This paper investigates leachate leakage of a typical valley-type landfill in South China and health risk of groundwater pollution. Through geophysical detection on landfill, chemical analysis of 19 parameters such as pH, total dissolved solids (TDS), total hardness (TH), potassium permanganate index (COD_Mn), ammonia nitrogen (NH₃–N), sulfate (SO₄²⁻), chloride (Cl⁻), fluoride (F⁻), nitrate (NO₃–N), nitrite (NO₂–N), and heavy metals (Hg, Fe, Mn, Cu, Zn, Cr, Pb and Cd) in groundwater, and model simulation, the prediction of pollution resource and risk level is achieved. This aggregated approach aims to effectively manage and control groundwater contamination and public health risks from the source. The results of transient electromagnetic method showed that four leakage areas of impermeable layer were existed in the landfill, with an area of 336.8 m² and a depth of 15–22 m. The chemical analysis and pollution assessment revealed that groundwater at ZK01 and ZK04 were heavily polluted, ZK02 and ZK03 were slightly polluted, and ZK05 was non-polluted. Water quality of points ZK01–04 exceeded the standard value of Class III water in the Groundwater Quality Standard (GB/T 14,848-2017), and the main excessive parameters are pH, NH₃–N, Mn and Fe. The landfill leakage, rock weathering dissolution and water–rock interaction possibly were the main sources of groundwater pollution through correlation analysis (CA) and principle component analysis (PCA). Numerical simulation based on the RBCA (Risk-based Corrective Action) model thought Mn and NO₃–N had adverse non-carcinogenic effects o human health risk. Assuming that no pollution control measures are taken, the average increase rate of hazard index (HI) for pollutants within the past 20 years was between 0.04 and 0.08/a, and the average expansion rates of the risk area were 341–432 m²/a. The expansion rates of risk area along the groundwater runoff were 1.6–3.8 m/a, drinking water safety downstream of main pollution source runoff is the focus of protection.

The settlement and deformation of abandoned mining tunnels can lead to cracking, deformation, or even the collapse of surface structures. Recently, a dual-direction, four-lane expressway, designed a speed of 100 km/h, is planned to be constructed between Yuanling County and Chenxi County. This expressway will pass through a long-abandoned refractory clay mining area in Chenxi County. This study focuses on this abandoned mining area and employs the Electrical Resistivity Tomography (ERT) method to investigate the underground conditions, aiming to determine the location and scale of the subterranean goaf. A total of five survey lines were deployed for the investigation. The inversion results indicate the presence of five low-resistivity anomalies in the underground structure (with six low-resistivity anomalies identified along line L1). These low-resistivity anomalies are preliminarily interpreted as subsurface cavities. Subsequent borehole verification revealed that the five low-resistivity anomalies correspond to a total of eight water-filled cavities, including six abandoned mining tunnels and two karst caves. At the location K33+260~K33+350, a large low-resistivity anomaly was identified which actually consisted of three closely spaced water-filled abandoned mining tunnels. Additionally, the surrounding strata primarily consisted of fractured mudstone, which has a high water content and thus exhibits low resistivity. These two factors combined resulted in the three water-filled abandoned mining tunnels appearing as a single large low-resistivity anomaly in the inversion profile. Meanwhile, at K33+50~K33+110, two water-filled abandoned mining tunnels were found. These tunnels are far apart along line L1 but are relatively close to each other on the other four survey lines. Consequently, in the inversion results, line L1 displays these as two separate low-resistivity anomalies, while the other four survey lines show them as a single large low-resistivity anomaly. Based on the 2D inversion results, a 3D model of the study area was constructed. This model provides a more intuitive visualization of the underground cavity structures in the study area. The findings not only serve as a reference for the subsequent remediation of the goaf area but also offer new insights into the detection of abandoned mining tunnels.

