Method for nesting and connecting residual network based on multi-branch selective kernel

A nested connection and selective technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve problems such as noise suppression of complex seismic data that cannot be adaptively processed, and achieve high computing efficiency and denoising performance. Improve computational efficiency and reduce the effect of residual connections

Active Publication Date: 2021-12-28
SOUTHWEST PETROLEUM UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to provide a method based on multi-branch selective kernel nested connection residual network, to solve the problem that traditional methods cannot adaptively deal with complex seismic data noise suppression

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  • Method for nesting and connecting residual network based on multi-branch selective kernel
  • Method for nesting and connecting residual network based on multi-branch selective kernel
  • Method for nesting and connecting residual network based on multi-branch selective kernel

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[0036] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0037] Such as Figure 1-5 As shown, a method based on multi-branch selective kernel nested connection residual network, it should be noted that the residual network is a network architecture proposed for the difficulty of deep convolutional neural network training, which can overcome the network depth increase The problem of network degradation caused by the problem, but the number of network layers is large, and the calculation efficiency needs to be improved; the residual network consists of a series of such as figure 2 The Residual Module (Residual Module, ResM) in b is composed of figure 2 The convolutional neural network module (ConvolutionNeural Network Module, CNNM) in a is based on the identity mapping (Identi...

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Abstract

The invention relates to the technical field of seismic data processing, and discloses a method for nesting and connecting a residual network based on a multi-branch selective kernel, which comprises the following processing steps of: nesting and connecting multiple layers of residual modules to obtain a residual nesting network; and adding a multi-branch selective kernel, taking the residual nested network as the input of the multi-branch selective kernel, performing feature extraction on each branch by using convolution of different scales, and calculating and outputting a final image. According to the method, nested residual connection is adopted, residual connection can be reduced, and the calculation efficiency is improved. The nested residual connection is combined with the multi-branch selective kernel, the feature map output by the nested connection residual network can be used as the input of the multi-branch selective kernel module, and the convolution kernels of different sizes are used for multi-branch fusion to obtain the feature map rich in content, so that the method adapts to complex data processing. The method has high operation efficiency and denoising performance, and can be widely applied to random noise processing of actual seismic data.

Description

technical field [0001] The invention relates to the technical field of seismic data processing, in particular to a multi-branch selective kernel nested connection residual network method. Background technique [0002] Random noise is formed by the comprehensive action of various factors, has no fixed frequency and propagation direction, and is distributed in all time and all frequency bands, so it is difficult to effectively separate it from seismic records. Traditional seismic data noise suppression methods such as wavelet transform, f-x domain filtering, curvelet transform, and Gaussian filtering are mainly based on the predictability and sparsity of seismic data, and their noise suppression effects are limited by factors such as model assumptions and parameter settings. Adaptively processing seismic data in complex areas, the denoising effect needs to be improved. [0003] In recent years, in view of the good performance of deep learning in computer vision and image proc...

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Application Information

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IPC IPC(8): G06K9/40G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/213G06F18/214
Inventor 曾梦张固澜罗一梁梁晨曦段景李勇詹熠宗杨志红
Owner SOUTHWEST PETROLEUM UNIV
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