Abstract
Different geophysical methods provide information about various physical properties of rock formations and mineralization. In many cases, this information is mutually complementary. At the same time, inversion of the data for a particular survey is subject to considerable uncertainty and ambiguity as to causative body geometry and intrinsic physical property contrast. One productive approach to reducing uncertainty is to jointly invert several types of data. Non-uniqueness can also be reduced by incorporating additional information derived from available geological and/or geophysical data in the survey area to reduce the searching space for the solution. This additional information can be incorporated in the form of a joint inversion of multiphysics data. This paper presents an overview of the main ideas and principles of novel methods of joint inversion, developed over the last decade, which do not require a priori knowledge about specific empirical or statistical relationships between the different model parameters and/or their attributes. These approaches are designated as follows: (1) Gramian constraints; (2) Gramian-based structural constraints; (3) localized Gramian constraints; and (4) joint focusing constraints. We provide a short description of the mathematical foundations of each of these approaches and discuss the practical aspects of their applications in mineral exploration.
Highlights
Information from different surveys is mutually complementary, which makes it natural to consider a joint inversion of the data to a shared model, a process which can be implemented using several different physical and mathematical approaches
The joint inversion can use these relationships or can indicate and characterize the existence of this correlation, yielding an improved final model. Another approach to joint inversion uses a clustering concept from statistics, which assumes that the subsurface geology can be described by the models with petrophysical parameters forming a specific number of the known clusters in the space of the models (e.g., [10,11,12])
We review the mathematical principles of these four advanced approaches to joint inversion of multiphysics geophysical data and discuss some aspects of their applications
Summary
Information from different surveys is mutually complementary, which makes it natural to consider a joint inversion of the data to a shared model, a process which can be implemented using several different physical and mathematical approaches. This review paper outlines and illustrates four novel approaches to joint inversion, which would not require a priori knowledge about specific empirical or statistical relationships between the different model parameters and/or their attributes. The form of the relationships between different model parameters can change from one section of the inversion domain, with one type of lithological properties to another with different lithology This is important in the case of complex geology. We have included, as an illustration, one example related to joint inversion of airborne magnetic and electromagnetic data using Gramian-based structural constraints. This example clearly illustrates the benefits of joint inversion of the EM and magnetic data in mineral exploration
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