To investigate deep Earth information, researchers often utilize geomagnetic observatories and satellite data to obtain the conversion function of geomagnetic sounding, C-response data, and employ traditional inversion techniques to reconstruct subsurface structures. However, the traditional gradient-based inversion produces geophysical models with artificial structure constraint enforced subjectively to guarantee a unique solution. This method typically requires the model parameterization knowledge a priori (e.g., based on personal preference) without uncertainty estimation. In this paper, we apply an efficient trans-dimensional (trans-D) Bayesian algorithm to invert C-response data from observatory and satellite geomagnetic data for the electrical conductivity structure of the Earth’s mantle, with the model parameterization treated as unknown and determined by the data. In trans-D Bayesian inversion, the posterior probability density (PPD) represents a complete inversion solution, based on which useful inversion inferences about the model can be made with the requirement of high-dimensional integration of PPD. This is realized by an efficient reversible-jump Markov-chain Monte Carlo (rjMcMC) sampling algorithm based on the birth/death scheme. Within the trans-D Bayesian algorithm, the model parameter is perturbated in the principal-component parameter space to minimize the effect of inter-parameter correlations and improve the sampling efficiency. A parallel tempering scheme is applied to guarantee the complete sampling of the multiple model space. Firstly, the trans-D Bayesian inversion is applied to invert C-response data from two synthetic models to examine the resolution of the model structure constrained by the data. Then, C-response data from geomagnetic satellites and observatories are inverted to recover the global averaged mantle conductivity structure and the local mantle structure with quantitative uncertainty estimation, which is consistent with the data.

The Tieshilong Pb-Zn polymetallic district is located along the northern margin of the Dongpo ore field in the middle section of the Nanling metallogenic belt, southern Hunan, China. It represents a typical granite-related, fault-controlled hydrothermal vein-type lead–zinc polymetallic deposit in southern Hunan. In recent years, large-scale tin mineralization has been newly discovered during exploration in the deeper and peripheral areas of the district. However, the timing and genetic nature of this tin mineralization remain undetermined, which limits understanding of the characteristics of the deposit’s metallogenic system and its deep exploration potential. In this study, we present in situ cassiterite U–Pb geochronology and trace element data from deep Pb-Zn-Sn orebodies in the Tieshilong mining district. LA-ICP-MS U-Pb analyses of cassiterite yield a Tera–Wasserburg lower-intercept age of 159.2 ± 6.2 Ma (MSWD = 1.4), indicating that Sn mineralization occurred during the Late Jurassic. This age overlaps, within uncertainty, with the main ca. 160~150 Ma W–Sn metallogenic event recognized throughout the Nanling belt. Trace element data reveal that Tieshilong cassiterite is enriched in Fe (1100–5800 ppm) and W (120–11,660 ppm), and depleted in Nb (0.1–87 ppm) and Ta (0–7.1 ppm). The Zr/Hf ratios range from 23 to 52, with a mean value of approximately ~36, which is close to the chondritic values. These geochemical signatures, together with the occurrence of cassiterite intergrown with hydrothermal quartz and its replacement by later sulfides, support precipitation from a granite-related magmatic–hydrothermal system. Based on the findings and the literature, the Tieshilong deposit is therefore interpreted as a Pb–Zn-dominant expression of a Late Jurassic granite-related polymetallic system, in which deeper Sn ± W mineralization was overprinted by later Pb–Zn–Cu sulfide mineralization along fault-controlled fluid pathways. The recognition of cassiterite-bearing, medium- to high-temperature assemblages at depth suggests that down-dip extensions, fault intersections, and strike-inflection zones of the ore-controlling structures represent priority targets for future exploration.

The traditional gradient-based inversion produces geophysical models with artificial structure constraint enforced subjectively to guarantee a unique solution.This method typically needs the model parameterization knowledge a priori (e.g.,based on the personal preference) without uncertainty estimation. In this paper, we apply an efficient trans-dimensional (trans-D) Bayesian algorithm to invert C-response data from observatory and satellite geomagnetic data for the electrical conductivity structure of the Earth’s mantle, with the model parameterization treated as unknown and determined by data itself. In trans-D Bayesian inversion, the posterior probability density (PPD) represents a complete inversion solution, based on which useful inversion inference about the model can be made with the requirement of high-dimensional integration of PPD. This is realized by an efficient reversible-jump Markov-chain Monte Carlo (rjMcMC) sampling algorithmbased on Birth/Death scheme. Within the trans-D Bayesian algorithm, the model parameter is perturbated in the principal-component parameter space to minimize the effect of inter-parameter correlations and improve the sampling efficiency. A parallel tempering scheme is applied to guarantee the complete sampling of the multiple model space. Firstly, the trans-D Bayesian inversion is applied to invert C-response data from two synthetic models to examine the resolution of model structure constrained by the data. Then C-response data from geomagnetic satelliteand observatory are inverted to recover the global averaged mantle conductivity structure and the local mantle structure with quantitative uncertainty estimation,consistent with the data.