Semi-airborne transient electromagnetic (SATEM) method is an effective exploration tool in complex environments due to its utilization of unmanned aerial platforms for data collection. With the escalating demands of detection accuracy, the conventional 1-D inversion is no longer sufficient. Over the last two decades, there has been rapid development in 3-D electromagnetic (EM) inversions. SATEM method usually has a substantial number of receiver stations. To obtain accurate 3-D solutions, one has to refine the grids near the receiver points, which causes a huge number of grids and reduces computational efficiency. Thus, computational complexity is one of the primary factors restricting the practicality of 3-D inversions. In this article, we have developed an approach using decoupled meshes. This method uses a series of meshes to parallelly calculate the forward modeling and Jacobian information, with one mesh containing only a subset of receiver points. This scheme aims to decrease the number of grids and improve the efficiency. After that, we map the results to an overarching inversion mesh using the node cloud technique and renew the model parameters. We check our algorithm via both synthetic and field data. Numerical experiments show that the decoupled mesh inversion method can effectively recover the location and resistivities of the anomalies under the complex topography. When compared with the conventional inversion method, the decoupled mesh inversion method can significantly reduce the calculation time.

Landslides are common geological hazards that often result in significant casualties and economic losses. Due to their occurrence in complex terrain areas, conventional geophysical techniques face challenges in early detection and warning of landslides. Semi-airborne electromagnetic (SAEM) technology, utilizing unmanned aerial platforms for rapid unmanned remote sensing, can overcome the influence of complex terrain and serve as an effective approach for landslide detection and monitoring. In response to the low computational efficiency of conventional semi-airborne EM 3D forward modeling, this study proposes an efficient forward modeling method. To handle arbitrarily complex topography and geologic structures, the unstructured tetrahedron mesh is adopted to discretize the earth. Based on the vector finite element formula, the Dual–Primal Finite Element Tearing and Interconnecting (FETI-DP) method is further applied to enhance modeling efficiency. It obtains a reduced order subsystem and avoids directly solving huge overall linear equations by converting the entirety problem into the interface problem. We check our algorithm by comparing it with 1D semi-analytical solutions and the conventional finite element method. The numerical experiments reveal that the FETI-DP method utilities less memory and exhibits higher computation efficiency than the conventional methods. Additionally, we compare the electromagnetic responses with the topography model and flat earth model. The comparison results indicate that the effect of topography cannot be ignored. Further, we analyze the characteristic of electromagnetic responses when the thickness of the aquifer changes in a landslide area. We demonstrate the effectiveness of the 3D SAEM method for landslide detection and monitoring.

We develop a new spectral-element method by incorporating a domain-decomposition technique, referred to as the dual-primal spectral-element tearing and interconnecting method (SETI-DP), for high-precision forward simulation of the 3D direct current (DC) resistivity method. To handle the terrain and complex underground structures, we use unstructured tetrahedral meshes to divide the computational domain. These meshes are then partitioned into nonoverlapping subdomains. By applying a continuity condition of the current density to be solved, all subdomains become coupled to each other. Using a Lagrange multiplier, the global equation system for our forward modeling can be simplified into that for the subdomain interfaces, and the continuity conditions on the subdomain interfaces are obtained by solving these subdomain interface equations. Finally, the forward problem for each subdomain is solved independently using these boundary conditions. Numerical experiments indicate that our algorithm can greatly improve computational efficiency and reduce memory consumption without losing accuracy when choosing the appropriate number of subdomains. We further apply our method to the forward simulation of a tunnel detection problem. The numerical results demonstrate that our new SETI-DP method can effectively simulate the tunnel’s complex structures, and the DC resistivity method can detect the adverse geologic bodies in front of the tunnel face.

Over the past few decades, electromagnetic field signals have been widely used to develop data interpretation methods for the ground controlled source electromagnetic (CSEM) method in the frequency domain. However, identifying anomalous features from these signals remains challenging, as the intensity of the signals linked to these anomalies is weaker. To address this issue, this paper presents an inversion algorithm that utilizes apparent resistivity data to enhance anomaly detection. Due to the high computational complexity and memory demands of 3D CSEM inversion modeling, many real-world surveys are interpreted using 1D inversion schemes, which simplifies the modeling process but may limit accuracy. A quasi-2D inversion imaging technique based on the tracking balance operator (hereafter referred to as QITB) is introduced to balance efficiency and interpretive accuracy. Rather than performing 3D inversion, QITB applies 1D modeling in a structured framework to approximate 3D subsurface structures. This imaging-focused algorithm eliminates the need for complex 3D computations while maintaining high accuracy in identifying subsurface anomalies. Numerical simulations demonstrate that wide-field apparent resistivity is significantly more effective than electric field data in highlighting subsurface anomalies. Inversion results from synthetic data show that QITB identifies anomalies and yields more accurate estimates of burial depth. In contrast, conventional 1D inversion without the tracking balance operator, although capable of fitting 3D data, fails to approximate the true 3D model closely. In conclusion, QITB is an innovative and efficient method for rapidly interpreting 3D ground CSEM data, combining computational efficiency with reliable subsurface structure identification.