To further explore the geological structure and the Mesozoic–Paleozoic hydrocarbon prospects in the northern area of the South Yellow Sea (SYS), multiple geological and geophysical data were systematically gathered and compiled, including gravity and magnetic data, seismic surveys, drilling data, and previous research results. The characteristics and genesis of the gravity and magnetic anomalies are examined. This study employs residual gravity anomalies and multiple edge detection methods to identify fault lineament structures and assess the tectonic framework. Moreover, the study utilizes 2.5D gravity-seismic joint modellings and regression analysis to estimate the basement depth. Additionally, the study examines the basement characteristics and discusses the thickness of the Mesozoic–Paleozoic strata. Finally, the study further identifies prospects for hydrocarbons in the Mesozoic–Paleozoic. Our findings show that the faults are incredibly abundant and that the intensity of fault activity weakens gradually from NW to SE. Specifically, NE (NEE) trending faults are interlaced and cut off by NW (NWW), near-EW, and near-SN trending secondary faults, which form an en-echelon composite faults system with a dominant NE (NEE) orientation. Thick Mesozoic–Paleozoic strata are preserved, but we observe distinct variations in basement characteristics and the pre-Cenozoic structural deformation along the N-S direction. Therefore, the Northern Basin of SYS (NBSYS) and the Middle Uplift of SYS (MUSYS) are characterized by alternating sags and bulges in the S-N direction and in the E-W direction, respectively, forming a chessboard tectonic framework. Considering the oil and gas accumulation model, we identify three target hydrocarbon prospects in the NBSYS and two favorable hydrocarbon prospects in the MUSYS.

Magnetic surveying encounters challenges in high-precision detection and inversion on strongly magnetized bodies. Nonuniform magnetization caused by self-demagnetization and mutual magnetization makes magnetic field calculation using analytical formulas impractical. The previous discrete numerical methods have suffered from computational inefficiency or low accuracy. To resolve this issue, we develop an algorithm to solve the magnetic field integral equation for calculating the magnetization state of high-susceptibility bodies, combining the improved spatial convolution forward approach with the adaptive relaxation iteration method. To address the excessive computational burden in the magnetization field between 3D voxel arrays of the discrete model, we construct the circular convolution kernel matrix directly when the subsurface space is divided with an equidistant grid, significantly reducing redundant computations. Then, the fast Fourier transform is used to accelerate the forward discrete circular convolution operation. In addition, we derive an iterative computation scheme that adaptively adjusts the relaxation factor based on magnetic field changes to correct the effective magnetization for algorithm convergence. From a pragmatic perspective, equating the cuboid to equal-volume sphere subdivisions is developed to enhance computational efficiency further by approximately 50% despite sacrificing slight accuracy. The sphere and spherical shell models validate the algorithm accuracy, efficiency, and convergence. Furthermore, we analyze the characteristics of the mutual magnetization effect among magnetic bodies under different magnetization conditions. Finally, the applicability of the algorithm is demonstrated with a realistic example. Our algorithm shows promise for precisely exploring highly magnetized minerals, underground pipelines, and unexploded ordnance.

Euler deconvolution is a popular technique used for analyzing potential field data because it requires little prior information. However, the reliability of Euler solutions can be impacted by interference from adjacent field sources, or background fields. In this manuscript, we present an effective Euler deconvolution algorithm that accounts for linear background fields. Our algorithm, called improved finite-difference Euler deconvolution, builds upon the finite-difference method and is less susceptible to interference from nearby sources. We use this algorithm to achieve a joint estimation of the coefficients of the source coordinates, the structure index, and the linear background trend. Compared to Euler deconvolution methods based on differential similarity transformations, which also account for linear background fields, our method is easier to understand and implement programmatically and is faster. We tested our method using both 2D and 3D synthetic data, and the results indicate that our algorithm has better computational accuracy than the finite-difference algorithm and is comparable to the Euler deconvolution algorithm based on differential similarity transformations. In addition, our method was shown to be effective when tested on real data.

The absence of regional electrical data has constrained our comprehension of the deep geological structures in the Jizhong Depression. This limitation has impeded the exploration of factors and principles governing geothermal reservoirs. Historically, studies on these factors primarily centered on geothermal field attributes, such as anomalous geothermal flux, geothermal gradients, and deep Moho variations. In our research, we undertook an exhaustive interpretation of extended-period magnetotelluric readings. This was amalgamated with prior regional geological and geophysical studies to discern deep geological structural details pertinent to geothermal resource components, encompassing heat sources, pathways, reservoirs, and cap rocks. Our analysis spanned the foundational surface of the Cenozoic era, fault configurations, carbonate formation distribution, and layers with low velocity yet high conductivity. We also statistically evaluated geothermal wells, their density, and the water temperature attributes across various structural entities. This aided in comprehending how structural units influence thermal well features. Our research delineated the spatial interrelation between geothermal well distribution and deep geological structures, including carbonate rock distribution, Cenozoic thickness, fault patterns, and profound low-velocity, high-conductivity layers. Moreover, we scrutinized how structural unit types impact thermal well attributes, offering insights into the formation principles of deep carbonate geothermal reservoirs within the Jizhong Depression. Our findings suggest that the genesis of deep geothermal resources in the Jizhong Depression is swayed by regional geological conditions. Notably, the distribution of regional and especially deep geothermal wells is intimately tied to regional stratigraphy, structural designs, and profound geological structural traits. Furthermore, the temperature within geothermal wells correlates strongly with the burial depth, carbonate formation thickness, Cenozoic thickness, and proximity to fault channels responsible for fluid movement and heat transmission.