2021/04/27 by E. Papachristos, Ioannis Stefanou, Papachristos, Efthymios +1
Computer Science · Earth and Planetary Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Physical sciences #Geophysics (physics.geo-ph) #Seismology and Earthquake Studies #earthquake and tectonic studies
paper · pdf · doi:10.48550/arxiv.2104.13180
openalex publication_date 2021/04/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.