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ABSTRACT Precise knowledge of earthquake magnitudes is vital for accurate characterization of seismic hazards. However, the estimation of earthquake magnitude, particularly for small events, is complicated by differences in network procedures and completeness. This produces disparate magnitude estimates for the same event and emphasizes the need for a consistent and transportable magnitude estimation procedure. Here, we investigate the use of the relative magnitude method, which measures earthquake magnitude from a least-squares inversion of interlinked waveform amplitude ratios. Our results show that that the relative magnitude method can establish both local and moment magnitudes for many events in the 2019 Ridgecrest sequence. The method also provides constraints on moment magnitude estimates for M <3 events, which are not routinely available using current methods. Although the relative magnitude method is advantageous because it can be applied uniformly in various regions and does not require empirical distance or attenuation corrections, there are several parameters that require subjective decision making and may introduce bias in the resulting magnitude estimates. These include acceptable thresholds for signal-to-noise ratios and cross correlation, filtering procedures, sampling windows, and station selection. Here, we not only calculate magnitude but also investigate how the subjective decision making affects the resulting magnitudes. Based on our analysis, we present recommendations to enhance the utility of this method for future users.
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HypoSVI: Hypocentre inversion with Stein variational inference and physics informed neural networks
SUMMARY We introduce a scheme for probabilistic hypocentre inversion with Stein variational inference. Our approach uses a differentiable forward model in the form of a physics informed neural network, which we train to solve the Eikonal equation. This allows for rapid approximation of the posterior by iteratively optimizing a collection of particles against a kernelized Stein discrepancy. We show that the method is well-equipped to handle highly multimodal posterior distributions, which are common in hypocentral inverse problems. A suite of experiments is performed to examine the influence of the various hyperparameters. Once trained, the method is valid for any seismic network geometry within the study area without the need to build traveltime tables. We show that the computational demands scale efficiently with the number of differential times, making it ideal for large-N sensing technologies like Distributed Acoustic Sensing. The techniques outlined in this manuscript have considerable implications beyond just ray tracing procedures, with the work flow applicable to other fields with computationally expensive inversion procedures such as full waveform inversion.
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- Award ID(s):
- 1822214
- PAR ID:
- 10536169
- Publisher / Repository:
- Oxford university Press
- Date Published:
- Journal Name:
- Geophysical Journal International
- Volume:
- 228
- Issue:
- 1
- ISSN:
- 0956-540X
- Page Range / eLocation ID:
- 698 to 710
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/gji/ggab309
