Hongyu Sun;Bo Yuan;Neal N. Xiong;Jiao Song;Wensi Ding;Qiang Liu
{"title":"MDRL-ETT:用于检测异常地质结构的多代理深度强化学习增强型传输断层摄影系统","authors":"Hongyu Sun;Bo Yuan;Neal N. Xiong;Jiao Song;Wensi Ding;Qiang Liu","doi":"10.1109/TSMC.2024.3417394","DOIUrl":null,"url":null,"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.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":null,"pages":null},"PeriodicalIF":8.6000,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MDRL-ETT: A Multiagent Deep Reinforcement Learning-Enhanced Transmission Tomography System to Detect Anomalous Geological Structures\",\"authors\":\"Hongyu Sun;Bo Yuan;Neal N. 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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.\",\"PeriodicalId\":48915,\"journal\":{\"name\":\"IEEE Transactions on Systems Man Cybernetics-Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.6000,\"publicationDate\":\"2024-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Systems Man Cybernetics-Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10601538/\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Systems Man Cybernetics-Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10601538/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
MDRL-ETT: A Multiagent Deep Reinforcement Learning-Enhanced Transmission Tomography System to Detect Anomalous Geological Structures
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.
期刊介绍:
The IEEE Transactions on Systems, Man, and Cybernetics: Systems encompasses the fields of systems engineering, covering issue formulation, analysis, and modeling throughout the systems engineering lifecycle phases. It addresses decision-making, issue interpretation, systems management, processes, and various methods such as optimization, modeling, and simulation in the development and deployment of large systems.