The SCEC/USGS Community Stress Drop Validation TAG using the 2019 Ridgecrest Earthquake Sequence Data

Annemarie S. Baltay, Shanna Chu, Rachel E. Abercrombie, & Taka'aki Taira

Submitted September 11, 2022, SCEC Contribution #12049, 2022 SCEC Annual Meeting Poster #012

We present earthquake stress drop comparisons from the SCEC/USGS community stress drop validation study using the 2019 Ridgecrest earthquake sequence, in which researchers are invited to use a common dataset to estimate earthquake stress drop. We seek to understand the physical controls and methodological reasons for similarity or differences in stress drop estimates, so that they can be used more reliably by the earthquake science community. The common dataset consists of 2 weeks of earthquakes following the 2019 Ridgecrest earthquake, including nearly 13,000 events of M1 and greater, recorded on stations within 100 km; we also have selected 55 events of M2 - 4, representing a range of depths, mechanisms and azimuths, for focused study.

In the initial submission round in November 2021, we received 14 results submissions from 11 research groups using the common data set and have since received several more, including refinements and updates to the initial results. The analytical approaches used include spectral decomposition/generalized inversion; spectral ratios or empirical Green’s functions (eGfs) in the frequency domain; source time functions or eGf in the time domain; and other methods such as ground-motion and single-station approaches. Comparison of submitted stress drops reveals considerable scatter, yet stronger correlations between results using similar methods. We observe significantly reduced inter-method scatter within a subset of events that were used in at least 8 analyses. We also consider uncertainty in other parameters, such as moment, record quality control, earthquake depth, and path considerations. Lastly, we consider the use of a dataset of synthetic earthquake records, for which we would prescribe the stress drop, as a way to test the methods and assumptions alone, while controlling for path and recording station variability.

Baltay, A. S., Chu, S., Abercrombie, R. E., & Taira, T. (2022, 09). The SCEC/USGS Community Stress Drop Validation TAG using the 2019 Ridgecrest Earthquake Sequence Data. Poster Presentation at 2022 SCEC Annual Meeting.

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