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Seismic data random noise suppression method combining deep learning

A seismic data and random noise technology, applied in the field of earth science, can solve the problem of insufficient feature extraction of seismic data, and achieve the effect of improving generalization ability and convergence ability, and improving noise suppression effect.

Active Publication Date: 2020-07-03
NORTHEAST GASOLINEEUM UNIV
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AI Technical Summary

Problems solved by technology

Solve the problem of insufficient feature extraction of seismic data through the joint learning scheme, and improve the effect of noise suppression

Method used

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  • Seismic data random noise suppression method combining deep learning
  • Seismic data random noise suppression method combining deep learning
  • Seismic data random noise suppression method combining deep learning

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0034] refer to figure 1 and figure 2 , a seismic data random noise suppression method of combined deep learning, characterized in that: the suppression method comprises the following steps:

[0035] Step 1: Training dataset preprocessing:

[0036] In order to combine the characteristics of the wave atomic domain, the minimum unit of the training sample is set to several slice data x with a size of 256×256 cut from the seismic data samples in the training set. The random noise of the seismic data is passed through the zero mean normal distribution Gaussian random noise simulation, the standard deviation of the noise is positively correlated with the standard deviation of the original seismic data, the noise standard deviation is defined as:

[0037]

[0038] M is the total number of slice time samples, N is the total number of slice seismic trace samples, t is the time sampling sequence number, s is the seismic trace record sequence number, u is the mean value of seismic...

Embodiment 2

[0054] refer to Figure 3-8 The training of the joint learning network model of this method mainly includes training data preprocessing, preparing sample labels in the frequency domain and air domain, designing the network structure of the joint learning model, designing the air domain-frequency domain joint loss function, training and saving the network model, There are six parts to test network model performance. First, the training data is preprocessed to obtain the original seismic slice data and the slice data containing random noise; secondly, the sample labels in the frequency domain and the air domain are prepared; then the network structure of the joint learning model is designed, which consists of three parallel deep Convolution network, two of which are the prediction network of wave atomic domain coefficient matrix, and one is the prediction network of spatial domain seismic data; then design the space domain-frequency domain joint loss function; then the training ...

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Abstract

The invention belongs to the technical field of earth science, and particularly relates to a seismic data random noise suppression method combining deep learning, which comprises the following steps of: 1, preprocessing a training data set; 2, preparing a sample label; 3, designing a network structure of the joint learning model G; 4, designing a loss function; 5, training and storing a network model; and 6, testing the performance of the network model. Performing effective sparse representation on the wave-front texture features of the seismic data through wave atom transformation to obtain the texture features of the wave atom domain seismic data; and constructing a spatial domain and wave atom domain joint deep learning network structure by taking noise-containing seismic data as inputand wave atom domain data and features of actual noise-free data as labels. The problem of insufficient seismic data feature extraction is solved through a joint learning scheme, and the noise suppression effect is improved. Features of a spatial domain and a wave atom domain are combined, and seismic data random noise is removed by using a spatial domain and wave atom domain combined deep learning technology.

Description

Technical field: [0001] The invention belongs to the technical field of earth sciences, and in particular relates to a random noise suppression method of seismic data combined with deep learning. Background technique: [0002] In the information age with the rapid development of technologies such as big data and artificial intelligence, a number of novel and effective processing methods have emerged in various industries. The field of seismic data processing is also actively integrating new technologies to solve the problems of traditional methods. As the scope of oil and gas exploration continues to expand, harsh environments and complex geological structures will adversely affect the acquisition of seismic data. Random noise suppression is the basic work of seismic data processing, aiming to improve the signal-to-noise ratio and resolution of seismic data. In order to improve the speed and accuracy of subsequent seismic data processing and interpretation. [0003] The exi...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08G01V1/30
CPCG06N3/084G01V1/30G01V2210/32G06N3/045G06F2218/04G06F18/253G06F18/214
Inventor 张岩李新月王斌聂永丹唐国维赵建民李井辉
Owner NORTHEAST GASOLINEEUM UNIV