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Statistics and Prediction of Destruction Indexes of Factors Effecting Geological Condition in Unquarried Zone

Wu Liangcai1,2 Wei Zhiming 2 (1 Department of Surveying, China University of Mining And Technology, Beijing 100083, China; 2 Department of Surveying, East China Institute of Technology, Fuzhou 344000, Jiangxi, China )

Abstract:

It is required to determine destruction indexes of influencing factor for appraisal of geological conditions. According to historic documents about mining at coal mines and a great deal of statistics and analysis, this paper reveals the relations between exposure information surrounding tunnel of working place and actual information on the operational area and establishs a model to predict destruction indexes such as face fault, fold and thin seam etc. The effection of this model is marked approved by many examples.

Key words:

geological conditions; destruction indexes; prediction model

1 Forword

The overall merit of geological mining condition in the working face is the first job in the mine business management and important link in improving economic performance and social performance. The appraisal of geological condition is the base of production-index prediction. Its quality directly affects forecasts and decisional results. On evaluating the geological conditions in the exploitation face, the first step is determining the geologic factor quantized value. After the operational area determined, author analyses home and discovers that most geological factors can be confirmed straightly and less discrepancy compared to the picked actual value excluding some, such as fault, fold which more complex and then it’s difficulty to identify destruction indexes. This paper, on the basis of an abundance of historical materials on the mine producing and the relations between exposure information surrounding tunnel of working place and actual information on the operational area, builds a model to predict destruction indexes of fault and fold.

2 Statistics and Prediction of fault destruction indexes

Practices make that fault destruction indexes exposured surrounding tunnel of working place is proportional to inside working face. There are several kinds of fault destruction indexes surrounding tunnel of working place as follows:
1 (1)

2 (2)

3 (3)
n' :The number of faults in tunnel; L:The length of working face tunnel; hi :Fault fall; mi :Coal horizon thickness.

In order to studing the relation between the tunnel fault index and the working place fault index, we use twenty-five samples of working spot materials in mining bureau Pingdingshan regression to carry on regression adopting with the power function, the exponential function, the converse index, the logarithmic function, the line shape function. Table 1 shows the most superior result.

Table 1 The result of regression analysis about fault index of tunnel and working place

Working place fault index

Tunnel fault index

Adopting-Predicted tunnel fault index

5
(r=0.558)
4 6 8
(r=0.8661)
7
(r=0.8378)
Formula(2) can predicts fault destruction indexes inside working place owing to y0,01(1,24)=0,5168 ,regression equation significantly correlations to height in Table 1, and Kg2 keeps the closest to the index K in fault. Utilizing this model to practically precdict the fault destruction indexes in the three working face and the average relatively error is less, following the result in table 2.
Table 2 Regression Model of fault index and its predicted result

The rule of regression analysis

Regression equation

Relative predicted error(three surface averages)

Least square 9 43.12%
Robust Regression
No picking out 10 38.31%
Picking out 1 11 34.39%
Picking out 4 12 29.32%
Picking out 7 13 22.17%

3 Statistics and Prediction of destruction indexes in thin seam

The form of destruction index in the sector low coal seam is simple, namely 14, s' is area of the thin seam, s is the working surface area. Exposing tunnels, measuring the tunnel length i through the thin coal belt, tunnel total length L, supposing Kc=i/L , 15 , and then We may obtain the true destruction index C in the thin seam, building the statistic relation between C and Kc or C and K'c. We have collected twenty-five samples working surfaces material in the Pingdingshan mining bureau, doing a statistics and regressions forecast separately on C - Kc and C - K'c of their destruction indexes in this sector, the best answer lists in Table 3 and the actual prediction result in Table 4.
Table 3 Thin coal belt index return model and its forecast result (Kc=i/L)

The rule of regression analysis

Regression equation

Relative predicted error(five surface averages)

Least square 16 9.36%
Robust Regression
No picking out 17 9.00%
Picking out 3 18 9.25%
Picking out 4 19 9.05%
Picking out 6 20 9.61%
Table 4 Thin coal belt index return model and its forecast result

