RBM-based seismic prestack signal clustering method

A signal clustering and pre-data technology, applied in seismic signal processing, etc., can solve problems such as inadaptability and difficulty in interpretation of clustering algorithms, and achieve good abstract feature extraction capabilities, stable clustering results, and rich and reasonable information

Active Publication Date: 2018-02-13
UNIV OF ELECTRONIC SCI & TECH OF CHINA
View PDF8 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In clustering algorithms, it is often necessary to combine feature dimensionality reduction algori

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • RBM-based seismic prestack signal clustering method
  • RBM-based seismic prestack signal clustering method
  • RBM-based seismic prestack signal clustering method

Examples

Experimental program
Comparison scheme
Effect test

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...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses an RBM-based seismic prestack signal clustering method. Aiming at the characteristic of high dimensionality of seismic prestack signals, a dimension reduction method based on adepth limited Boltzmann machine network is introduced, a depth limited Boltzmann machine has relatively good abstraction feature extraction ability, and the output after dimension reduction can wellexpress an original signal. Aiming at the characteristic that the seismic signal is often noisy and hard to remove well, a fuzzy self-organizing neural network clustering algorithm is introduced, so that the seismic phase classification is expressed by membership degree; the information provided by the fuzzy classification result is more abundant and reasonable, so that the clustering result is more stable, and the classification result can be further explored. The method of the invention has lower computational complexity and can be popularized in large-scale seismic prestack 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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G01V1/36
CPCG01V1/36
Inventor 钱峰尹淼张乐胡光岷
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap