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Clustering Method of Seismic Prestack Signals Based on RBM

A signal clustering and azimuth technology, applied in seismic signal processing and other directions, can solve problems such as inadaptability of clustering algorithms and difficulty in interpretation, and achieve good abstract feature extraction capabilities, stable clustering results, and rich and reasonable information

Active Publication Date: 2019-12-31
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0005] In clustering algorithms, it is often necessary to combine feature dimensionality reduction algorithms, because high-dimensional data will make clustering algorithms unsuitable and difficult to interpret.

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  • Clustering Method of Seismic Prestack Signals Based on RBM
  • Clustering Method of Seismic Prestack Signals Based on RBM
  • Clustering Method of Seismic Prestack Signals Based on RBM

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Embodiment Construction

[0021] Embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0022] The process flow of the seismic prestack signal clustering method in the embodiment of the present invention is as follows figure 1 As shown, it specifically includes the following steps:

[0023] Step S1. The structure-oriented filtering algorithm removes noise, preserves structural features, and organizes the data of the target horizon segment into a format required by subsequent algorithms. Here, the structure-oriented filtering algorithm is usually used to remove noise, which can minimize the impact of noise on subsequent processing;

[0024] Each position of the prestack signal has multiple azimuths, that is, if a single azimuth data dimension is d, for seismic data with s azimuths in total, the characteristic data dimension of each position is s·d. Before performing the clustering algorithm, it is necessary to use the structure-guided filt...

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Abstract

The invention discloses a method for clustering seismic pre-stack signals based on RBM. The clustering method of the invention aims at the high-dimensional characteristics of seismic pre-stack signals, and introduces a dimensionality reduction method based on a depth-restricted Boltzmann machine network. Restricted Boltzmann machine has good abstract feature extraction ability, and the output after dimensionality reduction can well express the original signal; in view of the characteristics that seismic signals are often noisy and difficult to remove, a fuzzy self-organizing neural network clustering algorithm is introduced , so that the classification of seismic facies is expressed by the degree of membership, the information provided by the fuzzy classification results is more abundant and reasonable, the clustering results are more stable, and the classification results can be further explored. The method of the invention has relatively low computational complexity and can be extended to large-scale seismic pre-stack data.

Description

technical field [0001] The invention belongs to the technical field of earthquakes, and relates to seismic signal classification technology, in particular to a seismic pre-stack signal clustering method. Background technique [0002] The generation of seismic phase maps by seismic signal classification technology to determine underground reservoirs plays an important role in guiding oil exploration. The existing seismic signal classification technology is mainly aimed at post-stack signals. Post-stack signals are the horizontal summation of pre-stack signals, resulting in a lot of seismic information lost. Compared with the post-stack signal, the notable feature of the pre-stack signal is that the dimension becomes higher. Directly using the existing method will bring the disaster of dimensionality, and the classification algorithm is not suitable. At the same time, the amount of data becomes large, and algorithms with high computational complexity cannot be used. After ob...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01V1/36
CPCG01V1/36
Inventor 钱峰尹淼张乐胡光岷
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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