The electrical resistivity method is widely used in near-surface mineral exploration. At present, the deterministic algorithm is commonly employed in three-dimensional (3-D) electrical resistivity inversion to obtain subsurface electrical structures. However, the accuracy and efficiency of deterministic inversion rely on the initial model. In practice, obtaining an initial model that approximates the true subsurface electrical structures remains challenging. To address this issue, we introduce a broad learning (BL) network to determine the initial model and utilize the limited memory quasi-Newton (L-BFGS) algorithm to conduct the 3-D electrical resistivity inversion task. The powerful mapping capability of the BL network enables one to find the model that elucidates the actual observed data. The single-layer BL network makes it efficient and easy to realize, leading to much faster network training compared to that using the deep learning network. Both the synthetic and field experiments suggest that the BL framework could effectively obtain the initial model based on observed data. Furthermore, in comparison to using a homogeneous medium as the initial model, the L-BFGS inversion with the BL framework-designed initial model improves the inversion accuracy of subsurface electrical structures and expedites the convergence speed of the iteration. This study provides an effective approach for fast initial model design in a data-driven manner when the prior information is unavailable. The proposed method can be useful in high-precision imaging of near-surface mineral electrical structures.

Gravity data inversion is of critical importance in geophysics, encompassing a range of applications, such as the exploration of geological resources, the identification of geological structures, and the detection of groundwater resources. This study proposes a three-dimensional (3D) machine learning approach to enhance the efficiency of the aforementioned exploration tasks by leveraging gravity data. The mapping relationship between gravity data and subsurface density structures is modeled by the broad learning network, distinguished by its high training efficiency and robust modeling capability. Notably, the proposed inversion method obviates the constraints on the number of anomalies prior to the inversion process. This is achieved by setting one anomaly with varied locations for different training samples. Numerical and field data applications demonstrate the efficiency of the proposed 3D machine learning gravity data inversion method, especially in automatically determining the number of anomalies. In particular, the proposed method produced accurate density inversion results in the field application, aiding in the identification of potential oil and gas reservoirs in the target region and offering the potential for broader application in other resource exploration. The proposed inversion method can promote the construction of density structures of subsurfaces based on gravity data.

Reduction to the pole (RTP) is a fundamental method for interpreting magnetic anomaly data. It faces challenges at low magnetic latitudes and uneven observation surfaces, potentially leading to inaccurate localization of magnetic sources. To address these issues, we develop a novel approach under the machine-learning framework for achieving stable and rapid magnetic RTP in arbitrary regions. First, a single magnetic dipole with random size, position, and magnetization magnitude is used in the forward model to generate samples of the original total magnetic intensity (TMI) anomaly data and the corresponding vertical TMI, which simplifies the establishment of training data. Second, the broad learning system (BLS) is used to establish a mapping relationship between the original and vertical TMI data, as the BLS exhibits a fast network training process due to its concise network structure and strong mapping ability. The combination of the sample generation strategy and BLS allows for rapid network construction and RTP completion. The feasibility of our method is validated by synthetic models and field applications. Synthetic data tests suggest that the BLS approach can be effectively applied to magnetic RTP across various regions, such as the magnetic equator and undulating surfaces. It demonstrates superior accuracy and noise robustness compared to the wavenumber-domain method. Field applications indicate that BLS prediction results facilitate further exploration of mining areas and save over 91% of the time compared with the equivalent source and deep-learning methods. This study is useful for the rapid and accurate localization of magnetic sources and for providing horizontal positional constraints for 3D magnetic inversion.

Three-dimensional (3-D) magnetic inversion is an essential technique for revealing the distribution of subsurface magnetization structures. Conventional methods are often time-consuming and suffer from ambiguity due to limited observations and non-uniqueness. To address these limitations, we propose a novel inversion method under the machine learning framework. First, we design a training sample generation space by extracting the horizontal positions of magnetic sources from the analytic signal amplitude and the reduced-to-the-pole anomalies of magnetic field data. We then employ coordinate transformation to achieve data augmentation within the designed space. Subsequently, we utilize a broad learning network to map the magnetic anomalies to 3-D magnetization structures, reducing the magnetic inversion time. The efficiency of the proposed method is validated through both synthetic and field data. Synthetic examples indicate that compared to the traditional inversion method, the proposed method approximates the true model more closely. It also outperforms traditional and deep learning methods in terms of computational efficiency. In the field example of the Danzhukeng Pb-Zn-Ag deposit in South China, the inversion result is consistent with drilling and controlled-source audio frequency magnetotelluric survey data, providing valuable insights for subsequent exploration. This study provides a new practical tool for processing and interpreting magnetic anomaly data.

The Fengjiaping Landslide, located at the transitional zone between the western Qinling Mountains and the southwestern margin of the Loess Plateau in China, is a reactivated loess–mudstone interface landslide. Its complex evolution is influenced by geological, hydrological and climatic factors, as well as human activities. The critical zone regulates precipitation infiltration, which, in turn, controls soil moisture and groundwater dynamics. Although excessive water infiltration is recognized as the primary trigger, the landslide exhibits heterogeneous deformation, with recurrent events not always correlated with rainfall, making its reactivation mechanisms difficult to understand and predict. Potential sliding zones in moisture-induced landslides are typically characterized by high soil moisture and elevated water fluxes, manifesting as low electrical resistivity and enhanced streaming current densities. In this study, we applied an integrated geophysical approach, combining direct-current electrical resistivity tomography (ERT) and self-potential (SP) measurements, to infer subsurface water pathways and identify zones potentially contributing to slope instability. The joint interpretation of SP and ERT data suggests preferential flow channels and groundwater activity beneath scarps and cracks, highlighting their potential role as conduits for infiltration and slope weakening. Despite these insights, uncertainties remain due to limitations in data coverage, boundary effects, and simplified assumptions in the inversion framework. Future work should focus on continuous and time-lapse SP and ERT monitoring, complemented by methods such as induced polarization and borehole investigations, to better constrain subsurface hydrogeological properties and improve the understanding of the processes governing slope instability.