Poster #074, Seismology

Analyzing Stress drops and other earthquake parameters from the 2019 Ridgecrest Earthquake Sequence

Vivian G. Rosas, & Annemarie S. Baltay
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Poster Presentation

2020 SCEC Annual Meeting, Poster #074, SCEC Contribution #10738 VIEW PDF
Stress drop is related to the energy released from an earthquake rupture. Of great interest is how stress drop scales with different variables such as magnitude, moment, depth, etc. Since stress drop is a key source parameter in describing the energetics of an earthquake, these relationships can help understand future earthquakes and their physical effects. The 2019 Ridgecrest earthquake sequence had 3,557 earthquakes of magnitude M2+ recorded in the first two weeks alone, the largest magnitude being 7.1. This earthquake sequence provided a wealth of high-quality data has been analyzed by multiple researchers using multiple methods. This presents an opportunity to help determine important pa...tterns in stress drops and how they reflect true source processes and complexities.

To understand how stress drops from different researchers and approaches differentiate, we examine stress drop data sets . From Trugman (2020) who uses a spectral decomposition method, and Arias stress drops as calculated in Parker et al. (2020). We use MATLAB to load the data to plot and compare; creating different types of plots helps information become clearer by comparing the differences and similarities. When analyzing the data sets, we observed how the intersecting data on stress drops did not show similarities, this might be due to the various methods used in finding stress drops. We continue to collect more stress drop estimates to compare a larger dataset to see if a difference in results emerge, considering other methods, such as the single-station Brune fitting method used in the gmprocess software (Rekoske et al., 2019) as well as ground-motion measures of stress drop from Parker et al. (2020). We examine not just the stress drop values, but also how the seismic moments and corner frequencies compare between methods. We try to understand if the similarities and differences between the datasets are indicators of other physical earthquake information, to get better knowledge of the foundation of these events.

This research is part of the stepping stones to helping model earthquakes that could happen in the future. The more we study earthquake parameters and their effects the more we are able to better prepare for upcoming seismic events.