Predicting Induced Seismicity in Oklahoma Using Machine Learning Methods

  • Ellsworth, WL Injection-induced earthquakes. Science 3411225942. https://doi.org/10.1126/science.1225942 (2013).

    PubMed Google Scholar article

  • Keranen, KM, Weingarten, M., Abers, GA, Bekins, BA & Ge, S. Sharp increase in central Oklahoma seismicity since 2008 induced by massive sewage injection. Science 345, 448–451. https://doi.org/10.1126/science.1255802 (2014).

    ADS CAS PubMed Article Google Scholar

  • Yeah, w. et al. Far-field pressurization probably caused one of the largest injection-induced earthquakes by reactivating a large, pre-existing subsurface fault structure. Geophys. Res. Lett. 43. https://doi.org/10.1002/2016GL070861 (2016).

  • Haffener, J., Chen, X. & Murray, K. Multi-scale analysis of spatio-temporal relationship between injection and seismicity in Oklahoma. J. Geophys. Res. solid ground 1238711–8731 (2018).

    Article on Google Scholar Ads

  • Langenbruch, C., Weingarten, M. & Zoback, MD Physics-Based Prediction of Man-Made Earthquake Hazard in Oklahoma and Kansas. Nat. Common. 9, 1–10. https://doi.org/10.1038/s41467-018-06167-4 (2018).

    CAS Google Scholar Article

  • Segall, P. & Lu, S. Injection-induced seismicity: poroelastic and seismic nucleation effects. J. Geophys. Res. solid ground 1205082–5103 (2015).

    Article on Google Scholar Ads

  • Goebel, T., Weingarten, M., Chen, X., Haffener, J. & Brodsky, E. The 2016 mw5.1 fairview, Oklahoma earthquakes: Evidence for long-range poroelastic triggering at > 40 km from fluid evacuation wells. Earth. Science. Lett. 47250–61 (2017).

    ADS CAS Article Google Scholar

  • Zhai, G., Shirzaei, M., Manga, M. & Chen, X. Pore-elastic stress-enhanced pore pressure diffusion controls induced seismicity in Oklahoma. proc. Natl. Acad. Science. 11616228–16233 (2019).

    ADS CAS Article Google Scholar

  • Chen, X. et al. The Pawnee earthquake as a result of the interaction between injection, faulting and anticipation. Science. representing seven1–18 (2017).

    Google Scholar article

  • Pennington, C. & Chen, X. Coulomb strain interactions during the Mw 5.8 Pawnee sequence. Seismol. Res. Lett. 881024-1031 (2017).

    Google Scholar article

  • Qin, Y., Chen, X., Carpenter, BM & Kolawole, F. Coulomb stress transfer influences fault reactivation in wastewater injection zones. Geophys. Res. Lett. 4511–059 (2018).

    Google Scholar

  • Eyre, TS et al. The role of seismic slip in hydraulic fracturing-induced seismicity. Science. Adv. 5eaav7172 (2019).

    Article on Google Scholar Ads

  • Norbeck, J. & Rubinstein, JL The hydromechanical model of seismic nucleation predicts the onset, peak, and fall in rates of induced seismicity in Oklahoma and Kansas. Geophys. Res. Lett. 452963-2975 (2018).

    Article on Google Scholar Ads

  • Hincks, T., Aspinall, W., Cooke, R. & Gernon, T. Oklahoma-induced seismicity is strongly related to sewage injection depth. Science 3591251-1255 (2018).

    ADS CAS Article Google Scholar

  • Shah, AK & Keller, GR Geological Influence on Induced Seismicity: Potential Field Data Constraints in Oklahoma. Geophys. Res. Lett. 44152-161 (2017).

    Article on Google Scholar Ads

  • Pei, S., Peng, Z. & Chen, X. Locations of injection-induced earthquakes in Oklahoma controlled by crustal structures. J. Geophys. Res. solid ground 1232332-2344 (2018).

    Article on Google Scholar Ads

  • Weingarten, M., Ge, S., Godt, JW, Bekins, BA, and Rubinstein, JL High-rate injection is associated with increased seismicity in the mid-continent of the Americas. Science 3481336-1340 (2015).

    ADS CAS Article Google Scholar

  • Montoya-Noguera, S. & Wang, Y. Bayesian identification of multiple seismic change points and variable seismic rates caused by induced seismicity. Geophys. Res. Lett. 443509-3516 (2017).

    Article on Google Scholar Ads

  • Zhu, W. & Beroza, GC Phasenet: A seismic arrival time selection method based on a deep neural network. Geophys. J.Int. 216261-273 (2019).

    Article on Google Scholar Ads

  • Mousavi, SM, Ellsworth, WL, Zhu, W., Chuang, LY & Beroza, GC Earthquake transformer – an attentive deep learning model for simultaneous earthquake detection and phase selection. Nat. Common. 111–12 (2020).

    Google Scholar article

  • Rubinstein, JL, Barbour, AJ & Norbeck, JH Induced Earthquake Hazard Prediction Using a Hydromechanical Seismic Nucleation Model. Seismol. Res. Lett. (2021).

  • Langenbruch, C. & Shapiro, SA Decay rate of fluid-induced seismicity after termination of reservoir stimulations after injection seismicity. Geophysics 75MA53–MA62 (2010).

    Article on Google Scholar Ads

  • Shapiro, SA, Dinske, C., Langenbruch, C. & Wenzel, F. Seismogenic Index and Magnitude Probability of Induced Earthquakes During Reservoir Fluid Stimulations. Leading edge 29304–309 (2010).

    Google Scholar article

  • Barbour, AJ, Norbeck, JH & Rubinstein, JL The effects of varying injection rates in Osage County, Oklahoma, on the 2016 Mw 5.8 Pawnee earthquake. Seismol. Res. Lett. 881040-1053 (2017).

    Google Scholar article

  • Dieterich, J., Cayol, V. & Okubo, P. Using earthquake rate changes as a stress meter at Kilauea Volcano. Nature 408457–460 (2000).

    ADS CAS Article Google Scholar

  • Walter, J.I. et al. The Oklahoma Geological Survey statewide seismic network. Seismol. Res. Lett. 91611–621 (2020).

    Google Scholar article

  • Wiemer, S. & Wyss, M. Minimum Magnitude of Completeness in Earthquake Catalogs: Examples from Alaska, the Western United States, and Japan. Bull. Seismol. Soc. A m. 90859–869 (2000).

    Google Scholar article

  • Rosson, Z., Walter, J., Goebel, T., and Chen, X. Narrow Spatial Aftershock Zones for Oklahoma Induced Earthquake Sequences. Geophys. Res. Lett. 46, 10358–10366. https://doi.org/10.1029/2019GL083562 (2019).

    Article on Google Scholar Ads

  • Crain, KD & Chang, JC Crystalline Basement Summit Elevation Map in Oklahoma and Surrounding States. Oklahoma Geol. Surv. File opened Rept. OF1-2018 (2018).

  • Ho, T. K. Random Decision Forests. In Proceedings of the 3rd International Conference on Document Analysis and Recognition, Flight. 1, 278–282 (IEEE, 1995).

  • Ho, TK Random subspace method for building decision forests. IEEE Trans. Anal model. Mach. Information. 20832–844 (1998).

    Google Scholar article

  • Rouet-Leduc, B. et al. Machine learning predicts earthquakes in the lab. Geophys. Res. Lett. 449276–9282 (2017).

    Article on Google Scholar Ads

  • Sherry J. Basler