The rule of regression analysis

Regression equation

Relative predicted error(five surface averages)

Least square C=0.1189+0.9278K'c; r=0.8059 12.32%
Robust Regression
No picking out C=0.1131+0.9936K'c; r=0.8482 12.02%
Picking out 1 C=0.1073+1.0911K'c; r=0.8906 11.85%
Picking out 3 C=0.1068+1.0938K'c; r=0.9192 11.82%
From Table 3 and Table 4, the destruction index of sector seam can be approximately to the result with good virtue, which predicted by the seam information exposing arrounding sector tunnel, Using Kc=i/L , then the best.

4 Fold destruction index statistical forecast

The working surface fold destruction index form is simple, its formula is:
21 (4)
In the formula:C:Fold destruction index; S'i :In working surface ith fold area; S:Working surface area; n:In working surface fold integer.

After around the working surface the tunnel digs, has the roof elevation change situation according to the periphery tunnel, may directly select to the partial folds, to fold which vanishes in the working surface, may measure results in tunnel length L which the fold passed through, estimates the fold highly h, according to the above establishes S' and L and the h statistical relations, then extracts fold destruction index C.

According to the Da Zhuang mine sixteen working surfaces samples material, obtains S' and L and the h most superior statistical relations see Table 5 and Table 6.
Table 5 Fold area return model and its forecast result (Dual)

The rule of regression analysis

Regression equation

Relative predicted error(four surface averages)

Least square S' =-2207.198+71.428L+79.638h r=0.768 36.07%
Robust Regression
No picking out S' =-2306.141+70.924L+86.969h r=0.77 35.29%
Picking out 2 S' =-6709.664+70.068L+237.477h r=0.768 26.77%
Picking out 3 S' =-10776.3+77.478L+307.437h r=0.768 28.80%
Table 6 The fold area harnesses turns over to the model and its the forecast result

The rule of regression analysis

The least square Regression equation

Relative predicted error(four surface averages)

x=L S' =-812.622+70.463x; r=0.768 37.03%
x=Lh S' =10497+2.570x; r=0.771 28.70%
x=L S' =10370.52+7.394?10x; r=0.683 24.95%
x=L S' =1221.295+118.128x r=0.768 25.29%
By table three and table four obviously, S' and L and the h relativity is higher, according to the correlation y coefficient gamma value size, may result in the fold area the statistical forecast model is:

S'=-10776.3+77.478L+307.437h (5)

S'=10497.52+2.57(Lh) (6)

5 Conclusion

Not yet has mined the working surface geologic structure forecast is the very many mining survey personnel and the geologist for many years continuously in the exploration question. This article in the massive mines mining history material foundation, after repeatedly analyzes the research, chooses each mathematical model, after the regression analysis, has established the working surface fault and the fold destruction index forecast model. After the massive examples confirmation, this forecast, the model has a higher precision. This model establishment regarding further studies the working surface mining condition appraisal, the classification and the production target forecast has the important value, regarding the science formulation productive plan, the realization simulates the market and simulates the legal person to manage, the evolution enterprise reform, reasonably uses the coal resources, reduces the coal production cost, enhances the economic efficiency and so on to have the extremely vital significance.

References

[1] Wang Yunjia, Jiao Baowen. Optimum Form of Working Face Fault Damaged Coefficent and Its Statiaycal Prediction. Coal Geology and Exploration, 1996,(2): 23-27(in Chinese)

[2] Wang Yunjia, Huang bolu. Study on the Applications of Robust Statistics in Mining Engineering. World Coal Technology, 1993, (8):31-35(in Chinese)

[3] Wu Liangcai. Study on Evaluation of Condition and Prediction of Production Index in Working Face Mining. D Thesis. Beijing: China University of Mining and Technology, 1995(in Chinese)

[4] Wang Rongxin. Mathematical Statistics. Xi’an: Xi’an Jiaotong University Press, 1989 (in Chinese)

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