基于Facet模型梯度算子一致性的地震数据不连续性识别方法

存在断层、角度不整合面等不连续结构的地质体的自动识别在地震构造解释中具有重要的意义,这些地质特征的地震响应为同相轴不连续。常规的地震数据不连续性识别方法应用范围有限,参数设置依赖于人为经验,易导致识别结果辨识度差。本文将一致性作为新的不连续性识别方法引入到地震数据处理中。首先利用定位精度高、易于扩展的Facet模型梯度算子计算一致性,其次对一致性数据作阈值化处理,最后利用数学形态学中的腐蚀、膨胀及细化算法作进一步处理,实现了对地震数据不连续性信息的自动识别。经过理论和实际资料测试,并与C3相干算法和方差算法对比分析,证实了本文所提方法在地震数据不连续性识别方面具有更高的稳定性和辨识度,可以作为地层不连续性识别的有力工具。

Seismic Data Discontinuity Identification Using Coherence Based on Facet Model Gradient Operator

Automatic identification of discontinuous geological bodies such as faults and angle unconformity is of significance in seismic structural interpretation, which in the seismic profile appears as the discontinuity of reflection events.The application scope of the conventional seismic data discontinuity identification method is limited due to the fact that its parameter setting relies on human experiences, which could easily results in an improper identification.Therefore, in this paper, coherence is introduced into seismic data processing as a new discontinuity identification parameter.Firstly, the coherence was calculated by the Facet model gradient operator, which is high positioning accurate and easy to be expanded.Secondly, we applied a threshold on the coherence data.Finally, the corrosion, expansion and thinning algorithm in mathematical morphology were used for further processing to realize the automatic identification of the seismic data discontinuity information.Through the synthetic and field seismic data tests, and the comparison with C3 coherent algorithms, as well as variance algorithm, we can demonstrate that the proposed method can be used as a powerful tool for the discontinuity identification in formations, with a higher stability and identification ability in seismic data discontinuity lineation.