基于聚类和多元地质统计学的电—震联合建模约束反演技术及应用

随着待勘探目标地质体越来越复杂且埋深增大，单一地球物理勘探方法的片面性和局限性日益突显，综合利用多种地球物理技术及相应数据已成为现今必然趋势。为此，提出基于多元地质统计学的交叉—变差函数建立速度与电阻率之间的岩石物理关系，并在此基础上利用机器学习中的引导模糊C均值聚类算法进行基于岩石物理关系的多重约束反演，实现电—震联合建模。大杨树盆地南部坳陷实际资料的应用结果表明，该电—震联合建模约束反演可逐步降低单一地球物理方法的多解性，提高对目标地质体的识别能力。非地震与地震方法所得结果相互印证，展示了该联合建模约束反演技术具有良好应用潜力。

Technique and application of joint magnetotelluric and seismic modeling and constrained inversion based on clustering and multivariate geostatistics

With more and more complex and deeper geological bodies, the one-sidedness and limitation of a single geophysical method have become more and more prominent, and comprehensive utilization of multiple geophysical technologies and data has become an inevitable trend. This paper proposes cross-variogram in multivariate geostatistics to establish the petrophysical relationship between velocity and resistivity. Based on the relationship, the guided fuzzy C-means clustering algorithm in machine learning is used to conduct multi-constraint inversion and realize MT-seismic joint mo-deling. Application to real data recorded from the southern depression of the Dayangshu Basin has proved that the MT-seismic joint modeling and multi-constraint inversion can gradually reduce the ambiguity of a single geophysical method and improve the ability to identify target geological bodies. The results of MT and seismic methods are verified each other, demonstrating that the joint modeling and multi-constraint inversion technology is potential in field application.