基于地震数据的近地表地裂缝智能识别方法研究

Intelligent identifi cation method for near-surface ground fi ssures based on seismic data

以晋中盆地的山西祁县某研究区为例,对该区块发育的地裂缝进行了智能识别。在对研究区区域地质背景充分分析的基础上,采用快速傅里叶算法(fast fourier transformation,FFT)对地震数据进行了倾角导向的中值滤波计算,不但使地震数据同相轴连续性提高而且还消除了随机噪音。在处理后的地震数据中拾取地层连续样本点200个,地层非连续样本点500个,并且提取多种属性(相干、曲率、振幅、频率等)作为多层感知器(multi-layer perceptron,MLP)神经网络训练的输入,训练期间利用归一化均方根(normalised RMS)和错误分类(misclassification)两个参数曲线追踪训练结果,两条曲线在训练期间为走低趋势,misclassification曲线值稳定在0.18,normalised RMS曲线值稳定在0.68,当normalised RMS曲线值达到最小值时为最优结果,此时可以终止训练,将该训练结果推广到整个数据体从而得到地裂缝智能识别体。从该属性体的剖面和沿层切片对地裂缝特征进行分析和识别,最终解释了11条地裂缝,倾角为60°~85°,落差在0~43 m,延展长度为300~1100 m。经统计得知,73%地裂缝走向与区域构造发育方向相一致,其中4条地裂缝与地表出露吻合,实际资料验证率达到100%,证明基于倾角导向体的地裂缝智能识别方法对地裂缝空间分布预测是十分有效的。

Taking a study area in Jinzhong Basin in Qixian County,Shanxi Province,as an example,this work performs an intelligent interpretation of ground fissures.On the basis of a complete analysis of the regional geological background in the study area,dip-steering cube operation and median filtering of seismic data were performed using fast Fourier transform to improve the continuity of seismic events and eliminate random noise.A total of 200 stratigraphic continuous sample training points and 500 discontinuous training points were obtained from the processed seismic data.Thereafter,a variety of attributes(coherence,curvature,amplitude,frequency,etc.)were extracted as the input for the multilayer perceptron neural network training.During the training period,the training results were traced by normalized root mean square error(RMSE)and misclassifi cation.The training results showed a downward trend during the training period.The misclassifi cation curve was stable at 0.3,and the normalized RMSE curve was stable at 0.68.When the value of the normalized RMSE curve reached the minimum,the training was terminated,and the training results were extended to the whole data volume to obtain the attribute cube of intelligent ground fi ssure detection.The characteristics of ground fi ssures were analyzed and identifi ed from the sections and slices.A total of 11 ground fissures were finally interpreted.The interpretation results showed that the dip angles were 60°-85°,the fault throws were 0-43 m,and the extension lengths were 300-1,100 m in the whole area.The strike of 73%of the ground fi ssures was consistent with the direction of the regional tectonic settings.Specifi cally,four ground fi ssures coincided with the surface disclosed,and the verifi cation rate reached 100%.In conclusion,the intelligent ground fi ssure detection attribute based on the dip-steering cube is eff ective in predicting the spatial distribution of ground fi ssures.