为了帮助煤矿寻找井田内开采地质条件较好的区块,将研究区划分为若干个单元,分别统计各单元断裂信息维及其相关因素数据.通过对已揭露区统计数据进行灰色关联分析和逐步回归分析,发现断裂信息维能综合反映断层条数、断层密度、断层长度、断层强度等特征,且与断裂构造的相对复杂程度呈正相关关系.将已揭露区各单元断裂信息维及其影响因素(不含断层类指标)统计数据作为样本进行人工神经网络训练,达到精度要求后,用于预测井田不同单元的断裂信息维.对比东庞井田已揭露区各单元断裂信息维的统计值与预测值,准确率达90%左右.图2,表1,参9. In order to help the coal mine find the mining area with good geological conditions in the minefield, the study area is divided into a number of units, and the fracture information of each unit is calculated and its related factors are calculated respectively. By analyzing the statistical data of the uncovered areas, Regression analysis shows that the fractal dimension information can comprehensively reflect the features such as the number of faults, the fault density, the fault length and the fault strength, and has a positive correlation with the relative complexity of the fault structure. (Without fault index) as artificial neural network training samples, to meet the accuracy requirements for predicting fracture information dimension of different units in the mine field.Compared with the statistics and prediction Value, the accuracy rate of about 90%. Figure 2, Table 1, reference 9.