Mineral resources in Asia continent and its mining industry play a significant role in the economic growth and industrialization of both Asia and the world. Asia continent boasts the most comprehensive kinds of minerals, with reserves of at least 38 of over 80 widely used minerals worldwide accounting for more than 30% of the global total reserves. Asia continent experienced three main tectonic evolution and mineralization stages: The Precambrian, the Paleozoic, and the Mesozoic to Cenozoic. The abundant mineral resources in this continent can be divided into seven first-order metallogenic belts (metallogenic domains), 18 second-order metallogenic belts (metallogenic provinces), 61 third-order metallogenic belts (metallogenic zones), and nine main minerogenetic series. Asia continent exhibits the most significant metallogenic specialization among all continents. Specifically, granite belts of Asia continent manifest pronounced metallogenic specialization of tin, rare metals, and porphyry Cu-Au-Mo deposits. Its maficultramafic rock belts and ophiolite belts display notable metallogenic specialization of lateritic nickel deposits and magmatic type chromite deposits, while its Mesozoic to Cenozoic basalt belts show remarkable metallogenic specialization of lateritic bauxite deposits. Consequently, many giant metallogenic belts were formed, including the Southeast Asian tin belt, the Qinghai-Xizang Plateau rare metal metallogenic belt, the Tethyan porphyry Cu-Au-Mo metallogenic belt, the circum-Pacific porphyry Cu-Au-Mo metallogenic belt, the Southeast Asian lateritic bauxite metallogenic belt, the Deccan Plateau lateritic bauxite metallogenic belt in India, the Southeast Asian lateritic nickel metallogenic belt, and the Tethyan magmatic type chromite metallogenic belt—all of which are significant metallogenic belts in Asia continent. Future mineral exploration in Asia should focus primarily on the Precambrian mineralization of ancient cratons, the Paleozoic mineralization of the Central Asian-Mongolian orogenic belt, and the Mesozoic to Cenozoic mineralization of the Tethyan and circum-Pacific mobile belts. Asia's mining industry not only underpins its own economic growth but also propels global economic development and industrialization, contributing significantly to the world economy. Asia boasts the highest production value of minerals, the largest annual production of minerals, and the greatest trade value of mineral products among all the continents, having emerged as the trade center of global mineral products and the center of the mining industry economy. China is identified as one of the few countries that possess the most comprehensive kinds of minerals, and its mining industry has supported and driven the economic development and industrialization of Asia and even the world. Standing as the largest mineral producer worldwide, China ranked first in the production of 28 mineral commodities in the world in 2022. Besides, China exhibits the highest annual production value of minerals and the largest trade value of mineral products among all countries. Therefore, China's demand for global mineral products influences the global supply and demand patterns of minerals and the world economic situation.
Hydraulic stimulation technology is widely employed to enhance the permeability of geothermal reservoirs. Nevertheless, accurately predicting hydraulic fracture propagation in complex geological conditions remains challenging, thereby hindering the effective utilization of existing natural fractures. In this study, a phase field model was developed utilizing the finite element method to examine the influence of fluid presence, stress conditions, and natural fractures on the initiation and propagation of hydraulic fractures. The model employs Biot's poroelasticity theory to establish the coupling between the displacement field and the fluid field, while the phase field theory is applied to simulate fracture behavior. The results show that when σx0/σy0 < 3 or qf < 20 kg/(m3·s), the presence of natural fractures can alter the original propagation direction of hydraulic fractures. Conversely, in the absence of these conditions, the propagation path of natural fractures is predominantly influenced by the initial stress field. Furthermore, based on the analysis of breakdown pressure and damage area, the optimal intersection angle between natural fractures and hydraulic fractures is determined to range from 45° to 60°. Finally, once a dominant channel forms, initiating and propagating hydraulic fractures in other directions becomes increasingly difficult, even in highly fractured areas. This method tackles the challenges of initiating and propagating hydraulic fractures in complex geological conditions, providing a theoretical basis for optimizing Enhanced Geothermal System (EGS) projects.
Effective groundwater management is crucial for economic sustainable development, particularly as climate change and population growth increase the uncertainty of aquifer dynamics. Due to limited geological data, Punjab's complex hydrogeological conditions and Quaternary alluvial deposits present significant challenges for groundwater management. This study employs cost-effective numerical techniques as alternatives to traditional methods to safeguard groundwater quality, quantity, and accessibility. It introduces an edit-embedded transition frequency model that integrates regional datasets and utilizes algorithms such as GAMEAS, MCMOD, and TSIM to evaluate aquifer heterogeneity and simulate spatial variations using one-dimensional and three-dimensional Markov chains. Findings show that sand exhibits the highest self-transition (33.112 m), indicating strong stability, followed by silt, clay, and gravel, suggesting overall hydrofacies stability both horizontally and vertically. The model's predictions are largely consistent with actual material distribution, with a slight under-prediction of clay (−0.750%) and an over-prediction of sand (2.985%), which accounts for 58.77% of the aquifer material. It also highlights significant heterogeneity in the northern mountainous regions and minor variations in the south. The study emphasizes Punjab's severe water crisis, with groundwater reserves of 3502.3 BCM, declining water levels (0.38-33.62 m), and low hydraulic conductivity, urging government action on rainwater harvesting and sustainable groundwater management policies.
