Groundwater in basalt-covered areas primarily occurs within pore zones, cooling contraction fractures, primary joints, tectonic fractures, and weathered layers, resulting in a highly heterogeneous and discontinuous distribution pattern of the aquifers. This work investigates the groundwater system in the Changbai Mountain basalt region using electrical resistivity tomography (ERT). The study area is located in Baili Village, Longcheng Town, Helong City, Jilin Province, where three parallel survey lines of ERT were deployed. The subsurface electrical model of the study area was obtained using a smooth-constrained leastsquares inversion method. The results revealed the presence of a localized shallow aquifer and a regionally developed fault zone. The regional fault zone plays a dominant role in controlling shallow groundwater migration and serves as a pathway for groundwater accumulation. The findings demonstrate that ERT is an effective tool for revealing the spatial distribution patterns of discrete groundwater in basalt-covered areas.

Electrical resistivity tomography (ERT) and seismic refraction tomography (SRT) are two important near-surface geophysical imaging methods. The resistivity is highly sensitive to factors such as water content and porosity, whereas the seismic velocity provides high-resolution imaging of layer interfaces and velocity structures. Due to the strong heterogeneity and multi-scale structural characteristics of near-surface media, as well as environmental noise and human activity interference, a single method is prone to generating multiple solutions and resulting in low imaging accuracy, making it difficult to accurately characterize complex nearsurface structures. In this study, the authors propose an ERT and SRT joint inversion method based on the ensemble Kalman filter (EnKF), which enables the jointy constrained inversion of resistivity and velocity and provides uncertainty quantification results. By introducing structural consistency constraints during the inversion update process, the inversion accuracy of the resistivity and velocity models at layer interfaces and anomalous structures is improved, enhancing interface continuity. Model testing and field data from the Northeast Black Soil Test Field demonstrate that the proposed EnKF joint inversion strategy can effectively improve imaging resolution, providing a reliable framework for geophysical imaging and interpretation in complex near-surface applications.

Monitoring air quality is a core scientific issue in atmospheric environmental research. Based on the hourly air quality index (AQI) data from 298 monitoring stations, a multi-scale analysis of air quality was conducted in the middle–lower reaches of the Yellow River from June 2016 to May 2020. Methodologically, unified quality control and missing-data imputation were applied to the hourly AQI records, after which annual, seasonal, and monthly mean AQI values were calculated using arithmetic averaging. The Kriging interpolation was applied to map multi-temporal spatial patterns, while the coefficient of variation and global/local spatial autocorrelation were used to quantify spatial disparity and clustering of AQI. Then, the effects of natural and socioeconomic factors were identified and cross-validated using the geographical detector and multiple linear regression. The results show that AQI exhibits a pronounced downward trend over time, with clear seasonal characteristics and cyclical variations at the monthly scale; spatially, AQI has long displayed a pattern of higher values in downstream and lower values in midstream, with high-concentration areas mainly clustered in the adjacent regions of Henan, Shandong and Shanxi provinces; significant positive spatial autocorrelation is observed at all temporal scales, with stable high–high and low–low clustering patterns; population density, civilian vehicle ownership, and industrial dust emissions show the strongest explanatory power, while air temperature and afforestation area exert moderating effects on AQI variations. This study is of potential significance to cross-provincial coordinated control and refined air pollution management.

Low-porosity and low-permeability reservoirs are developed in the Enping Formation and Wenchang Formation in the Lufeng Sag, the Pearl River Mouth Basin. Due to their complex pore structure and strong heterogeneity, conventional porosity-permeability relationship models exhibit low accuracy in permeability calculation, making it difficult to meet the actual production needs such as reservoir effectiveness identification and productivity prediction. Starting from the generation principle of porosity spectrum in electrical imaging, and combining with core experiments and forward simulation results, the authors systematically compared the differences between nuclear magnetic resonance (NMR) T2 spectra and porosity spectrum generated by conventional methods, deeply analyzed the reasons for the shortcomings of conventional porosity spectrum in characterizing rock pore structure, and proposed an improved porosity spectrum construction method based on pore volume statistics. The improved porosity spectrum shows excellent consistency with the NMR T2 spectrum, which can more accurately reflect the pore structure characteristics of the reservoir. On this basis, the pseudo-NMR Schlumberger-Doll Research (SDR) model is used for reservoir permeability evaluation, which significantly improves the calculation accuracy over conventional methods. It can better reflect the advantage of high resolution compared to nuclear magnetic logging, providing reliable data support for fine reservoir description and productivity prediction.

Classical travel-time tomography struggles to resolve the heterogeneity within the medium. To address this challenge, this paper proposes a tomography method based on Hamiltonian Monte Carlo (HMC) sampling. In this method, forward modeling utilizes the fast marching method (FMM), governed by the Eikonal equation. For inversion, velocity parameters are expressed as probability distributions, and samples representing these parameters are obtained by employing a Markov chain. This chain is generated from the distributions. The Markov chain is controlled by an artificial Hamiltonian system. In this system, the models are treated as high-dimensional particles. These particles advance through trajectories in the extended phase space. HMC uses the derivatives of the forward equations to enable long-distance transitions between models. This approach enhances sample independence and maintains a high acceptance rate. Results demonstrate that this tomography method can accurately invert the position and shape of velocity anomalies, as well as the heterogeneity in stochastic medium models. This method provides a novel approach for seismic tomography. It can accurately characterize subsurface structures and is valuable for seismic exploration and geophysical studies.