The deep exploration potential of basic-ultrabasic rock masses associated with Panzhihua-type vanadium-titanium magnetite (VTM) deposits are closely tied to the occurrence of deep-seated rock bodies. In this study, we utilized newly acquired 1:50000 scale aeromagnetic data from the Panxi region to perform a 3-D magnetization inversion using an improved regularized focusing conjugate gradient approach to achieve high-resolution 3-D magnetic imaging of basic-ultrabasic rock masses within the “Panzhihua-type” VTM concentration zone at depths reaching 10 km. The inversion results reveal that the 3-D magnetic anomalies of strong magnetic sources correspond with the distribution of the NS fault zones in the study area. However, these anomalies are predominantly located within narrow zones between the fault zones rather than directly along the fault lines. It also suggests that during the Late Huashan period, two rift regions might have developed in the Panxi area: the Anninghe Rift and the Panzhihua Rift. The deep and large faults within these confined rift valleys likely controlled the eruption and intrusion of mantle-derived magma, facilitating the emplacement of basic-ultrabasic strong magnetic rock masses along these zones. Additionally, the local shear structures within the paleo-rift zones may have provided ample space and a relatively stable environment conducive to the formation of VTM deposits.

In recent years, deep learning techniques have increasingly been applied to gravity inverse problems, and ResU-Net++ has demonstrated significant success in density-detail preservation during image segmentation and feature extraction. However, its large parameter space and high computational demand make it prone to overfitting. Diffusion models, conversely, excel at generating detailed samples and handling noisy data but generally underperform in segmentation tasks compared to ResU-Net++. To address these challenges, we propose ResDM-Net++, a novel framework that integrates the essential modules of ResU-Net++ into a diffusion model while embedding geophysical insights throughout. Specifically, we employ multi-channel gravity inputs obtained from physically forward-modeled density distributions to ensure that both localized and global subsurface features are learned. Moreover, rather than applying a generic denoising approach, the diffusion component is carefully adapted to retain the inherent spatial correlations of geophysical data, thus improving inversion stability and mitigating overfitting risks. Finally, ResDM-Net++ further embeds physics-based constraints into both the encoder-decoder path and the diffusion steps, forging a synergy between robust denoising and domain-focused feature extraction. Numerical study shows that ResDM-Net++ accurately recovers subsurface density anomalies, exhibiting clear model boundaries and minimal fitting errors. In field applications, it successfully delineates the F2 salt dome in Norway’s Nordkapp Basin with boundaries closely matching seismic interpretations, underscoring ResDM-Net++’s effectiveness in gravity data analysis, structural reconstruction, and inversion in both synthetic and real-world scenarios.

The “Lala-type” copper-iron deposits in the Lala area are primarily located in the central part of the Kangdian tectonic zone, distributed along an East-West (EW) trending Neoproterozoic gabbro-dolerite belt that is prominently controlled by regional EW-oriented tectonic structures. The genesis of these deposits is widely believed to be closely related to ancient volcanic structures, but their relationship with deep-seated basic intrusive rocks remains highly controversial. This article proposes an improved 3-D re-weighting regularized conjugate gradient (RRCG) focusing inversion method, constrained by the background field to recover the anomaly with a high resolution. The magnetic structure in a real case indicates the presence of a distinctly oriented basic intrusive rock mass in the deep part of the “Lala-type” Shilong copper deposit. Large-scale, high-precision magnetic profiles and magnetotelluric inversion results show that this strongly magnetic and high-resistance intrusive rock mass, which extends to a depth of 1.5 km, has transformed the ore-bearing basement into a clearly imaged anticlinal uplift and fold structure. Trenches and drill holes reveal that the ore bodies are primarily located in the fold and detachment spaces formed at the intersections of EW and North-South (NS) faults. The breccia formed by the basic rock intrusion along the faults provides favorable conditions for the occurrence and enrichment of ore bodies. The multiple intrusive thermal events in the Lala area not only supplied the fluids necessary for the enrichment of the deposits but also facilitated the transformation of the basement and the formation of ore-bearing spaces.