With the efficient and intelligent development of computer-based big data processing, applying machine learning methods to the processing and interpretation of logging data in the field of geophysical well logging has broad potential for improving production efficiency. Currently, the Jiyuan Oilfield in the Ordos Basin relies mainly on manual reprocessing and interpretation of old well logging data to identify different fluid types in low-contrast reservoirs, guiding subsequent production work. This study uses well logging data from the Chang 1 reservoir, partitioning the dataset based on individual wells for model training and testing. A deep learning model for intelligent reservoir fluid identification was constructed by incorporating the focal loss function. Comparative validations with five other models, including logistic regression (LR), naive Bayes (NB), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM), show that this model demonstrates superior identification performance and significantly improves the accuracy of identifying oil-bearing fluids. Mutual information analysis reveals the model's differential dependency on various logging parameters for reservoir fluid identification. This model provides important references and a basis for conducting regional studies and revisiting old wells, demonstrating practical value that can be widely applied.
This study investigated the heterogeneous responses of organic matter (OM) in highly- to over-mature source rocks during thermal maturation. An integrated analysis was conducted on the Raman spectroscopic and geochemical signatures of shales from the Lower Silurian Longmaxi Formation and the Lower Cambrian Qiongzhusi Formation, as well as anthracites from the Lower Permian Shanxi-Formation and the Upper Carboniferous Taiyuan Formation (collectively referred to as the Shanxi Taiyuan Formations). Additionally, burial and thermal evolution modeling was employed to support the analysis. A systematic assessment of Raman spectral parameters (e.g., the positions and intensity ratio of the D and G bands) revealed robust correlations between the thermal history patterns of source rocks and molecular structural evolution parameters. The subsequent mechanistic quantification demonstrated that the maturation state of the source rocks was subjected to the hierarchical control of three principal factors: Peak heating temperature, the duration of sustained thermal intensity, and effective maturation duration. In addition, comparative analyses demonstrated that the anthracites attained higher structural ordering under sustained thermal conditions. This contrasts with the disordered carbon matrices observed in the intermittently heated shales. Raman spectroscopy further revealed broader variations in the D and G band intensities of the Longmaxi Formation compared to the Qiongzhusi Formation. This difference is associated with their different thermal histories. The thermal burial histories confirm that shales in the Longmaxi Formation underwent thermal exposure at lower peak temperatures over a shorter duration compared to those in the Qiongzhusi Formation. Finally, this study established a maturity calibration model for over-mature source rocks through a systematic correlation between Raman peak height ratios (RD/G) and vitrinite reflectance (Ro).
The Neogene Shawan Formation in the Chepaizi Uplift of the Junggar Basin (NW China) has obtained high oil flow, demonstrating a good potential for oil and gas exploration. The multi-source hydrocarbon generation background and strong tectonic activity have led to the simultaneous production of heavy oil and light oil from multi-layer in the area, which makes it very difficult to identify oil origins, presently, the hot debate on the oil origins needs to be clarified. In this paper, due to the selective consumption of different types of compounds in crude oils by severe and intense biodegradation, the commonly used oilsource correlation tools are ineffective or may produce misleading results, this study adopted a biomarker recovery method based on the principle of mass conservation that uses the sum of the mass of the residual biomarkers and their corresponding biodegradation products to obtain the mass of the original biomarkers, improving the reliability of oil origins determination. Based on the nature and occurrence of crude oils, the investigated oils are subdivided into three types, Group A, Group B and Group C. Group A, light oils occurred mainly in lower structure Neogene Shawan Formation in the western Chepaizi Uplift, while Group B, heavy oils occurred mainly in higher structure Neogene Shawan Formation in the western Chepaizi Uplift. The two types of crude oils may come from the mixed source of Jurassic Badaowan Formation source rocks (J1b) and Paleogene Anjihaihe Formation source rocks (E2-3a) in the Sikeshu Sag, and Jurassic Badaowan Formation source rocks (J1b) are the main source of crude oils. Group C, heavy oils occurred mainly in Neogene Shawan Formation in the eastern Chepaizi Uplift, showing good correlation with the Permian (P1f and P2w) source rocks in the Shawan Sag. At the same time, by combining stable carbon isotope and parameters related to triaromatic steroids, the accuracy of the oilsource correlation results by biomarker recovery method was further verified.
