The Geological Susceptibility of Induced Earthquakes in the Duvernay Play

The Geological Susceptibility of Induced Earthquakes in the Duvernay Play Presently, consensus on the incorporation of induced earthquakes into seismic hazard has yet to be established. For example, the nonstationary, spatiotemporal nature of induced earthquakes is not well understood. Specific to the Western Canada Sedimentary Basin, geological bias in seismogenic activation potential has been suggested to control the spatial distribution of induced earthquakes regionally. In this paper, we train a machine learning algorithm to systemically evaluate tectonic, geomechanical, and hydrological proxies suspected to control induced seismicity. Feature importance suggests that proximity to basement, in situ stress, proximity to fossil reef margins, lithium concentration, and rate of natural seismicity are among the strongest model predictors. Our derived seismogenic potential map faithfully reproduces the current distribution of induced seismicity and is suggestive of other regions which may be prone to induced earthquakes. The refinement of induced seismicity geological susceptibility may become an important technique to identify significant underlying geological features and address induced seismic hazard forecasting issues. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Geophysical Research Letters Wiley

The Geological Susceptibility of Induced Earthquakes in the Duvernay Play

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
©2018. American Geophysical Union. All Rights Reserved.
ISSN
0094-8276
eISSN
1944-8007
D.O.I.
10.1002/2017GL076100
Publisher site
See Article on Publisher Site

Abstract

Presently, consensus on the incorporation of induced earthquakes into seismic hazard has yet to be established. For example, the nonstationary, spatiotemporal nature of induced earthquakes is not well understood. Specific to the Western Canada Sedimentary Basin, geological bias in seismogenic activation potential has been suggested to control the spatial distribution of induced earthquakes regionally. In this paper, we train a machine learning algorithm to systemically evaluate tectonic, geomechanical, and hydrological proxies suspected to control induced seismicity. Feature importance suggests that proximity to basement, in situ stress, proximity to fossil reef margins, lithium concentration, and rate of natural seismicity are among the strongest model predictors. Our derived seismogenic potential map faithfully reproduces the current distribution of induced seismicity and is suggestive of other regions which may be prone to induced earthquakes. The refinement of induced seismicity geological susceptibility may become an important technique to identify significant underlying geological features and address induced seismic hazard forecasting issues.

Journal

Geophysical Research LettersWiley

Published: Jan 28, 2018

Keywords: ; ; ;

References

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