基于多尺度地震曲率属性融合的煤田小断层识别分析

Identification Analysis of Small Faults in Coalfields Based on Multi-scale Seismic Curvature Attribute Fusion

为适应中国煤矿智能化发展对地质条件透明度的要求,识别落差在3 m左右的小断层已成为构造解释中的重要地质任务。通过介绍S变换时频分析、PCA(Principal Components Analysis,主成分分析)和RGB融合基本原理,提出了1种基于多尺度地震曲率属性融合的煤田小断层识别方法。结果发现:S变换时频分析得到多频率的地震数据体,不同频率的地震数据体对应不同尺度的地下地质信息写照;对地震数据进行频谱分析,确定地震信号的主导频率、频带等参数,提取不同频率下地震数据体的最大正曲率属性;采用PCA方法对不同频率地震数据的地震曲率属性进行分析,采用RGB融合方法对前3个主成分进行融合;实际应用发现多尺度地震曲率属性融合结果在识别小断层方面具有明显优势,可以提高地震资料中小断层的精度和解释精度。

In order to adapt to the requirements of the intelligent development of China's coal mines for the transparency of geological conditions,the identification of small faults with a drop of about 3 m has become an important geological task in the tectonic interpretation.Based on the introduction of the basic principles of the S-transform time-frequency analysis,PCA(Principal Component Analysis)and RGB fusion,a method of identifying the small faults in coalfields based on the fusion of multi-scale seismic curvature attributes was proposed.The results showed that the S-transform time-frequency analysis obtained multi-frequency seismic data bodies,and different frequency seismic data bodies corresponded to different scales of subsurface geologic information writings;spectral analysis was performed on the seismic data to determine the dominant frequency,frequency band and other parameters of seismic signals,and to extract the maximum positive curvature attributes of the seismic data bodies at different frequencies;the seismic attributes of the seismic curvature data of different frequencies were analyzed using PCA method,and the first three seismic curvature attributes of the seismic data of different frequencies were fused using the RGB fusion method;the application of actual seismic data found that the results of multi-scale seismic curvature attribute fusion had obvious advantages in identifying small faults,which can improve the accuracy and interpretation precision of small faults in seismic data.