Controlling earthquake-like instabilities using artificial intelligence
This research addresses minimizing seismicity in industrial projects like geothermal energy, with potential future applications to natural earthquake control, representing an incremental step in applying AI to geophysical problems.
The study tackled the problem of controlling earthquake-like instabilities by applying reinforcement learning to develop injection policies, showing for the first time that deep reinforcement learning can control such instabilities using a reduced physical model.
Earthquakes are lethal and costly. This study aims at avoiding these catastrophic events by the application of injection policies retrieved through reinforcement learning. With the rapid growth of artificial intelligence, prediction-control problems are all the more tackled by function approximation models that learn how to control a specific task, even for systems with unmodeled/unknown dynamics and important uncertainties. Here, we show for the first time the possibility of controlling earthquake-like instabilities using state-of-the-art deep reinforcement learning techniques. The controller is trained using a reduced model of the physical system, i.e, the spring-slider model, which embodies the main dynamics of the physical problem for a given earthquake magnitude. Its robustness to unmodeled dynamics is explored through a parametric study. Our study is a first step towards minimizing seismicity in industrial projects (geothermal energy, hydrocarbons production, CO2 sequestration) while, in a second step for inspiring techniques for natural earthquakes control and prevention.