Ground gravity survey for regional structure unit delineation and oil and gas exploration in China is addressed in this paper with examples. Gravity survey scales, coverage, technical regulations and achievements at the national level are introduced, including data processing and anomaly interpretation techniques. Bouguer anomalies of terrestrial territories of China and classification of anomalous feature zones are also described; they are well correlated with lithotectonical boundaries, fault zones, and unexposed igneous plutons. Recent study results of petroliferous sedimentary basins are presented as well, including concealed boundaries and major structures of large basins. It is concluded that gravity survey is the most effective and economic method in unveiling unexposed and deep-seated structures, targeting and delineating oil and gas-bearing sedimentary basins, and locating main trap structures within prolific basins in early stage of exploration in China. Suggestions for improving exploration of both conventional and unconventional oil and gas reservoirs in China are also given in the paper.

The advancement of geological big data has rendered data-driven methodologies increasingly vital in Mineral Prospectivity Mapping. The effective integration of quantitative and qualitative data, including experiential and knowledge-based insights, is crucial in geological data fusion. Specifically, the conversion of raw data into samples and the selection of predictive methods are two core issues that constitute the focus of this study. Traditional clustering methods require the user to specify the number of clusters in advance. The two-step clustering can automatically determine the clustering result ‘k’ while analyzing both continuous and categorical variables, by building a Cluster Feature (CF) and using information criteria to merge nodes. In this study, we conducted an analysis utilizing stream sediment element data, residual gravity anomalies, and fault distribution through the two-step clustering method. Factor analysis (FA) was employed to reduce 16 elemental variables from stream sediments into five uncorrelated continuous variables; additionally, residual gravity anomalies were transformed from continuous to categorical variables via an interval-based method before being combined with fault distribution, resulting in seven variables for clustering. The research findings indicate that categorical variables significantly influence clustering results; concurrently, as the importance of continuous variables within the cluster increases, so does k. When only one categorical variable is present, residual gravity anomalies show significantly better clustering than fault distribution; however, when two categorical variables are involved, it is essential to consider the quantity of categories: more categories lead to poorer quality. The results from the Jiaolai Basin’s northeastern margin indicate a significant correlation with known gold deposits; two-step clustering is a promising and effective method for improving mineral prospecting efforts.

In this work, polymer-bonded sugars were used as a substitutive material for polymer-bonded explosives (PBXs). Their production process and damage mechanism under compressive process were analysed. We also investigated the initial Poisson’s ratio of PBXs as well as their mechanical properties under quasi-static compression and variation of Poisson’s ratio during the one-dimensional compressive process. Compressive expansion behaviour due to compressive loading was investigated comprehensively. It was found that due to the damage caused by the loading process, volumetric strain and Poisson’s ratio showed three stages characteristic. Experimental results proved that compression–expansion had occurred during the one-dimensional compressive process. Simulation results verified that PBXs crystal underwent mainly transgranular fracture under quasi-static compression.

Mining and analyzing information from multiple sources—such as geophysics and geochemistry—is a key aspect of big data-driven mineral prediction. Clustering, which groups large datasets based on distance metrics, is an essential method in multidimensional data analysis. The Two-Step Clustering (TSC) approach offers advantages by handling both categorical and continuous variables and automatically determining the optimal number of clusters. In this study, we applied the TSC method to mineral prediction in the northeastern margin of the Jiaolai Basin by: (i) converting residual gravity and magnetic anomalies into categorical variables using Ward clustering; and (ii) transforming 13 stream sediment elements into independent continuous variables through factor analysis. The results showed that clustering is sensitive to categorical variables and performs better with fewer categories. When variables share similar distribution characteristics, consistency between geophysical discretization and geochemical boundaries also influences clustering results. In this study, the (3 × 4) and (4 × 4) combinations yielded optimal clustering results. Cluster 3 was identified as a favorable zone for gold deposits due to its moderate gravity, low magnetism, and the enrichment in F1 (Ni–Cu–Zn), F2 (W–Mo–Bi), and F3 (As–Sb), indicating a multi-stage, shallow, hydrothermal mineralization process. This study demonstrates the effectiveness of combining Ward clustering for variable transformation with TSC for the integrated analysis of categorical and numerical data, confirming its value in multi-source data research and its potential for further application.