Gravity and magnetic inversion are important methods for comprehensive quantitative interpretation of data obtained in, e.g., mineral, oil and gas, and geothermal exploration. At present, the 3-D joint inversion technology of gravity and magnetic data is facing challenges from large-scale data exploration applications. In this letter, a new algorithm for 3-D joint inversion of gravity and magnetic data with high accuracy and low computational cost is presented. We use the geometric trellis method to perform fast forward calculations and then introduce the sparse constraint and adaptive sensitivity matrix into the model constraint terms. The inexact structural resemblance method is then used to add the cross-gradient constraint penalty term to the objective function. Finally, an algorithm (DS-TGN) combining data-space (DS) and truncated Gauss–Newton (TGN) methods is used to solve the joint inversion objective function. Numerical experiments with synthetic data show that the proposed algorithm can significantly reduce the computational cost and obtain high accuracy density and magnetization models with structural resemblance and sharp boundaries. We also apply the DS-TGN algorithm to data obtained in the area of Greater Khingan in northwestern Heilongjiang, China. The underground density and magnetization distribution results provide a high-resolution geological model for the detection of skarn-type deposits.

Seismic background noise affects the recognition of reflection signals, thereby impeding the subsequent seismic data processing, such as seismic imaging and inversion. In addition, seismic background noise has relatively complex properties, such as non-stationarity and spectral aliasing, which can be further hampered with the deterioration of the exploration environment. Deep-learning methods have been successfully applied to effectively attenuate complex seismic noise and have shown significant improvements over conventional denoising methods. However, most denoising networks only utilize single-scale features, resulting in poor performance when confronted with seismic data in a low signal-to-noise ratio. To further enhance the denoising capability, a novel multiscale residual pyramid network (MRP-Net) was proposed to separate the desired signals and complex seismic noise. Compared with single-scale networks, MRP-Net can take advantage of the multiscale features, thereby improving noise attenuation capability. In general, the pyramid-like framework in MRP-Net can extract the potential features at different scales through down-sampling and up-sampling operations, and skip connections were applied to fuse the global-coarse and local-fine features. On this basis, a double-path spatial attention module was designed to enhance the desired features, further improving the processing performance of separating the desired signals from the intense seismic background noise. Comprehensive experiments on synthetic and field seismic data demonstrate that MRP-Net is effective for complex seismic noise attenuation, both for conventional geophone-acquired data and DAS records.

Full waveform inversion (FWI) is currently the most accurate technique for obtaining the properties of subsurface media. The absence of low frequencies in the observed data caused cycle-skipping phenomenon and poor initial model which affect the convergence of FWI. We propose a global-feature-fusion and multi-scale network (GM-Net) in a way of supervised learning to compensate for the absent low frequency components in the observed data trace by trace. The difficulty of extrapolating frequency is to achieve smoothness and continuity when changing from high frequency signals to low frequency signals, which is visually shown in the reduction and movement of the sidelobes in high-frequency signals and the overall oscillation of the signals is slowed down. For achieving better extrapolation, the encoder-decoder architecture with multi-scale feature extraction is designed as the backbone of the network. For avoiding the loss of information, we propose to perform 1/2 down-sampling on the original input signal separately based on the odd and even time samples, and then concatenate them along the channel dimension. Since 1-dimensional (1D) seismic data is a type of time-series signal and the wavelengths of low frequencies are long, we pay more attention to the relevance of contextual information. Thus, dilated convolution layers, gridding convolution blocks and non-local attention blocks are used to enlarger the receptive field both in time and channel dimensions to extract and fuse global features. Numerical tests both on synthetic data and different types of field marine data demonstrate the feasibility and generalization of our method.

