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Forecasting Induced Earthquake Hazard Using a Hydromechanical Earthquake Nucleation Model

Authors: Justin L. Rubinstein; Andrew J. Barbour; Jack H. Norbeck;

Forecasting Induced Earthquake Hazard Using a Hydromechanical Earthquake Nucleation Model

Abstract

AbstractIn response to the dramatic increase in earthquake rates in the central United States, the U.S Geological Survey began releasing 1 yr earthquake hazard models for induced earthquakes in 2016. Although these models have been shown to accurately forecast earthquake hazard, they rely purely on earthquake statistics because there was no precedent for forecasting induced earthquakes based upon wastewater injection data. Since the publication of these hazard models, multiple physics-based methods have been proposed to forecast earthquake rates using injection data. Here, we use one of these methods to generate earthquake hazard forecasts. Our earthquake hazard forecasts are more accurate than statistics-based hazard forecasts. These results imply that fluid injection data, where and when available, and the physical implications of fluid injection should be included in future induced earthquake hazard forecasts.

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Top 10%
Average
Top 10%
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