Optimizing microseismic sensor networks in underground space using Cramér-Rao Lower Bound and improved genetic encoding

Abstract

The layout of a sensor network is a critical determinant of the precision and reliability of microseismic source localization. Addressing the impact of sensor network configuration on positioning accuracy, this paper introduces an innovative approach to sensor network optimization in underground space. It utilizes the Cramér-Rao Lower Bound principle to formulate an optimization function for the sensor network layout, followed by the deployment of an enhanced genetic encoding to solve this function and determine the optimal layout. The efficacy of proposed method is rigorously tested through simulation experiments and pencil-lead break experiments, substantiating its superiority. Its practical utility is further demonstrated through its application in a mining process within underground spaces, where the optimized sensor network solved by the proposed method achieves remarkable localization accuracy of 15 m with an accuracy rate of 4.22% in on-site blasting experiments. Moreover, the study elucidates general principles for sensor network layout that can inform the strategic placement of sensors in standard monitoring systems.

Keywords

Underground space / Sensor network optimization / Microseismic monitoring / Cramér-Rao Lower Bound / Improved genetic encoding

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

CRediT authorship contribution statement

Yichao Rui: Writing - review & editing, Writing - original draft, Resources, Methodology, Conceptualization. Jie Chen: Writing - review & editing, Supervision, Resources, Conceptualization. Junsheng Du: Writing - review & editing, Validation, Data curation. Xiang Peng: Funding acquisition. Zelin Zhou: Validation, Supervision, Resources, Data curation. Chun Zhu: Supervision, Formal analysis.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

All authors would like to acknowledge the financial support provided by the National Natural Science Foundation of China (Grant No. 52304123), Postdoctoral Fellowship Program of China Postdoctoral Science Foundation (Grant No. GZB20230914), the 10th Young Talent Lifting Project of the China Association for Science and Technology (No. 2024QNRC001), China Postdoctoral Science Foundation (Grant No. 2023M730412), Sichuan-Chongqing Science and Technology Innovation Cooperation Program Project (No. CSTB2024TIAD-CYKJCXX0016), and National Key Research and Development Program for Young Scientists (Grant No. 2021YFC2900400).

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