Mine Fault Structure Prediction Method

A prediction method and fault technology, applied in the fields of instrumentation, calculation, electrical and digital data processing, etc., can solve the problem of not quantitatively predicting the development degree of small faults, and achieve the effects of reducing input dimensions, fast learning speed, and easy parameters.

Inactive Publication Date: 2019-09-06
SHANDONG UNIV OF SCI & TECH
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Problems solved by technology

However, this method only judges whether there are small structures in front of the working face, and does not quantitatively predict the development degree of small faults in the working face

Method used

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  • Mine Fault Structure Prediction Method
  • Mine Fault Structure Prediction Method
  • Mine Fault Structure Prediction Method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0084] Embodiment: The prediction object is a small fault in the 7th coal seam of a certain coal mine, and the prediction steps are as described above.

[0085] The specific prediction process and results are as follows:

[0086] (1) Determine and count the quantitative parameters of the sedimentary medium of the whole area (including the developed area and the undeveloped area) and the quantitative parameters of the small coal seam faults of each statistical unit in the developed area.

[0087] According to the difficulty of fault index statistics and the actual situation of the mine, the quantitative parameters of small coal seam faults in the 7th coal seam are used to select the density of small coal seam faults; on the mining engineering plan, the developed area is divided into 30 statistical units according to the grid of 300m×300m, Statistically calculate the coal seam small fault density in each unit in the mined area, and draw the thematic map ( Figure 4 ). The qu...

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Abstract

The present invention relates to a method for predicting mine fault structures, comprising: step 10, determining and counting the quantitative parameters of sedimentary medium in developed areas and undeveloped areas, and the quantitative parameters of small coal seam faults in each statistical unit in the developed areas; step 20, Use the gray correlation analysis method to screen the quantitative parameters of the sedimentary medium, so as to obtain the main controlling factors that play a decisive role in the development of small faults in the coal seam; step 30, use the principal component analysis method to perform dimensionality reduction processing on the main controlling factors to eliminate the main controlling factors Correlation between factors to obtain multiple principal components; step 40, use the data containing only the principal components as sample data, and use the Elman neural network to establish a quantitative prediction model for coal seam small faults; step 50, according to the prediction model, predict undeveloped Minor faults in coal seams in the region. The invention has the characteristics of low cost, easy selection of parameters, fast learning speed and high prediction accuracy.

Description

technical field [0001] The invention relates to the field of coal mines, in particular to a method for predicting mine fault structures. Background technique [0002] Small coal seam faults refer to small faults that only appear in coal seams (especially thick coal seams, medium-thick coal seams), roof or floor, and have a small plane distribution range and limited vertical distance. Minor faults in coal seams are one of the important geological factors affecting coal mine production, which have great influence and safety threats on roadway excavation and working face mining. In recent years, due to the lack of understanding of the formation structure and the development of small faults, the coal mining process has caused great economic losses and even caused accidents. Quantitative forecasting of coal seam small faults has important theoretical guiding significance and practical value. [0003] Due to the hidden and limited development of small faults in coal seams, they ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/50
Inventor 邱梅施龙青牛超翟培合
Owner SHANDONG UNIV OF SCI & TECH
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