MDRL-ETT: A Multiagent Deep Reinforcement Learning-Enhanced Transmission Tomography System to Detect Anomalous Geological Structures

Abstract

—In this article, a novel system based on the simultaneous iterative reconstructive technique (SIRT) and multiagent deep reinforcement learning is proposed for detection of anomalous geological structures in coal mines. The system employs the SIRT optimization inversion method to construct a computational model for channel wave signal imaging. Then, the back projection technique (BPT) was introduced to the system. By utilizing the BPT algorithm to provide initial values for the SIRT, the channel wave signals can be prescreened, improving the ability of the SIRT algorithm to suppress model noise and enhancing its resolution. Furthermore, we employ multiagent reinforcement learning method for image feature classification of anomalous geological structures. Moreover, we conduct two-dimensional and three-dimensional imaging of four types of changes and energy fluctuations. The results demonstrate a high degree of concordance between the computed channel wave results and the slowness of the measured channel wave signals. Experimental findings validate the exceptional computational accuracy of this novel system, with relative errors and coefficient of deviation both within 1%, surpassing traditional SIRT inversion methods, damped least-squares methods, conjugate gradient methods, and classical algebraic reconstruction methods. These discoveries demonstrate the feasibility and superiority of utilizing transmission tomography imaging technology for the detection of anomalous structures in coal seams, offering new perspectives for underground exploration in coal mines.