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ABSTRACT We present initial findings from the ongoing Community Stress Drop Validation Study to compare spectral stress-drop estimates for earthquakes in the 2019 Ridgecrest, California, sequence. This study uses a unified dataset to independently estimate earthquake source parameters through various methods. Stress drop, which denotes the change in average shear stress along a fault during earthquake rupture, is a critical parameter in earthquake science, impacting ground motion, rupture simulation, and source physics. Spectral stress drop is commonly derived by fitting the amplitude-spectrum shape, but estimates can vary substantially across studies for individual earthquakes. Sponsored jointly by the U.S. Geological Survey and the Statewide (previously, Southern) California Earthquake Center our community study aims to elucidate sources of variability and uncertainty in earthquake spectral stress-drop estimates through quantitative comparison of submitted results from independent analyses. The dataset includes nearly 13,000 earthquakes ranging from M 1 to 7 during a two-week period of the 2019 Ridgecrest sequence, recorded within a 1° radius. In this article, we report on 56 unique submissions received from 20 different groups, detailing spectral corner frequencies (or source durations), moment magnitudes, and estimated spectral stress drops. Methods employed encompass spectral ratio analysis, spectral decomposition and inversion, finite-fault modeling, ground-motion-based approaches, and combined methods. Initial analysis reveals significant scatter across submitted spectral stress drops spanning over six orders of magnitude. However, we can identify between-method trends and offsets within the data to mitigate this variability. Averaging submissions for a prioritized subset of 56 events shows reduced variability of spectral stress drop, indicating overall consistency in recovered spectral stress-drop values.
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Preparing Spectral Data for Machine Learning: A Study of Geological Classification from Aerial Surveys
This study focuses on improving the preparation of spectral data for machine learning. It does so by conducting a case study that involves matching an airborne gamma-ray spectral survey of the San Francisco Bay area to geological classifications provided by the United States Geological Survey (Graymer et al., 2006).Our investigation has revealed three key approaches for enhancing accuracy in this task:1) eliminating extraneous data segments unrelated to the main task,2) augmenting minority classes to improve class balances,and 3) merging inconsistent classes.By incorporating these methods, we were able to achieve a significant increase in classification accuracy. Specifically, we increased the accuracy from an initial 40.8% to approximately 72.7%. We plan to continue our work to further enhance performance, with the goal of extending the applicability of these methods to other data types and tasks. One potential future application is the detection of rare earth elements from aerial surveys.
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- Award ID(s):
- 2247619
- PAR ID:
- 10509399
- Publisher / Repository:
- Machine Learning and the Physical Sciences Workshop, NeurIPS 2023.
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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