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Fault identification method based on artificial intelligence

A fault identification and artificial intelligence technology, applied in the field of seismic exploration data processing, can solve problems such as large computational load and slow computational speed

Pending Publication Date: 2021-06-04
李辉
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AI Technical Summary

Problems solved by technology

Identifying faults through variance body technology minimizes the influence of human factors or other external factors on fault identification and further improves the accuracy of fault identification. slightly slower

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  • Fault identification method based on artificial intelligence
  • Fault identification method based on artificial intelligence
  • Fault identification method based on artificial intelligence

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Embodiment Construction

[0071] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts fall within the protection scope of the present invention.

[0072] The embodiment of the present invention discloses an artificial intelligence fault identification method, comprising the following steps:

[0073]Input training data, the quality of the neural network training results depends largely on the training data, considering that there may be other geological features in the actual data that affect the recognition of faults by the neural network. At the same time, relying on human subjective judgment in the p...

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Abstract

The invention discloses a fault identification method based on artificial intelligence, and aims to solve the problem of how to accurately identify a fault from seismic data and improve calculation efficiency. Through a series of improvements on a traditional AlexNet neural network, including replacing original local response normalization (LRN) with batch normalization (BN), the training speed is increased. Fault recognition is regarded as a dichotomy problem, and Sigmoid is used for replacing Softmax to serve as a classification function. Meanwhile, in the fault identification problem, the proportion of positive and negative samples (fault and non-fault) is seriously unbalanced, so that a balanced cross entropy loss function is introduced on the basis of a dichotomy cross entropy loss function to solve the problem. Finally, a 1 * 1 convolutional layer is used for replacing three full-connection layers of a traditional AlexNet network, so that the number of parameters of a network structure is greatly reduced. Through analysis of prediction results of theoretical data and actual data, it can be obtained that a model trained through the improved AlexNet network has good performance in the field of fault recognition.

Description

technical field [0001] The invention relates to the technical field of seismic exploration data processing, and more specifically relates to an artificial intelligence convolutional neural network automatic identification method for seismic data faults. Background technique [0002] Fault identification plays a vital role in the process of seismic exploration interpretation. Fault is a common geological phenomenon formed by the internal movement of the earth's crust. It refers to the phenomenon of relative displacement of the bottom layer along the rupture surface. It is an associated product of tectonic movement displacement and is closely related to the formation, distribution and enrichment of oil and gas reservoirs. contact. To explain the fault, the first problem is to identify it on the seismic section. According to practice, the fault mainly has the following signs on the seismic section: ① The reflected wave event is dislocated. Due to the different scales and leve...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08G01V1/28G01V1/30
CPCG06N3/08G01V1/282G01V1/301G01V1/307G06N3/048G06N3/045G06F2218/08G06F2218/12G06F18/2415G06F18/241
Inventor 李辉
Owner 李辉
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