Addressing the significant impact of undulating terrain on 3D Direct Current inversion, the unreliability of traditional linearized polarizability inversion for highly polarizable anomalies, and the non-uniqueness of separate resistivity and Time-Domain Induced Polarization inversion, this paper conducts a joint inversion study of 3D Direct Current resistivity and Time-Domain Induced Polarization with structural constraints in undulating topography. A regularly arranged deformed hexahedron mesh simulates undulating surface terrain, transformed into regular hexahedron elements for 3D undulating terrain DC resistivity modeling. Based on the exact inversion of polarization calculated from the inversion results of apparent resistivity and equivalent apparent resistivity data, a joint inversion of resistivity and polarization constrained by cross-gradient is implemented. Synthetic data examples show that the application of arbitrary hexahedron elements significantly reduces the influence of terrain on inversion, and the implementation of joint inversion markedly improves the recovery of high-polarization anomalies while enhancing both the model resolution and the inversion accuracy. The proposed algorithm is applied to the joint inversion of resistivity and polarizability in the lead-zinc mining area of Xiagalaiaoyi River in Huzhong area, the Great Khingan Mountains, northwestern Heilongjiang Province, achieving good results.

Joint inversion algorithms continue to receive a lot of interest in the application of geophysical interpretation due to the reduction of uncertainty of the inverse solutions. Over the past decade, several coupling constraints, structural or rock physics, have been introduced to the joint inversion of multiple geophysical data sets. However, a single coupling constraint may not provide the optimum solution for all geologic scenarios. To improve the application of the multiple geophysical data sets, in which some rock-physics information is available, we have developed a new joint inversion algorithm based on a mixed structural and rock-physics coupling constraints. The presented algorithm incorporates the cosine dot product gradient function, a structural coupling constraint, with a guided fuzzy c-means clustering strategy, which is a rock-physics coupling constraint. The former imposes the structural similarity among different model parameters, whereas the latter improves the linear or nonlinear correlation among model parameters. We use the exact structural resemblance algorithm to numerically solve the mixed coupling constraint joint inversion objective function. The developed algorithm is applied on 2D synthetic and field data examples. Our results find a significant improvement in the resolution of the reconstructed models, in comparison with traditional single coupling algorithms, even when the rock-physics information is not complete. This algorithm is more conducive to obtaining clear geologic unit boundaries and provides a favorable basis for delineating metallogenic target areas.

It is of great significance to construct a three-dimensional underground velocity model for the study of geodynamics and tectonic evolution. Southeast Asia has attracted much attention due to its complex structural features. In this paper, we collected relative travel time residuals data for 394 stations distributed in Southeast Asia from 2006 to 2019, and 14,011 seismic events were obtained. Then, teleseismic tomography was applied by using relative travel time residuals data to invert the velocity where the fast marching method (FMM) and subspace method were used for every iteration. A novel 3D P-wave velocity model beneath Southeast Asia down to 720 km was obtained using this approach. The tomographic results suggest that the southeastern Tibetan Plateau, the Philippines, Sumatra, and Java, and the deep part of Borneo exhibit high velocity anomalies, while low velocity anomalies were found in the deep part of the South China Sea (SCS) basin and in the shallow part of Borneo and areas near the subduction zone. High velocity anomalies can be correlated to subduction plates and stable land masses, while low velocity anomalies can be correlated to island arcs and upwelling of mantle material caused by subduction plates. We found a southward subducting high velocity body in the Nansha Trough, which was presumed to be a remnant of the subduction of the Dangerous Grounds into Borneo. It is further inferred that the Nansha Trough and the Dangerous Grounds belong to the same tectonic unit. According to the tomographic images, a high velocity body is located in the deep underground of Indochina–Natuna Island–Borneo–Palawan, depth range from 240 km to 660 km. The location of the high velocity body is consistent with the distribution range of the ophiolite belt, so we speculate that the high velocity body is the remnant of thee Proto-South China Sea (PSCS) and Paleo-Tethys. This paper conjectures that the PSCS was the southern branch of Paleo-Tethys and the gateway between Paleo-Tethys and the Paleo-Pacific Ocean. Due to the squeeze of the Australian plate, PSCS closed from west to east in a scissor style, and was eventually extinct under Borneo.