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Forecasting induced seismicity in Oklahoma using machine learning methods.

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Area of Science:

  • Geophysics
  • Earthquake Science
  • Machine Learning Applications

Background:

  • Oklahoma earthquakes are primarily linked to wastewater injection activities.
  • Understanding and predicting induced seismicity is crucial for risk management.

Purpose of the Study:

  • To forecast the rate of induced seismicity in Oklahoma using machine learning.
  • To identify key parameters influencing earthquake occurrence related to fluid injection.

Main Methods:

  • Utilized the Random Forest machine learning algorithm.
  • Trained the model on operational (injection rate, pressure), geological (basement depth), and modeled stress/pressure data from 2011-2015.
  • Tested the model's forecasting ability on data from 2015-2020.

Main Results:

  • The Random Forest model achieved a good overall match with observed seismicity rates (adjusted R-squared of 0.75).
  • Pore pressure rate and poroelastic stressing rates were identified as the most significant predictors.
  • Temporal changes in stressing rates were found to be more critical than absolute stress values.

Conclusions:

  • Machine learning, specifically Random Forest, can effectively forecast induced seismicity rates.
  • Focusing on the rates of change in pore pressure and poroelastic stress is key to predicting earthquake activity.
  • This study highlights the dynamic nature of stress changes in triggering seismic events.