Automatic microseismic signal arrival time picking method based on depth belief neural network

A deep belief network and neural network technology, applied in the field of automatic picking of microseismic signals when they arrive, can solve problems such as insufficient robustness of the picking method

Inactive Publication Date: 2017-02-15
CHINA UNIV OF MINING & TECH (BEIJING)
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Problems solved by technology

[0006] The present invention aims to solve the problem that the traditional short-long time window energy ratio method needs certain human intervention dur

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  • Automatic microseismic signal arrival time picking method based on depth belief neural network
  • Automatic microseismic signal arrival time picking method based on depth belief neural network
  • Automatic microseismic signal arrival time picking method based on depth belief neural network

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Embodiment Construction

[0048] The principle of the present invention will be described below in conjunction with specific method implementation processes, and the examples given are only used to explain the present invention, and are not intended to limit the scope of the present invention.

[0049] A method for picking up the arrival time of microseismic signals based on deep belief neural network, the embodiment can be:

[0050] Step 1: Sampling the original data according to a fixed dimension, and the selected dimension is 1024.

[0051] Step 2: Manually pick up part of the data as the label information of the corresponding sample data.

[0052] Step 3: Put the data and labels into the data set, and generate new sample data by adding Gaussian noise, and make the number of samples corresponding to each type of label consistent.

[0053] Step 4: The total data set is 300,000 samples, and each data sample contains 1024 data features and 1 corresponding wave arrival time label; the data set is divid...

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Abstract

The invention discloses an automatic microseismic signal arrival time picking method based on a depth belief neural network. According to the method, each microseismic record is sampled according unified fixed dimensions, the signal arrival time of partial records is manually picked and is taken as the label information of the corresponding records; the information-picked records and labels of the information-picked records are taken as a total data set during network construction, including a training data set, a verification data set and a test data set; through inputting the data to the depth belief neural network for training and testing, the depth belief neural network is constructed; the actually-acquired to-be-processed data is inputted to the trained network model to carry out microseismic signal identification and automatic arrival time picking, and the network output is an arrival time point of the microseismic data.

Description

technical field [0001] The invention belongs to the technical field of geophysical detection, and relates to a method for automatically picking up microseismic signals when they arrive, based on a deep belief neural network. Background technique [0002] Microseisms are earthquakes whose magnitude is greater than one and less than three. Such small magnitude earthquakes are difficult for people to detect and can only be monitored with instruments. But even a small underground vibration will generate corresponding excitation to the underground medium, and such excitation may change the mechanical state of the underground medium. In the field of mine safety monitoring, microseisms are a precursor to mine dynamic disasters, and real-time monitoring of microseisms can effectively predict and prevent dynamic disasters. In addition, in the exploration and development of unconventional oil and gas such as shale gas and coalbed methane, the fracture monitoring technology based on m...

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Application Information

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IPC IPC(8): G01V1/28G06K9/62G06N3/08
CPCG06N3/084G01V1/288G06F18/2415
Inventor 郑晶陆继任彭苏萍
Owner CHINA UNIV OF MINING & TECH (BEIJING)
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