The Intelligent Layered Noise Filtering Method for Microseismic Signals of Rock Mass Fracture in Deep-Buried Tunnels

A wavelet noise filtering method based on intelligent layered is proposed to solve the problem of microseismic signal processing of rock mass fracture in deep-buried tunnels. With the deepening of tunnel engineering, the microseismic monitoring system plays a vital role in real-time monitoring of the safety of deep engineering. However, due to the interference of electrical signals and mechanical vibration in the complex construction environment, the traditional wavelet transform method relies on experience in the selection of the number of decomposition layers, which has the problems of strong subjectivity and poor generalization ability, and cannot adapt to the diversified signal characteristics. Therefore, this paper proposes an intelligent layered noise filtering technology by automatically judging the number of decomposition layers, which can dynamically select the optimal number of decomposition layers according to the characteristics of the signal and the processing target, to improve the accuracy and efficiency of signal analysis. Experiments show that the proposed method can effectively retain useful information, eliminate noise, and improve the accuracy of rockburst early warning and risk assessment in the purification of microseismic signals and rock mass fracture feature extraction in Y tunnels. This method is not only suitable for this project, but also provides an important technical reference for rockburst monitoring in deep tunnels under similar complex geological conditions.