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

发布时间：2018-04-04 07:32

本文选题：一致性　切入点：Facet模型梯度算子　出处：《吉林大学学报(地球科学版)》2017年04期

【摘要】：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。
[Abstract]:The automatic identification of geological bodies with discontinuous structures such as faults and angular unconformities is of great significance in seismic structural interpretation. The seismic responses of these geological features are cophase discontinuous.The conventional seismic data discontinuity identification method has a limited range of applications, and the parameter setting depends on human experience, which can easily lead to poor identification degree of the identification results.In this paper, consistency is introduced into seismic data processing as a new method of discontinuity identification.Firstly, consistency is calculated by using gradient operator of Facet model, which has high positioning accuracy and is easy to be extended. Secondly, consistency data is treated by thresholding. Finally, corrosion, expansion and thinning algorithms in mathematical morphology are used for further processing.The automatic recognition of discontinuity information of seismic data is realized.Through theoretical and practical data testing, and compared with C3 coherent algorithm and variance algorithm, it is proved that the proposed method has higher stability and identification degree in seismic data discontinuity recognition.It can be used as a powerful tool for recognition of stratigraphic discontinuity.
【作者单位】：吉林大学地球探测科学与技术学院;国土资源部应用地球物理重点实验室;
【基金】：国家自然科学基金项目(41430322,41522404) 国家重点基础研究发展计划(“973”计划)项目(2013CB429805)~~
【分类号】：P631.4

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