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.

Conventional 3D magnetic inversion methods are based on the assumption that there is no remanent magnetization, and the inversion is run for magnetic susceptibility only. This approach is well-suited to targeting mineralization; however, it ignores the situation where the direction of magnetization of the rock formations is different from the direction of the induced magnetic field. We present a novel method of recovering a spatial distribution of magnetization vector within the rock formation based on joint inversion of airborne gravity gradiometry (AGG) and total magnetic intensity (TMI) data for a shared earth model. Increasing the number of inversion parameters (the scalar components of magnetization vector) results in a higher degree of non-uniqueness of the inverse problem. This increase of non-uniqueness rate can be remedied by joint inversion based on (1) Gramian constraints or (2) joint focusing stabilizers. The Gramian constraints enforce shared earth structure through a correlation of the model gradients. The joint focusing stabilizers also enforce the structural similarity and are implemented using minimum support or minimum gradient support approaches. Both novel approaches are applied to the interpretation of the airborne data collected over the Thunderbird V-Ti-Fe deposit in Ontario, Canada. By combining the complementary AGG and TMI data, we generate jointly inverted shared earth models that provide a congruent image of the rock formations hosting the mineral deposit.

Gravity and magnetic surveys have been extensively employed in various fields like regional geology studies, environmental engineering, and mineral exploration. However, interpreting the data from these surveys remains a challenge because the potential field inversion lacks uniqueness. To address this issue, combining gravity and magnetic data in a joint inversion helps to narrow down the possible solutions. This study introduces a method for jointly inverting gravity and magnetic data, specifically focusing on the sediment–basement interface. Within this framework, a 3D voxel-type inversion using joint Gramian-based techniques incorporates a depth-to-basement model as a guiding constraint. This approach was applied to gravity and magnetic data collected around the Victoria Mine area in Nevada, USA. The joint inversion successfully produced 3D models representing the density and susceptibility of both unconsolidated sediments and the basement underlying the surveyed region.

Full tensor magnetic gradiometry (FTMG) is becoming a practical method for exploration due to recent advancements in superconducting quantum interference device (SQUID) technology. This paper introduces an efficient method of 3D modeling and inversion of FTMG data. The forward modeling uses single-point Gaussian integration with pulse basis functions to compute the volume integrals representing the second spatial derivatives of the magnetic potential. The inversion is aimed at recovering both the magnetic susceptibility and magnetization vectors. We have introduced a 3D regularized focusing inversion technique that utilizes Gramian regularization and a moving sensitivity domain approach. We have also developed a new method of magnetization vector decomposition into induced and remanent parts. The case study includes applying the developed inversion method and computer code to interpret a helicopter-borne FTMG survey carried out over the Thompson Nickel Belt. We have analyzed and separately inverted the observed FTMG and total magnetic intensity (TMI) data using the developed 3D inversion methods to obtain the subsurface susceptibility and magnetization vector models. Furthermore, we present a comparison of the inversions utilizing the FTMG data and the TMI data.

The presence of shallow conductive heterogeneities can cause static shift in controlled source audio-frequency magnetotellurics (CSAMT) apparent resistivity sounding curves. This is observed as a shift along the apparent resistivity axis in double logarithmic coordinates. Such effect can cause difficulties in CSAMT data interpretation. In this letter, we established a new method to identify and correct the CSAMT static shift based on the high-resolution Radon transformation. We took advantage of the property that the static shift of apparent resistivity curves behaves as a point in the Radon domain. We presented 3-D synthetic study to demonstrate that the static shift can be effectively removed from the apparent resistivity curve. However, due to the low resolution and precision, the traditional Radon transform can generate ``scissor-tail'' and then, the static shift may not be completely removed. We proposed to use a new high-resolution Radon transform by improving the regularization matrix using the least squares inversion. Our numerical simulation shows that by using this high-resolution Radon transform, the static shift converges accurately to a point without showing the ``scissor-tail.'' In the application to field CSAMT data, the high-resolution Radon transform method was able to correct the static shift effectively; thus, it can improve the precision and accuracy in data interpretation.