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NOISE SUPPRESSION METHOD FOR EXTRACTING TARGET FREQUENCY SIGNAL FROM CSAMT TIME SERIES

发明专利审中
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9权利要求 · 1 独立
§ Ⅰ

卷宗概要

发明人

Zhiguo An; Bingcheng Xu; Ying Han; Gaofeng Ye

IPC 分类

G1V 3/8

CPC 分类

G1V3/83G1V2003/86

A noise suppression method for extracting a target frequency signal from a CSAMT time series, and aims at solving the problems that an existing CSAMT data denoising method is long in denoising time, small in application range and poor in robustness. The method includes the following steps: acquiring CSAMT data to be subjected to noise suppression as input data, the CSAMT data being the CSAMT data with noise; preprocessing the input data to obtain preprocessed data; performing noise suppression on the preprocessed data through a trained denoising network to obtain noise-suppressed CSAMT data; wherein the denoising network is constructed based on an improved temporal convolutional network, a bidirectional long short-term memory network and a fully connected layer which are connected in sequence. According to the method, the denoising time of the CSAMT data is shortened, the application range is expanded, and the robustness is improved.

原文(中文)

A noise suppression method for extracting a target frequency signal from a CSAMT time series, and aims at solving the problems that an existing CSAMT data denoising method is long in denoising time, small in application range and poor in robustness. The method includes the following steps: acquiring CSAMT data to be subjected to noise suppression as input data, the CSAMT data being the CSAMT data with noise; preprocessing the input data to obtain preprocessed data; performing noise suppression on the preprocessed data through a trained denoising network to obtain noise-suppressed CSAMT data; wherein the denoising network is constructed based on an improved temporal convolutional network, a bidirectional long short-term memory network and a fully connected layer which are connected in sequence. According to the method, the denoising time of the CSAMT data is shortened, the application range is expanded, and the robustness is improved.