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.
翻译:地震是致命的、代价高昂的。本研究的目的是通过应用通过强化学习检索的注射政策避免这些灾难性事件。随着人工智能的迅速增长,预测控制问题更是由功能近似模型来解决的,这些功能近似模型学会如何控制具体的任务,即使是没有模型的/未知的动态和重要不确定性的系统也是如此。在这里,我们第一次展示了利用最先进的深层强化学习技术来控制类似地震的不稳定的可能性。控制器是使用一个简化的物理系统模型,即弹簧滑模型来培训的,该模型体现了特定地震程度的物理问题的主要动态。通过参数研究来探索其对非模型动态的强性。我们的研究是尽量减少工业项目(热能、碳氢化合物生产、CO2固存)中的地震性的第一步,而作为激励自然地震控制和预防技术的第二步。