The joint processing and integrated interpretation of multi-source geophysical exploration data are indispensable to the exploration evaluation of deep geothermal resources. Joint inversion and post-inversion geological differentiation are two major hot research topics in deep resource exploration. To integrate the multi-source geophysical field information and reduce the inversion multiplicity of single geophysical fields, this study built a structural model using the stratigraphic structure information from seismic interpretation, with the prior information of petrophysical properties as a guide. This study constrained the stratigraphic geophysical parameters using the Gaussian mixture model and conducted regularized joint inversion of gravity, magnetic, and magnetotelluric data, thus achieving the coupling of multiple physical structures. Finally, this study developed the software for the joint inversion of gravity, magnetic, magnetotelluric, and seismic data. Based on the joint inversion results and electrical resistivity, this study predicted the temperature field at typical hot dry rock sites using the Arrhenius law. The forward modeling results of the theoretical model for cubic anomalies were used for the joint inversion. Compared with individual inversion, the joint inversion performs well in the spatial characterization of anomalies and the recovery of physical property values. Furthermore, the joint inversion can fully integrate multiple data on geology, petrophysical properties, and geophysics, thus well conforming to the actual conditions.
Keywords：Gaussian mixture model;
joint inversion of gravity-magnetic-magnetotelluric data;
petrophysical properties;
prior information;
temperature field prediction
LIAN Sheng, CHENG Zheng-Pu, LUO Xuan, LI Jing-Jie, TIAN Pu-Yuan. Joint inversion of geophysical data under the guidance of petrophysical properties[J]. Geophysical and Geochemical Exploration, 2023, 47(6): 1580-1587 doi:10.11720/wtyht.2023.0032
Fig.2
Density, magnetic susceptibility, resistivity distribution of forward modeling Gaussian mixture model
a—the density-magnetic susceptibility probability distribution density diagram of forward model;b—the right figure is the density-resistivity probability density distribution of forward model
Fig.3
Comparison of separate inversion results and joint inversion results of gravity, magnetic and magnetotelluric data of cuboid model
a—gravity separate inversion results;b—cuboid model joint inversion density results;c—magnetic method separate inversion results;d—cuboid model joint inversion susceptibility results;e—magnetotelluric separate inversion results;f—cuboid model joint inversion resistivity results
Joint inversion of teleseismic receiver functions and magnetotelluric data using a genetic algorithm:Are seismic velocities and electrical conductivities compatible?
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Geophysics aims to image subsurface geologic structure and identify different geologic units. While the former has dominated the interpretation of applied geophysical data, the latter has received much less attention. This appears to have persisted despite applications such as those in mineral exploration that inherently rely on the inference of geologic units from geophysical and geologic observations. In practice, such activities are routinely carried out in a qualitative manner. Thus, it is meaningful to examine this aspect and to develop a system of quantitative approaches to identify different geologic units. The development of geophysical inversions in the last three decades makes such interpretation tools possible. We refer to this newly emerging direction as geology differentiation and the resultant representation of geology model as a quasi-geology model. In this article, we will provide an overview of the historical background of geology differentiation and the current developments based on physical property inversions of geophysical data sets. We argue that integrating multiple physical property models to differentiate and characterize geologic units and work with the derived quasi-geology model may lead to a step change in maximizing the value of geophysical inversions.
We propose a new framework for incorporating petrophysical and geological information into voxel-based geophysical inversion. By developing the geophysical inverse problem from a probabilistic perspective, we redesign the objective function and the iteration steps as a suite of cyclic optimization problems in which three separate MAP optimization problems are solved using geophysical, petrophysical and geological data, respectively. By quantitatively linking these data into a single framework, we recover a final inverted model that reproduces the observed, or desired, petrophysical and geological features while fitting the geophysical data. To achieve our goal we replace the Gaussian prior, used in the Tikhonov inversion approach, by a Gaussian mixture model. After each geophysical model update, the mixture parameters (means, variances and proportions) are determined by the geophysical model and the expected characteristics of the lithologies through another optimization process using the expectation–maximization algorithm. We then classify the model cells into rock units according to the petrophysical and geological information. These two additional steps over the petrophysical and geological data result in a dynamic update of the reference model and associated weights and guide the inversion towards reproducing the expected petrophysical and geological characteristics. The resulting geophysical objective function does not require extra terms to include the additional petrophysical and geological information; this is an important distinction between our work and previous frameworks that carry out joint geophysical and petrophysical data inversion. We highlight different capabilities of our methodology by inverting magnetotelluric and direct-current resistivity data in 1-D and 2-D, respectively. Finally, we apply our framework to inverting airborne frequency domain data, acquired in Australia, for the detection and characterization of saline contamination of freshwater.
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In a previous paper, we introduced a framework for carrying out petrophysically and geologically guided geophysical inversions. In that framework, petrophysical and geological information is modelled with a Gaussian mixture model (GMM). In the inversion, the GMM serves as a prior for the geophysical model. The formulation and applications were confined to problems in which a single physical property model was sought, and a single geophysical data set was available. In this paper, we extend that framework to jointly invert multiple geophysical data sets that depend on multiple physical properties. The petrophysical and geological information is used to couple geophysical surveys that, otherwise, rely on independent physics. This requires advancements in two areas. First, an extension from a univariate to a multivariate analysis of the petrophysical data, and their inclusion within the inverse problem, is necessary. Secondly, we address the practical issues of simultaneously inverting data from multiple surveys and finding a solution that acceptably reproduces each one, along with the petrophysical and geological information. To illustrate the efficacy of our approach and the advantages of carrying out multi-physics inversions coupled with petrophysical and geological information, we invert synthetic gravity and magnetic data associated with a kimberlite deposit. The kimberlite pipe contains two distinct facies embedded in a host rock. Inverting the data sets individually, even with petrophysical information, leads to a binary geological model: background or undetermined kimberlite. A multi-physics inversion, with petrophysical information, differentiates between the two main kimberlite facies of the pipe. Through this example, we also highlight the capabilities of our framework to work with interpretive geological assumptions when minimal quantitative information is available. In those cases, the dynamic updates of the GMM allow us to perform multi-physics inversions by learning a petrophysical model.
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Joint inversion of teleseismic receiver functions and magnetotelluric data using a genetic algorithm:Are seismic velocities and electrical conductivities compatible?
