, 2018 Major forecast uncertainties lead to factor of 30 difference in hazard estimate 0.03% to 1% chance of M≥7 earthquake next 7 days Probabilities decay very quickly UCERF3 Forecast following Bombay Swarm What faults are likely to be triggered? Aftershock forecast following Bombay Beach swarm Average of 100,000 simulations UCERF3 Forecast following Bombay Swarm How are probabilities elevated compared to long-term rate? San Andreas (Coachella section) Rupture Probabilities r r k rs rs y day 1 wee 10 y 1 yea 1 hou 100 1 1 0-1 1 month 1 0-2 bability 1 0-3 1 0-4 1 0-5 M≥6.7 Participation Pro UCERF3-TI UCERF3-TD UCERF3-ETAS 1 0-6 -3 -2 -1 0 1 2 1 0 1 0 1 0 1 0 1 0 1 0 Forecast Timespan (years) Conclusions During seismic swarms, the chance of a large earthquake can change San Andreas (Coachella section) Rupture Probabilities r r k rs rs y day by orders of magnitude 1 wee 10 y 1 yea 1 hou 100 1 1 0-1 1 month 1 0-2 bability These elevated -3 1 0 probabilities decay 1 0-4 quickly 1 0-5 M≥6.7 Participation Pro UCERF3-TI UCERF3-TD UCERF3-ETAS 1 0-6 Proximity to major faults is -3 -2 -1 0 1 2 1 0 1 0 1 0 1 0 1 0 1 0 Forecast Timespan (years) a concern (but it is not proven that proximity to major faults elevates foreshock probability) Conclusions During seismic swarms, the chance of a large earthquake can change by orders of magnitude These elevated probabilities decay quickly Proximity to major faults is a concern (but it is not proven that Questions? proximity to major faults elevates foreshock probability) .
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