We investigate an algorithm for 3D time-domain airborne electromagnetic (AEM) inversion based on the finite-volume (FV) method and direct Gauss-Newton optimization, where we obtain high efficiency by constraining the modeling volume to the AEM volume of influence (VOI) of a 3D source within the earth, rather than using the larger VOI of the AEM system. A half-space or layered earth is used to model the background field in the time domain, taking into account the transmitter waveform through convolution. Assuming that the 3D source of any secondary field detected at a survey point lies within the moving VOI of the airborne system, we conduct time-domain forward modeling and Jacobian calculation using an FV method within the 3D source VOI that requires a small number of cells for discretization. A local mesh and direct solver are shown to further speed up the computation. A synthetic isolated synclinal conductor inversion shows good agreement with the model geometry and provides a good fit to the data contaminated with noise. A synthetic multiple-body model inversion was also quite successful, showing that our algorithm is effective and about four times faster than inversion using the total-field method. Finally, we inverted GEOTEM data over the Lisheen deposit, where our inversion result was consistent with the published geology.

Three dimensional gravity inversion is an effective way to extract subsurface density distribution from gravity data. Different from the conventional geophysics-based inversions, machine-learning-based inversion is a data-driven method mapping the observed data to a 3D model. We have developed a new machine-learning-based inversion method by establishing a decomposition network (DecNet). Unlike existing machine-learning-based inversion methods, the proposed DecNet method is a mapping from 2D to 2D, which requires less training time and memory space. Instead of learning the density information of each grid point, this network learns the boundary position, vertical center, thickness, and density distribution by 2D-to-2D mapping and reconstructs the 3D model by using these predicted parameters. Furthermore, by using the highly accurate boundary information learned from this network as supplement information, the DecNet method is optimized into a DecNetB method. By comparing the least-squares inversion and U-Net inversion on synthetic and real survey data, the DecNet and DecNetB methods have shown the advantage in dealing with inverse problems for targets with boundaries.

Airborne electromagnetic (AEM) forward modeling has been extensively developed in past years. However, not much attention has been paid to the adaptive numerical algorithms for time-domain electromagnetic modeling. We have created an adaptive method that can generate an effective mesh for time-domain 3D AEM full-wave modeling using an unstructured finite-element method and a backward Euler scheme. For the estimation of the posterior error in the adaptive process, we use a hybrid technique based on the continuity of the normal current density for modeling the off-time channels, and on the continuity of the tangential magnetic field for the on-time channels. To improve the stability of the forward modeling and control the number of grids in the adaptive process, a random grid-selection technique is applied. We check the modeling accuracy of the algorithm by comparing our adaptive results with the semianalytical solution for a time-domain AEM system over a homogeneous half-space. Furthermore, we test the effectiveness of our algorithm for multiple-source time-domain AEM systems by analyzing the meshes generated by the adaptive method and the model results. Finally, we study the topographic effect by calculating time-domain AEM responses over a hill model with an abnormal body embedded.

For the mineral exploration in complex terrain areas, the semi-airborne transient electromagnetic (SATEM) technology is one of the most powerful methods due to its high efficiency and low cost. However, since the mainstream SATEM systems only observe the component dBz/dt and the data are usually processed by simple interpretation or one-dimensional (1D) inversion, their resolutions are too low to accurately decipher the fine underground structures. To overcome these problems, we proposed a novel 3D forward and inversion method for the multi-component SATEM system. We applied unstructured tetrahedron grids to finely discretize the model with complex terrain, subsequently we used the vector finite element method to calculate the SATEM responses and sensitivity information, and finally we used the quasi-Newton method to achieve high-resolution underground structures. Numerical experiments showed that the 3D inversion could accurately recover the location and resistivities of the underground anomalous bodies under the complex terrain. Compared to a single component data, the inversion of the multi-component data was more accurate in describing the vertical boundary of the electrical structures, and preferable for high-resolution imaging of underground minerals.

Many mineral deposits have low resistivity. This property makes the electromagnetic (EM) method a very useful tool for mineral exploration. In the past few decades, the application of EM exploration technologies has been reviewed by many researchers. However, most reviews focused on EM exploration methods, the development of equipment, or their applications. Three-dimensional forward modeling and inversions are high-accuracy EM interpretation techniques in which great progress has been made in recent years in mineral exploration. We review the development of EM technology for mineral exploration with a focus on 3D EM forward modeling, inversion technology, and its applications. We first briefly introduce the EM methods for mineral exploration. Next, we give a comprehensive review of 3D EM forward modeling, inversion, and data interpretation, with special attention paid to the development of EM theory and applications in mineral exploration. We hope this review can promote the application of 3D EM numerical simulation and inversion in mineral exploration.