Semi-supervised resistivity inversion method and system based on adversarial generative network and pseudo labeling

A resistivity and semi-supervised technology, applied in the field of geophysical exploration, can solve the problems of not being able to obtain the corresponding labels of electrical data, not being able to satisfy supervised deep learning, and being difficult to obtain, and achieve the effect of not being able to obtain a large number of underground medium models

Pending Publication Date: 2021-01-08
SHANDONG UNIV +1
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Problems solved by technology

For electrical data, due to the difficulty of obtaining information on underground media, it is impossible to obt

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  • Semi-supervised resistivity inversion method and system based on adversarial generative network and pseudo labeling
  • Semi-supervised resistivity inversion method and system based on adversarial generative network and pseudo labeling
  • Semi-supervised resistivity inversion method and system based on adversarial generative network and pseudo labeling

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[0070] The present disclosure will be further described below in conjunction with the accompanying drawings and embodiments.

[0071] It should be noted that the following detailed description is exemplary and intended to provide further explanation of the present disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0072] It should be noted that the terminology used herein is only for describing specific embodiments, and is not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combinations thereof.

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Abstract

The invention provides a semi-supervised resistivity deep learning inversion method and a semi-supervised resistivity deep learning inversion system based on an adversarial generative network and pseudo-annotation, which can realize a deep learning inversion network under the condition that part of apparent resistivity data lacks a corresponding geological model; vertical features of the electrical method data are extracted, a resistivity model is finally obtained by extracting a feature map, and the mapping relation between the apparent resistivity data and the underground resistivity geologic model is completed; meanwhile, a discriminator is added into the network structure, pseudo labeling is introduced into apparent resistivity data without corresponding geological models, the data size of labeled data is expanded, and a semi-supervised learning strategy is achieved. Through the semi-supervised deep learning electrical method data inversion network, the deep learning network inversion effect is improved when the label data is less.

Description

technical field [0001] The disclosure belongs to the technical field of geophysical exploration, and relates to a semi-supervised resistivity inversion method and system based on confrontation generation network and pseudo-labeling. 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] The resistivity method is a common geophysical prospecting method. It is widely used in exploring coal mines and groundwater, solving related geological problems, etc., and has broad application prospects. The main principle of the resistivity method is based on the electrical difference between the rocks, two power supply electrodes A, B and two measurement electrodes M, N are driven into the ground, power is supplied to the ground through the power supply electrodes and a stable current field is established underground , when the resistivity value of the unde...

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Application Information

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IPC IPC(8): G06F30/27G06N3/04G06N3/08
CPCG06F30/27G06N3/08G06F2111/10G06N3/045
Inventor 蒋鹏汤宇婷刘本超王凯郭谦高雪池庞永昊许孝滨
Owner SHANDONG UNIV
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