Semi-supervised deep learning seismic data inversion method based on wave equation driving

Abstract

The invention provides a semi-supervised deep learning seismic data inversion method based on wave equation driving, which can realize a deep learning inversion network under the condition that part of seismic data lacks a corresponding geological model can be realized. The method comprises the steps of firstly, adopting a channel convolution-full connection network for pre-stack seismic data features to enhance seismic data, finally obtaining a geological wave velocity model by extracting a feature map, and completing the mapping relation between the seismic data and an underground multi-layer medium model; and adding a wave equation into a network structure at the same time, for seismic data without a corresponding geological model, replacing a wave velocity loss function by a data lossfunction, introducing a physical law, and realizing a semi-supervised learning strategy. Through the semi-supervised deep learning seismic data inversion network, the deep learning network inversion effect is improved when the labeled data is less.

Description

technical field [0001] The disclosure belongs to the field of geophysical exploration, and relates to a semi-supervised deep learning seismic data inversion method driven by wave equations. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Seismic method, as one of the most commonly used geophysical exploration methods, is widely used in petroleum exploration, coal field, and metal deposit detection, etc., and has broad application prospects. The main principle of the seismic method is based on wave field propagation. Multiple geophones are arranged on the surface, and the wave field is generated by stimulating the artificial seismic source multiple times and propagates in the underground medium. When the wave impedance of the underground medium changes, reflection or refraction returns to the ground. The geophones located on the ground re...

AI Technical Summary

Problems solved by technology

However, for seismic data, due to the difficulty in obtaining the information of the underground medium, the corresponding labels of all seismic data cannot be obtained, and the conditions for supervised deep learning cannot be met.

In addition, this data-driven algorithm lacks the support of physical meaning, and the generalization of the method is limited

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