Nearly all global datasets from satellite missions have been instrumental in advancing research on regional and global-scale underground density structures. However, satellite gravity inversion faces challenges such as instability, multiple solutions, and low resolution due to the ill-posed nature of the problem. Methods like depth-weighting and different norms improve resolution but can introduce instability and noise sensitivity, requiring further refinement for optimal results. This article proposes a novel gravity inversion method in spherical coordinates, employing dynamically re-weighting matrix to enhance inversion resolution. Initially, weights are assigned to each tesseroid based on cross-correlation coefficients, forming an initial model weighting matrix. This matrix is then iteratively optimized by minimizing two regularized objective functions that incorporate the kernel matrix and observed data, refining the weights distribution to better approximate the actual geological situation. Synthetic model studies demonstrate that this method significantly improves the resolution of inversion results, particularly in identifying gently dipping density anomalies. Application of this method to the India-Asia collision zone reveals consistent subduction plate geometry with previous studies, validating its practicality and effectiveness in imaging intricate density patterns. This approach offers a substantial advancement in gravity inversion techniques, providing clearer and more accurate geological models.

Salt domes are very important in hydrocarbon exploration and identification of potential drilling hazards. While seismic data is indispensable for detailed subsurface imaging, especially in delineating the geometry and properties of salt bodies and their boundaries, gravity inversion provides an additional layer of data by exploiting the density differential. However, traditional methodologies for tackling this problem are complicated by the ill-posedness of the inverse problems. The alternative approach to gravity image is based on machine learning algorithms. Despite the appealing attributes of Convolutional Neural Networks, they are not exempt from limitations, including diminished precision in pinpointing geological features, complications in managing the varying scales of geological structures, and inefficiencies in processing voluminous, high-dimensional data. These deficits can be mitigated by the proposed multi-scale functional MS-UNets network, which, through integration with Squeeze-and-Excitation and Strip Pooling modules, are designed to enhance the capture of detailed information about salt domes. These networks were subjected to rigorous testing using both synthetic and real gravity data, showcasing their robustness across diverse scenarios. This testing highlighted their significant potential for applications in geophysical data interpretation, structural modeling, and inversion processes.

The stark contrast in density between geological layers is a fundamental aspect in the examination of basic geological structures. The delineation between the crystalline basement and sedimentary layers, moreover, is pivotal in the pursuit of strategic energy resources, such as petroleum and natural gas. Traditional full space density inversion, however, is beleaguered by issues of stability and resolution, impeding the accurate characterization of the sharp density interface. To rectify these shortcomings, we introduce an innovative methodology for estimating 2-D depth-to-basement and overlying density distribution, employing a deep neural network with a leaky rectified linear unit as an activation function. Evaluation of the proposed method on simulated sedimentary basin models underscores its superior ability to discern complex geometries of basin boundaries and overlying density, despite the presence of various degrees of Gaussian noise. In practical application to the Poyang basin, the relief of the Cretaceous basement is proficiently recovered through vertical gravity field data, with validation provided by corresponding seismic sections and well-established stratigraphic markers.

Gravity interface inversion is a critical technique in delineating the substructure of basins, providing essential technological and data support for oil and gas exploration. Traditional gravity inversion approaches often encounter issues such as suboptimal local solutions and limited resolution. Moreover, conventional deep learning inversion methods typically require extensive time for empirical parameter adjustment, hindering the achievement of optimal training outcomes. By utilizing Bouguer gravity anomaly data, this research pioneers the application of the EfficientNetV2 network in predicting 3-D basement relief interfaces and variations in overburden density. The network employs a composite scaling technique to adaptively adjust its width, depth, and input resolution, thereby identifying the most effective network configuration. Concurrently, the innovative Fused-MBconv convolutional module efficiently achieves superior results with a reduced number of network parameters. Specifically, in the Poyang Lake Basin study in Jiangxi Province, China, the EfficientNetV2 model demonstrated enhanced accuracy in predicting density variations of the basement interface and overlying strata compared to traditional methodologies.