The Shenshan Group provides important geological information which is vital in unraveling the amalgamation and subsequent rifting processes of South China. While conventional studies have asserted its formation in a subduction setting, the distinct investigation reveals the necessity for reassessment. To address this, the authors employ integrated methods encompassing petrological, zircon U-Pb geochronological, Lu-Hf isotopic and geochemical methods for sedimentary rocks from the upper Shenshan subgroup and Banxi Group. The geochemical results indicate that they were formed through the recycling deposition of intermediate-acidic igneous source material and experienced moderate chemical weathering. Additionally, both sedimentary sequences exhibit characteristics consistent with those formed in an intracontinental extensional rift setting since ca. 810 Ma. The provenance analysis indicates that the upper Shenshan subgroup primarily originates from the Yangtze Domain, while the Banxi Group from both the Yangtze and Cathaysia domains. Synthesizing with previous studies, the Shenshan Group should be subdivided into the lower and upper subgroups which represent distinct tectonic backgrounds. The lower subgroup is inferred to have formed in an Early Neoproterozoic fore-arc setting, akin to the Zhoutan group. The upper subgroup corresponds to the Banxi Group, representing the Middle Neoproterozoic postorogenic rift setting, responding to the breakup of Rodinia.
The links between the adakitic rocks and Cu-Au mineralization have long been argued. This study investigates petrogenesis and its link to mineralization potential by a series of in-situ geochronological and geochemical signatures of apatite and zircon in three ore-related intrusions and one-barren intrusion in the Middle-Lower Yangtze River Metallogenic Belt (MLYRB). Zircon U-Pb dating yield ages of 139-143 Ma and 121 Ma for the ore-related and ore-barren intrusions, respectively. The ore-related rocks have higher apatite Sr/Y (1.57-9.69), (La/Yb)N (16.7-159.5), and δEu (0.45-0.74) than the ore-barren rocks of 0.57-1.02, 19.3-24.1 and 0.40-0.45, respectively, indicating the former has an adakitic affinity, while the latter has a non-adakitic affinity. The ore-related rocks have enriched zircon Hf isotopes with εHf(t) values of −15.9 to −5.5 and TDMC ages of 2408-1655 Ma and apatite Sr-Nd isotopes, indicating that the ore-related magmas were mainly originated from partial melting of subducted oceanic crust. The orebarren rocks have higher εHf(t) values of −6.6 to −4.6 and lower TDMC ages of 1598-1469 Ma and apatite Sr-Nd isotopes, indicating a lithospheric mantle source. The ore-related rocks have higher oxygen fugacity of mean ΔFMQ+2.00 and XF/XOH of 8.36-175 than the ore-barren rocks of mean ΔFMQ+1.43 and 3.72-4.96. It was inferred that magma source, water content, and oxygen fugacity emerge as critical factors governing the regional Cu-Au mineralization potential.
This study developed a modeling methodology for statistical optimization-based geologic hazard susceptibility assessment, aiming to enhance the comprehensive performance and classification accuracy of the assessment models. First, the cumulative probability method revealed that a low probability (15%) of geologic hazards between any two geologic hazard points occurred outside a buffer zone with a radius of 2297 m (i.e., the distance threshold). The training dataset was established, consisting of negative samples (non-hazard points) randomly generated based on the distance threshold, positive samples (i.e., historical hazards), and 13 conditioning factors. Then, models were built using five machine learning algorithms, namely random forest (RF), gradient boosting decision tree (GBDT), naive Bayes (NB), logistic regression (LR), and support vector machine (SVM). The comprehensive performance of the models was assessed using the area under the receiver operating characteristic curve (AUC) and overall accuracy (OA) as indicators, revealing that RF exhibited the best performance, with OA and AUC values of 2.7127 and 0.981, respectively. Furthermore, the machine learning models constructed by considering the distance threshold outperformed those built using the unoptimized dataset. The characteristic factors were ranked using the mutual information method, with their scores decreasing in the order of rainfall (0.1616), altitude (0.06), normalized difference vegetation index (NDVI; 0.04), and distance from roads (0.03). Finally, the geologic hazard susceptibility classification was assessed using the natural breaks method combined with a clustering algorithm. The results indicate that the clustering algorithm exhibited higher classification accuracy than the natural breaks method. The findings of this study demonstrate that the proposed model optimization scheme can provide a scientific basis for the prevention and control of geologic hazards.
