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Fault plane recognition method based on full convolution neural network

A convolutional neural network and identification method technology, applied in the field of artificial intelligence and machine learning, can solve problems such as ignoring geological meanings, achieve accurate fault segmentation and identification, avoid loss of spatial information, and quickly identify faults

Active Publication Date: 2018-12-25
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

However, the results of the above methods depend on the artificially selected seismic data attributes and attribute calculation methods, and the geological meaning in the original seismic amplitude volume data is also ignored in the fusion process

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  • Fault plane recognition method based on full convolution neural network
  • Fault plane recognition method based on full convolution neural network
  • Fault plane recognition method based on full convolution neural network

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[0036] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0037] Such as figure 1 As shown, it is a schematic flow chart of the fault plane identification method based on the fully convolutional neural network of the present invention. A fault plane identification method based on a fully convolutional neural network, characterized in that it comprises the following steps:

[0038] A. Obtain the marked seismic amplitude fault data and the seismic amplitude fault data to be identified from the three-dimensional seismic amplitude data volume;

[0039] B. Construct a fully convolutional neural network model;

[0040] C, using the seismic amplitude fault da...

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Abstract

The invention discloses a fault plane identification method based on a full convolution neural network, which comprises acquiring seismic amplitude fault data, constructing a full convolution neural network model, training a full convolution neural network training model, and identifying seismic amplitude fault data. The present invention extends from fault classification identification at the level of seismic amplitude images to classification at the level of pixels, using full convolution neural network to segment 3D seismic amplitude data can not only realize fast fault recognition, but also avoid the loss of spatial information by using full convolution instead of full connection layer, so as to obtain more accurate fault segmentation and recognition.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence and machine learning, and in particular relates to a fault plane recognition method based on a fully convolutional neural network. Background technique [0002] Fault interpretation is the basis and key of seismic structure interpretation, which can directly affect the efficiency and benefit of oil and gas exploration and production. Due to the various shapes of the fault itself, the discrete and complex structure, uncertainty and dispersion, coupled with the interference of seismic noise, horizon fissures and special formation occurrences, the difficulty of fault identification is further amplified. Nowadays, the exploration and mining cycle is gradually shortened, and the requirements for fine interpretation are increasing. How to improve the efficiency and accuracy of fault interpretation is a research hotspot and a major challenge in geological exploration. [0003] With the d...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/34G06N3/04G06F17/30
CPCG06V10/267G06N3/045
Inventor 姚兴苗黄浪胡光岷
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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