The Dahongshan deposit is influenced by pronounced structural controls and intrusions, and its model of mineralization resulting from volcanic sedimentation has faced criticism for an extended period. To describe the deep structure and distribution characteristics of the ore deposit, and to investigate its ore-forming process and metallogenic model, this study employs the gravity, magnetic, and controlled source audio-magnetotelluric data with different scales to independently recover the density, magnetization intensity, and electrical resistivity for shedding light on the deep structure and mineralization distribution of the deposit. The inversion results show the presence of a giant intrusion extending up to 6 km deep within the deposit. The distribution of iron-rich ore bodies and deposit are controlled by the basement tilting and faults, with the deposit exhibiting a U-shaped distribution and the mineral body occurring in a lens-like shape. Additionally, the electrical results indicate the presence of high-resistance magma along faults that intrude the deposit. We propose that the deposit is a magmatic-related deposit, with a deep intrusion believed to be the residual source body from early rift intrusion, providing the source for mineralization of the deposit. The characteristics of the deposit controlled by east-west and north-south structures and the lenticular orebody distribution indicate that the deposit is closely influenced by regional structure and magmatism of fault intrusion. The mineralization model is proposed to be a result of the coordinated actions of structural, magmatic, and regional dynamic background for the breakup and convergence of supercontinents.

The study and assessment of the subsurface density distribution are vital for mining and oil & gas exploration. This can be achieved by the three-dimensional (3D) inversion of the observed gravity and gravity gradiometry (GG) data. Due to the ill-posedness of the geophysical inverse problem, the nonuniqueness and instability of solutions represent the main difficulties in inversion. In recent years, convolutional neural networks, especially U-net technology, have found wide applications in image processing, recognition, and reconstruction. This paper proposes using this method for fast reconstruction of the subsurface density models based on the ResUnet technology. The developed new method was examined on two 3D synthetic gravity and gravity gradiometry datasets inversion. The results show that the ResUnet network can reconstruct the density anomaly with sharp boundaries and is robust to the noise, making the solution stable.

Semi-airborne transient electromagnetic (SATEM) method has been efficiently used recently in geophysical exploration due to its relatively portability compared to the standard airborne surveys. However, the SATEM data are often complicated by induced polarization (IP) effects manifesting as abnormal decay and sign reversal in the responses. The IP information can be extracted from SATEM data by joint inversion of the electromagnetic data into the electrical resistivity and IP parameters described in Cole-Cole model. In this paper, we introduce a quasi-two-dimensional inversion scheme to recover the resistivity and IP parameters from SATEM responses by (1) a fast semi-analytical method for Jacobian matrix calculation; and (2) a staged inversion strategy with lateral constraints. The methodology is examined on two synthetic polarized models. Our study indicates that the proposed scheme can improve inversion stability and recover the underground resistivity and IP property distributions.

Modeling of induced polarization (IP) phenomena is important for developing effective methods for remote sensing of subsurface geology. However, the quantitative interpretation of IP data in a complex 3-D environment is still a challenging problem of applied geophysics. This letter develops a method of determining a 3-D distribution of the four parameters of the Cole-Cole model based on surface IP data. The method takes into account the nonlinear nature of both electromagnetic induction and IP phenomena. The solution of the 3-D IP inverse problem is based on the regularized conjugate gradient method. The method was tested on a synthetic model with variable dc conductivity, intrinsic chargeability, time constant, and relaxation parameters, and it was also applied to the actual 3-D IP survey data. We demonstrate that the four parameters of the Cole-Cole model, namely, dc electrical resistivity, chargeability, time constant, and the relaxation parameter, can be recovered from the observed IP data simultaneously.