Ground-penetrating radar (GPR) has emerged as a promising technology for estimating the soil water content (SWC) in the vadose zone. However, most current studies focus on partial GPR data, such as travel-time or amplitude, to achieve SWC estimation. Full waveform inversion (FWI) can produce more accurate results than inversion based solely on travel-time. However, it is subject to local minima when using a local optimization algorithm. In this paper, we propose a novel and powerful GPR waveform inversion scheme based on Harris hawks optimization (HHO) algorithm. The proposed strategy is tested on synthetic data, as well as on field experimental data. To further validate our approach, the results of the HHO algorithm are also compared with those of partial swarm optimization (PSO) and grey wolf optimizer (GWO). The inversion results from both synthetic and real experimental data demonstrate that the proposed inversion scheme can efficiently invert both SWC and layer thicknesses, thus achieving very fast convergence. These findings further confirm that the HHO algorithm can be effectively applied for the quantitative interpretation of GPR data.

Volcanic rock reservoirs have received extensive attention from scholars all over the world because of their geothermal, mineral, and oil and gas resources. Shear wave velocity is the essential information for AVO (amplitude variation with offset) analysis and the reservoir description of volcanic rocks. However, due to factors such as cost, technical reasons, and so on, shear wave velocity is not provided in many logging data. This paper proposes a shear wave velocity prediction method suitable for the conventional logging of volcanic rocks. Firstly, the Xu–White model is improved. The probability distributions formed by the prior information of the logging area are used to initialize the key petrophysical parameters in the model instead of the fixed parameter value to establish the statistical petrophysical model between the logging curve and shear wave velocity. Then, based on the Bayesian inversion method, the simulated P-wave velocity is matched with the actual P-wave logging data to calculate the key petrophysical parameters, and is then used for S-wave velocity prediction. The method is applied to the actual logging data of the No. 5 structure in Nanpu Sag, eastern China. The prediction effect of shear wave velocity is better than that of the conventional method, indicating the feasibility and effectiveness of this method. This study will provide more accurate shear wave velocity data for the exploration and development of volcanic reservoirs.

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.

Traveltime picking accuracy is frequently affected by incoherent or random data noise. Within this context, we put forth a new denoising method called iterative interferometric denoising filtering. This method leverages the pseudo-Wigner distribution function to capture the offset and time-symmetric patterns of source wavelets convolved in seismic signals. Incoherent or random noises without this characteristic are eliminated via this approach. The processed data have waveform information distortion and more frequency components. However, the traveltime information can be considered correct, and the improved signal-to-noise ratio makes traveltime picking much more convenient. Our method’s practical applications in a synthetic and in two field datasets show that this technology can increase the signal-to-noise ratio, and the picked traveltime information can be used in traveltime tomography. These two field datasets were collected near the Aqaba Gulf and the Qademah fault, located in King Abdullah Economic City.

Monte Carlo-based sampling methods (MCMC) can be used to solve inverse problems affecting ground penetrating radar (GPR) data. However, due to their high computational complexity, they have not been widely used in practical applications. This article uses neural network methods to replace the computationally complex forward problem of Monte Carlo methods. However, the neural network method is an approximation of the accurate formula method, and this may introduce model errors. In order to reduce the impact of model errors, in this study, we incorporate the Squeeze-and-Excitation (SE) attention mechanism into Convolutional Neural Networks (CNN) to further improve the accuracy of the network. Moreover, with the statistical advantages of the MCMC method, model errors can be explained during the inversion process, further reducing their impact. We apply the proposed method to solve the inversion problem of crosshole ground-penetrating radar travel time data. Compared with commonly used approximate forward models, the method proposed in this paper has better accuracy. The results of data experiments indicate that this method can effectively invert the velocity of underground media.