Microseismic signal analysis sensor arrangement method and system, and computer device

Through the Port-Hamiltonian neural network model and Min-Max MPC controller, the problems of low sensor layout efficiency and insufficient monitoring accuracy of existing microseismic monitoring systems are solved, and more efficient and reliable microseismic signal monitoring is achieved.

CN119670549APending Publication Date: 2025-03-21WUYANG COAL MINE OF SHANXI LUAN ENVIRONMENTAL ENERGY DEV CO LTD
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Patent Information

Application Number
CN202411732682.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-29
Publication Date
2025-03-21

AI Technical Summary

Technical Problem

The existing microseismic monitoring system has low sensor layout efficiency, insufficient monitoring accuracy, and fails to fully consider the dynamic characteristics and energy transfer characteristics of the system.

Method used

The Port-Hamiltonian neural network model is used to combine Hamilton quantity and dissipation terms to learn the dynamic characteristics of the microseismic monitoring system, and a data-driven Min-Max MPC controller is constructed based on the dynamic parameters to generate a sensor layout strategy, and finally calculate the optimal layout position of the sensor.

Benefits of technology

It improves the scientificity and monitoring accuracy of sensor layout, realizes multi-objective optimization of monitoring accuracy and resource consumption, and ensures the feasibility and reliability of the layout plan.

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Abstract

The invention discloses a microseismic signal analysis sensor arrangement method and system and a computer device. The method comprises the following steps of: constructing a Port-Hamiltonian neural network model containing a Hamilton quantity and a dissipation item, wherein the Port-Hamiltonian neural network model comprises the Hamilton quantity and the dissipation item; historical micro-seismic data of the micro-seismic monitoring area are collected and preprocessed; based on the neural network model, the geological parameters and the preprocessed historical micro-seismic data, learning the dynamic characteristics of each monitoring unit of the micro-seismic monitoring system, and outputting dynamic parameters such as the inherent frequency, the damping ratio and the modal shape of the system; constructing a data-driven Min-Max MPC controller based on the kinetic parameters, wherein the controller generates a sensor arrangement strategy according to the monitoring precision and the resource consumption; and calculating the optimal arrangement position of the sensor according to the arrangement strategy. The invention further provides a system for implementing the method. According to the method, the Port-Hamiltonian neural network model is introduced, and the Hamilton quantity and the dissipation characteristics of the system are combined, so that the dynamic characteristics of the micro-seismic monitoring system are accurately described.
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