Coal and gas outburst strength prediction method based on deep learning

A gas salience and deep learning technology, applied in neural learning methods, prediction, biological neural network models, etc., can solve the problems of complex influencing factors, inability to truly reflect salient features and salient areas, and large limitations. Accuracy, excellent mapping effect, and strong expressiveness

Pending Publication Date: 2021-01-05
GUIZHOU UNIV OF ENG SCI
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AI Technical Summary

Problems solved by technology

Because the factors affecting coal and gas outburst are complex and have strong nonlinear characteristics, the above method is mai

Method used

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  • Coal and gas outburst strength prediction method based on deep learning
  • Coal and gas outburst strength prediction method based on deep learning
  • Coal and gas outburst strength prediction method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0025]Example 1

[0026]Taking 30 cases of outburst accidents in Panjiang mining area as samples for outburst strength prediction, the prediction of outburst strength is mainly achieved through the following links.

[0027](1) Select indicators and perform quantitative processing;

[0028]According to the comprehensive hypothesis theory of outburst, combined with gas geology and gas outburst prediction, 12 parameters were selected from three aspects of geological structure, coal structure and gas as the influencing factors of outburst accidents (Table 1).

[0029]Table 1 highlights the factors affecting the accident

[0030]

[0031]According to 30 cases of outstanding accidents, the indexes of the factors affecting the outstanding intensity were collected, and each predictive index was processed mathematically and non-dimensionally (Table 2).

[0032]Table 2 Training samples

[0033]

[0034]

[0035](2) Numerical processing of tags. Before the neural network reads the sample, the output is vectorized. Accordin...

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Abstract

The invention belongs to the technical field of gas exploitation, and particularly relates to a coal and gas outburst strength prediction method based on deep learning, which comprises the following steps: step 1, data preparation: selecting prediction indexes of coal and gas outburst, defining training data, and standardizing the data; 2, feature extraction: defining network or model compositionaccording to a data set, mapping input to a target, and extracting geological index features; 3, configuring a learning process, selecting a loss function, an optimizer and an index to be monitored, and setting the number of iterations; 4, training a model, inputting a fit method of a sample calling model to iterate on training data, and training and optimizing the model; and 5, verifying the model, predicting coal and gas outburst samples on the verification set, comparing the predicted coal and gas outburst samples with actual results, and determining the prediction precision of the model, so that the structure is reasonable, the expression ability is stronger, the mapping effect is better, and the outburst strength prediction accuracy can be further improved.

Description

technical field [0001] The invention relates to the technical field of gas mining, in particular to a method for predicting coal and gas outburst strength based on deep learning. Background technique [0002] Coal and rock dynamic disasters such as coal and gas outbursts, as well as accidents such as coal and gas explosions caused by them, have seriously restricted mine production and the improvement of economic benefits. In recent years, with the increasing intensity and depth of coal mine mining, the problem of coal and gas outburst has become more and more obvious. An effective outburst intensity prediction method is of great significance for outburst prevention and elimination. At present, the methods for predicting coal and gas outburst in my country mainly include qualitative comparative analysis, comprehensive evaluation method, electromagnetic radiation prediction, microseismic technology prediction and linear regression analysis. Because the factors affecting coal ...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06N3/04G06N3/08
CPCG06Q10/04G06N3/08G06N3/045
Inventor 关金锋周侃司中应邹福财聂子淇
Owner GUIZHOU UNIV OF ENG SCI
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