Rayleigh wave seismic data noise removal method, storage medium and electronic equipment

Abstract

The invention discloses a Rayleigh wave seismic data noise removal method. The method comprises the following steps: constructing a deep convolutional generative adversarial network; preprocessing the Rayleigh wave seismic data to obtain real noise-free data; adding Gaussian white noise based on the noise-free data to obtain noise-containing data; adding real noise-free data and noise-containing data as training samples into the deep convolutional generative adversarial network for training, and performing iterative calculation to obtain a deep convolutional generative adversarial network model; and carrying out de-noising processing on other Rayleigh wave seismic data to be de-noised. According to the method, random noise removal is carried out on the multi-channel surface wave data based on the deep convolutional generative adversarial network in the deep learning field, high-quality denoised Rayleigh wave seismic data is obtained, manual parameter adjustment is not needed, the labor cost is reduced, the Rayleigh wave seismic data denoising efficiency is improved, and the denoising effect is better.

Description

technical field [0001] The invention relates to the technical field of engineering geophysical exploration, in particular to a method for removing noise from Rayleigh wave seismic data, a storage medium and electronic equipment. Background technique [0002] The Rayleigh wave is an elastic wave that propagates along the free surface, and its amplitude decays exponentially with depth. The multi-channel surface wave analysis method (MASW) obtains the shallow surface shear wave velocity structure by inverting the Rayleigh wave phase velocity, which has been widely used. for a range of shallow surface geophysical and geological problems. [0003] Rayleigh surface wave exploration is a geophysical exploration method that has emerged in recent years, especially in engineering geophysical prospecting. The shear wave velocity of the shallow surface subsurface medium can be accurately obtained by inverting the surface wave dispersion curve, so as to obtain the shallow surface The me...

AI Technical Summary

Problems solved by technology

[0013] The present invention provides a method for removing noise from Rayleigh wave seismic data based on Deep Convolutional Generative Adversarial Network (DCGAN), which solves the technical problems of limitations in the application of the prior art and the need for manual adjustment of parameters, and can detect multi-channel surface waves The data is subjected to random noise removal, and the denoising results are more reliable

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