A deep metal mine pressure disaster early warning system and method

By constructing a three-dimensional wave velocity model using high-sensitivity sensors and intelligent inversion algorithms in deep metal mines, and combining multi-level signal processing and deep learning, the problems of insufficient accuracy and early warning in existing microseismic monitoring systems have been solved. This has enabled high-precision microseismic signal identification and real-time early warning, ensuring safe production in deep mines.

CN122135522APending Publication Date: 2026-06-02JINCHUAN GROUP NICKEL COBALT CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
JINCHUAN GROUP NICKEL COBALT CO LTD
Filing Date
2026-02-10
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing microseismic monitoring systems in deep metal mines suffer from problems such as low accuracy in wave velocity model construction, severe noise interference, single early warning model, and low system integration, resulting in insufficient monitoring accuracy and early warning accuracy, making it difficult to meet the real-time monitoring and early warning needs of ground pressure disasters in deep mines.

Method used

A high-sensitivity 14Hz Geophones sensor network is used to establish a three-dimensional wave velocity model by combining field measurement data with intelligent inversion algorithms. Through signal processing modules, multi-level filtering, wavelet denoising, blind source separation, and deep learning classification are performed to construct a multi-parameter fusion early warning model, thereby achieving high-precision microseismic signal identification and real-time early warning.

Benefits of technology

It significantly improves the accuracy of microseismic source location and early warning rate, enhances the system's real-time response capability and operational reliability, and enables efficient and accurate monitoring and early warning of ground pressure disasters in complex geological environments.

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Abstract

This invention relates to an early warning system and method for ground pressure disasters in deep metal mining. The system combines high-sensitivity microseismic sensors, distributed acquisition units, signal processing and recognition technology, three-dimensional wave velocity model construction, data visualization, and a multi-parameter fusion early warning algorithm to achieve real-time, accurate monitoring and intelligent early warning of the entire rock mass fracturing process. The system hardware layer includes a 14Hz Geophones sensor network deployed in different deep sections, a netADC / netSP acquisition and processing module, and a UPS power supply system. The software layer includes modules for seismic source localization, feature extraction, deep learning classification, multi-parameter fusion model construction, and dynamic early warning output, supporting multi-platform operation and remote control. By constructing a three-dimensional wave velocity model, identifying microseismic signals in real time, and extracting multiple parameters such as energy, frequency, cumulative apparent volume, and Schmidt number, the system can achieve an early warning accuracy rate of ≥90%, significantly improving the ability to prevent and control ground pressure disasters in mines under complex geological conditions.
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