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Abstract In areas of induced seismicity, earthquakes can be triggered by stress changes due to fluid injection and static deformation from fault slip. Here we present a method to distinguish between injection‐driven and earthquake‐driven triggering of induced seismicity by combining a calibrated, fully coupled, poroelastic stress model of wastewater injection with interpretation of a machine learning algorithm trained on both earthquake catalog and modeled stress features. We investigate seismicity from Paradox Valley, Colorado as an ideal test case: a single, high‐pressure injector that has induced thousands of earthquakes since 1991. Using feature importance analysis, we find that injection‐driven earthquakes are approximately 225% of the total catalog but act as background events that can trigger subsequent aftershocks. Injection‐driven events also have distinct spatiotemporal clustering properties with a larger b‐value, closer proximity to the well, and earlier occurrence in the injection history. Generalization of our technique can help characterize triggering processes in other regions where induced seismicity occurs.
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Long Short-Term Memory Networks for Pattern Recognition of Synthetical Complete Earthquake Catalog
Exploring the spatiotemporal distribution of earthquake activity, especially earthquake migration of fault systems, can greatly to understand the basic mechanics of earthquakes and the assessment of earthquake risk. By establishing a three-dimensional strike-slip fault model, to derive the stress response and fault slip along the fault under regional stress conditions. Our study helps to create a long-term, complete earthquake catalog. We modelled Long-Short Term Memory (LSTM) networks for pattern recognition of the synthetical earthquake catalog. The performance of the models was compared using the mean-square error (MSE). Our results showed clearly the application of LSTM showed a meaningful result of 0.08% in the MSE values. Our best model can predict the time and magnitude of the earthquakes with a magnitude greater than Mw = 6.5 with a similar clustering period. These results showed conclusively that applying LSTM in a spatiotemporal series prediction provides a potential application in the study of earthquake mechanics and forecasting of major earthquake events.
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- Award ID(s):
- 1918126
- PAR ID:
- 10253866
- Date Published:
- Journal Name:
- Sustainability
- Volume:
- 13
- Issue:
- 9
- ISSN:
- 2071-1050
- Page Range / eLocation ID:
- 4905
- 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/10.3390/su13094905
