Research on Microseismic Source Locations Based on Deep Reinforcement Learning

The technology used to locate the sources of shallow underground seismic activity is mainly used to assess underground damage, the positioning of bombs in ordnance test fields, and the positioning of charges in engineering blasting. Due to sensor acquisition clock errors and the lack of generalizability of vibration data, the current sourced location methods cannot easily meet the requirements of precise positioning and system reaction time in assessing near-field microseismic activity. Based on the seismic wave analysis technique and the deep reinforcement learning method, this paper proposes a dynamic model for locating shallow underground seismic sources. The model allows the generalization of low-dimensional vibration waves information to high dimensions through spatial scanning. The process of source detection is treated as a Markov process. The correspondence between the center of a source and a high-dimensional energy distribution is established, and training is then gradually performed using the deep reinforcement learning method. The optimization of the center of the source to accurately determine the position of the focal center is provisionally called the reinforcement learning source scanning algorithm (RL-SSA). In addition, a small site-based static explosion test shows that the positioning method can greatly improve the positioning accuracy (<1 m) in the location of near-field microseismic sources.