The research findings on the ground motion and liquefaction potential analyses during the 2018 Great Indonesia Earthquake (Mw 7.5) are significant and crucial. The earthquake triggered soil-structure damage due to liquefaction. This study, which thoroughly investigated four sites at Palu, was conducted by performing a comprehensive ground motion parameter analysis. The ground motion characteristics were presented and justified, particularly for the most impacted direction. Ground motion predictions were analysed to define the spectral accelerations, and matching spectral accelerations were conducted to produce ground motions for each site. Non-linear seismic ground response analysis based on the hyperbolic model of pressure pressure-dependent was performed to investigate cyclic soil behaviour. The results revealed that ground motion is crucial in significant soil damage, and the earthquake energy could trigger deep liquefaction. As the most significant ground motion, the vertical ground motion is essential in determining deep liquefaction. The discussion on the impact of liquefaction based on the results of the numerical analysis is presented. Significant ground motion with a longer duration could have a substantial impact on deep liquefaction in the study area. These findings depict how the 2018 Indonesia Earthquake (Mw 7.5) triggered a mega-liquefaction in Palu City. The results could enhance the understanding of the importance of seismic hazard assessment. It is recommended that site investigation and soil improvement should be planned to counteract liquefaction damage before construction. This study also suggests conducting seismic hazard assessments for city development to minimise the potential disaster impact in the study area.
The available heat content (stored heat energy) of hot dry rock (HDR) at a depth of 1-10 km in the global land crust is estimated to be 5.06 × 108 EJ, attracting considerable global attention. This paper presents a comprehensive analysis of the geological framework, HDR resource potential, exploration advancements, and the development of enhanced geothermal systems (EGSs) in China. HDR resources are extensively distributed across China. Within the depth range of 3-10 km, China's estimated potential approximates 2.29 × 107 EJ, with a theoretical power generation capacity of approximately 1.67 × 1016 kWh. Replacing coal power with HDR can help to achieve a net emission reduction of 1.34 × 1016 kg CO2 (approximately 1.34 × 1013 t), representing an emission reduction efficiency of 94.4%. Based on a development cycle of 100 years, the average annual emission reduction reaches 1.34 × 1010 t CO2, equivalent to 117% of China's annual carbon emissions in 2022. Furthermore, in the context of global warming, the development and utilization of HDR, which is feasible in virtually any region worldwide, offers significant potential to support global carbon reduction efforts. China has made substantial progress in HDR exploration in recent years. This paper systematically classifies China's HDR resources into four genetic types —highly radioactive heat-producing, sedimentary basin, active volcanic, and intensely tectonic zones—and offers detailed exploration insights for each category. Each classification exhibits distinct geological and tectonic characteristics that influence heat source mechanisms and resource distribution. Furthermore, this paper documents significant advances in EGS construction, particularly in the Gonghe Basin on the northeastern margin of the Qianghai-Xizang Plateau and the Matouying uplift in the North China Basin, where successful reservoir stimulation, microseismic monitoring, and experimental power generation have been achieved. Despite these developments, challenges persist, including technical adaptability under complex geological conditions and the economic viability of large-scale HDR development. This paper suggests that future initiatives should emphasize resource exploration, technological research, and policy support to foster sustainable HDR resource development in China, thereby contributing to the global energy transition and environmental sustainability.
The geothermal resources in China are primarily found in its sedimentary basins, particularly in the large basins located in eastern China, which hold significant potential for geothermal energy development. The Songliao, North China, and Zhangzhou basins are of special interest due to their considerable exploration depths, extensive development history, and high levels of research activity. This study focuses on the three basins to analyze their thermal reservoir characteristics in eastern China. Between 2017 and 2023, the research team carried out a comprehensive analysis involving deep boreholes that exceeded 4000 m in depth within these three basins. They meticulously created detailed physical profiles that captured essential characteristics such as porosity, permeability, and thermal properties, reaching down to the basement of each basin. The findings indicated that variations in thermal conductivity within shallow geotechnical layers significantly influence the redistribution of deep thermal energy in the upper layers of the earth. Furthermore, differences in physical properties notably affect heat transport processes. The research proposes distinct heat models tailored for each basin: For the Songliao Basin, a low-permeability model with homogeneous thermal properties is constructed; for the North China Basin, high permeability and thermal conductivity layers are highlighted; and a fracture network controlling water and heat is presented in the Zhangzhou Basin. To elucidate the thermal structure of these basins, the Curie surface and Moho surface were analyzed. The shallow Curie surface indicates ongoing intense thermal activity stemming from crustal heat sources, while a shallow Moho surface signifies historical vigorous mantle thermal activity associated with mantle source heat production. Furthermore, the research evaluates the geothermal resources and the potential for carbon emission reduction in these basins. Total volume of exploitable geothermal fluid is estimated to be 76.9×109 m3/a, corresponding to an annual renewable geothermal energy 1.47×1016 kJ. The implementation of geothermal energy could lead to a reduction in annual CO2 emissions by nearly 2×109 t, which constitutes about 17.4% of China's national carbon emissions in 2022. This estimation provides invaluable theoretical insights and data support for geothermal exploration and sustainable development in eastern China.