Locating microseismic sources in time is a challenging problem in microseismic monitoring. In order to improve the accuracy and efficiency of locating sources, this article presents a method for locating microseismic sources using deep reinforcement learning (RL). We first construct and train a convolutional autoencoder to preprocess the seismic records in the microseismic waveform database. Then, the problem of locating the source is described as a Markov decision process for the application of deep RL. We decompose the task of locating the source into three subtasks and design the critical elements of deep RL. Three agents independently learn optimal policies for their respective subtasks in the framework of a deep Q-network (DQN) and jointly determine the precise location of the microseismic source. Finally, we evaluate the proposed method using synthetic data generated from the Marmousi model and the 3-D velocity model. The experimental results indicate that the proposed method can locate microseismic sources efficiently and accurately.

中文翻译：

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使用深度强化学习的微震源定位

及时定位微震源是微震监测中的一个具有挑战性的问题。为了提高定位震源的准确性和效率，本文提出了一种利用深度强化学习（RL）定位微震震源的方法。我们首先构建和训练一个卷积自动编码器来预处理微震波形数据库中的地震记录。然后，将源定位问题描述为深度强化学习应用的马尔可夫决策过程。我们将定位源的任务分解为三个子任务，并设计深度强化学习的关键要素。三个智能体在深度 Q 网络 (DQN) 框架内独立学习各自子任务的最优策略，并共同确定微震源的精确位置。最后，我们使用从 Marmousi 模型和 3-D 速度模型生成的合成数据来评估所提出的方法。实验结果表明，该方法可以高效、准确地定位微震源。