Magnetotelluric (MT) inversion algorithms are an essential tool in exploration geophysics because they provide us with resistivity models of the subsurface. Inverse problems are ill-posed and non-unique. Resistivity models produced by geologically unconstrained MT inversions are typically smooth, which means that the geological interpretations obtained based on the resistivity models will be ambiguous and uncertain. To solve the above problems, geophysicists have made many efforts over the years to introduce any prior information (such as geological structure information and physical property data) into models, which could help to better constrain the inverted model with the inversion algorithms. In the last few years, there has been renewed interest in machine learning techniques. Various machine learning methods have been applied to inversions. Using the fuzzy c-means (FCM) clustering method, geophysicists proposed new inversion algorithms that are capable of building petrophysical information into the inversion. Although the FCM has been used and advanced in previous work, geophysicists concluded that a priori information of the correct number and value of cluster centers is very important for a successful FCM inversion. In fact, it is difficult to obtain the appropriate clustering information in some geophysical survey areas. In this study, we present an effective way to build the petrophysical information for the MT inversion based on FCM clustering algorithm. When the actual petrophysical information is insufficient, considering the characteristics of MT data, we perform a one-dimensional blocky inversion that can clearly identify the interface with distinct electrical property contrast; then, we obtain the number and value of cluster centers from 1D blocky inversion for the further MT inversion with FCM clustering. The algorithm uses guided FCM clustering to improve the model within the iterative minimization during MT inversion. In our MT inversion method, we integrate the geophysical inversion and geological differentiation into a unified scheme, which interact and enhance each other. The resistivity models obtained from the inversion respect the geophysical and petrophysical data and are easy to geologically interpret. We tested the algorithm using two synthetic examples and a field data example.

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.

We have used the integrated interpretation of gravity, magnetotelluric (MT) data, and seismic data to improve the structural imaging of the Dayangshu Basin. The Dayangshu Basin is mainly composed of clastic and volcanic rocks. The logging data in the basin show different degrees of direct hydrocarbon indication, suggesting that the Dayangshu Basin has good potential for exploration. However, the widely distributed volcanic rocks attenuate seismic waves and lead to poor seismic imaging. Thus, the seismic signal is weak in the Ganhe Formation (K1g) and reliable seismic images cannot be obtained below that formation. MT data can accurately obtain images of deep structures because the resistivity of volcanic rocks is significantly higher than that of sedimentary rocks. Therefore, to obtain a more reliable geologic model, we combine the traditional 3D MT inversion result with logging and seismic data to establish an initial model. The 3D MT fuzzy constrained inversion (FCI) produces a more reliable geophysical model and geologically meaningful results. The resistivity model inverted from FCI shows that volcanic rocks are widely distributed in the Ganhe Formation, and the resistivity value of the lower section of the Longjiang Formation is greater than that of the upper section of the Longjiang Formation. Finally, the 3D gravity inversion with structural constraints from 3D MT FCI method was performed to improve the model resolution in depth and to highlight the density variations within the Jiufengshan Formation, which can further optimize the geologic model. We have determined how the effective integration of gravity, MT, and seismic data can improve the structural imaging of the Dayangshu Basin.