Onset time automatic picking method of microseismic signal on the basis of time-recursive neural network

A recursive neural network, automatic picking technology, applied in neural learning methods, biological neural network models, seismic signal processing, etc.

Inactive Publication Date: 2017-02-15
CHINA UNIV OF MINING & TECH (BEIJING)
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that when the microseismic signal is picked up, the traditional short-long time window energy ratio method needs certain human intervention during the pi...

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  • Onset time automatic picking method of microseismic signal on the basis of time-recursive neural network
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  • Onset time automatic picking method of microseismic signal on the basis of time-recursive neural network

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Embodiment Construction

[0063] 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.

[0064] A time-recursive neural network-based microseismic signal arrival time picking method, the embodiment can be:

[0065] Step 1: Sampling the original data according to a fixed dimension, and the selected dimension is 1024.

[0066] Step 2: Manually pick up part of the data as the label information of the corresponding sample data. The specific method is: treat the microseismic event in each sample data as an effective signal, and the other part as noise. The label corresponding to the sample is a A binary vector with the same length as the sample data, the corresponding point of the effective signal part in the vector is set to 1, and the other parts are set to 0.

[0067] Step 3: Put the data and labels...

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Abstract

The invention discloses an onset time automatic picking method of a microseismic signal on the basis of a time-recursive neural network. Each microseismic record is sampled according to a uniform and fixed dimension; then, the onset time behaviors of parts of record are manually picked to serve as the label information of a corresponding record; the record of picked information and the label of the record are used as a total dataset during network construction, wherein the total dataset is divided into three parts: a training dataset, a verification dataset and a test dataset; the data is input into a deep belief neural network to be trained and tested, and the time-recursive neural network is constructed; and the data which is not subjected to onset time picking is input into a trained network model, and the network outputs the data as a sequence corresponding to input data, wherein a first point which is not zero in the sequence is the onset time point of microseismic data.

Description

technical field [0001] The invention belongs to the technical field of geophysical detection, and relates to a time-recursive neural network-based method for automatically picking up microseismic signals when they arrive. Background technique [0002] With the continuous development of electronic technology and computer technology, automatic real-time detection and positioning of microseismic events have been successfully applied in various engineering applications, such as hydraulic fracturing to exploit oil and shale gas, mine dynamic disaster monitoring, and deep rock mass excavation Unloading disturbance warning forecast, etc. [0003] The timely automatic picking of microseismic signals generated by rock fracture events is a prerequisite to ensure the accuracy and efficiency of positioning results. Whether the picking results are accurate or not directly affects the accuracy of the final results, and its processing speed directly affects the overall work. efficiency. ...

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Application Information

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IPC IPC(8): G06F19/00G06N3/08G01V1/28
CPCG01V1/288G06N3/08G06N3/084G16Z99/00
Inventor 郑晶陆继任彭苏萍
Owner CHINA UNIV OF MINING & TECH (BEIJING)
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