Since the initiation of plate tectonics on Earth, the evolution of tectonic regime and supercontinent cycles (Nuna, Rodinia and Pangea) have been the first order factors that contributed to Earth surficial environmental changes and thus development of Earth habitability. In particular, the Neoproterozoic to Cambrian eras recorded dramatic evolution in solid earth and surficial systems, including the breakup of the Rodinia supercontinent and the assembly of the Gondwana (680-430 Ma) landmasses, the Neoproterozoic Oxidation Event (NOE) and Cambrian life diversification. However, the key tectonic factors that were responsible for the evolution of Earth surficial systems during this period remain highly debated and are largely confined to conceptual models. The authors reconstructed the temporal trends of continental orogenic belts, using detrital zircon europium anomalies (Eu/Eu*) and orogen length big data database. The quantitative results indicate that the sustained orogens over 200 Ma formed prolonged super-mountains during the Gondwana assembly period. This might have occurred due to the initiation of modern type plate tectonic regime since the Neoproterozoic time, which allowed large-scale continental deep-subduction for the first time in Earth history. This coincides well with the large-scale Himalaya type collision orogenic belt during Gondwana assembly, along with the sudden drops of the global thermobaric ratio (T/P) and the large-scale occurrence of high-pressure and ultrahigh-pressure metamorphism. This suggests that orogenic elevation and scales were comparable to those of modern Earth. The high topographic relief of terrestrial systems during this time likely resulted in intense weathering and erosion of low-latitude orogenic belts, supplying huge continental sediments to the ocean, consistent with the seawater Sr isotope variation. Huge sediments and nutrients supply profoundly changed ocean composition and increased ocean productivity, triggering Neoproterozoic oxygenation events and providing environmental triggers for the Ediacaran-Cambrian explosion. Overall, multiple factors, with orogens being the most important one, contributed to the genetic, environmental and ecological conditions for Ediacaran life radiation and Cambrian life explosion.
The three-dimensional resistivity model of the lithosphere and fluid content plays a significant role in analyzing the spatiotemporal distribution and dynamic reasons for the North China Craton (NCC) destruction. The conductivity is related to temperature and melt fraction using laboratory experiments. The Hashin-Shtrikman (HS) bounds are used to constrain the conductivity range of rocks in the solid and solid-melt systems. The authors established the lithospheric conductivity-temperature relationship by combining the mineral composition obtained from the analysis of xenoliths, the steady-state heat conduction equation, water, and carbon dioxide. To discuss the destruction of the NCC, the three-dimensional resistivity structure model obtained from the magnetotelluric sounding (MT) array inversion of the NCC is compared with the model calculated using the HS bounds. The research results show that the higher the volatile water and carbon dioxide content in the fluid, the lower the mantle solidus. Based on the melt fractions of the Ordos Block, Trans-North China Orogen, and Bohai Bay Basin, the speculated lithosphere-asthenosphere boundary (LAB) exhibits the characteristics of deep in the west and shallow in the east. The partial melting of the Bohai Bay Basin began at a shallower depth than other places, with a more significant difference in fluid content in the depth.
Early to middle Triassic sedimentary units within the Sichuan Basin of southwest China contain extensive occurrences of lithium (Li)-rich clay layers (the so-called “Green bean rock”, or GBR) that have significant economic potential as a result of their substantial scale. However, a comprehensive understanding of the genesis and processes involved in the formation of these clay layers remains elusive. This study presents the results of a systematic investigation of three typical Li-rich clay profiles within the basin, using X-ray diffraction (XRD) and scanning electron microscopy (SEM) for mineralogical characterization and analysis. The presence of micron-scale primary high-temperature quartz and iron microspherules suggests that these Li-rich clay deposits have a volcanic origin. XRD analysis indicates that bulk rock samples of GBR are predominantly composed of quartz, clay minerals, and either calcite or gypsum, with the clay mineral assemblage dominated by illite and/or illite/smectite (I/S). Specifically, the GBR of the northeastern Sichuan Basin has a higher level of illitization compared to its southern counterpart, representing differences in sedimentary facies that are also found in other Li environments such as the Li deposits of Nevada. The northeastern GBR contains gypsum and clay minerals, whereas the southern counterpart consists mainly of carbonate lithologies with minor amounts of clay. This indicates that the sedimentological controls on the formation of clay mineral assemblages also influence the formation of clay-type Li deposits and Li enrichment processes. The composition of the clay components is indicative of probable derivation from smectite during diagenesis after originally being generated by the submarine modifications of volcanic materials. The later formation of I/S formation was also most likely the result of the illitization of volcanogenic smectite. The widespread presence of clay-type Li deposits indicates extensive eruptions of volcanic ash during Early to Middle Triassic volcanic events. The Li trapped within the illite and I/S is thought to have been sourced from volcanogenic materials via transportation in a composite fluid comprising meteoric, porewater, and hydrothermal fluids. These economically significant Li-rich clay minerals, found within a marine basin, represent a promising target for lithium exploration as a result of the extractable metal content. This research provides new insights into the processes involved in the formation mechanisms of clay-type lithium deposits and discusses the prospecting potential of volcanic clay-type Li deposits in the Sichuan Basin, contributing to the critical assessment of the Li resources needed for the energy transition and sustainable energy initiatives.
By 2024, the proven natural gas reserves of the Maokou Formation in the central Sichuan Basin had exceeded 1.5×1011 m3, suggesting great potential for hydrocarbon exploration. However, this formation exhibits small reservoir thicknesses and pronounced lateral heterogeneity due to sedimentation and erosion. These characteristics lead to significantly varying single-well production, complicating the identification of major controlling factors in high production and reducing the prediction accuracy of sweet spots. To identify the key geological factors controlling the gas production in the Maokou Formation, this study investigated the geological and seismic characteristics of the dolomite reservoirs in this formation. The primary geological factors influencing single-well production were explored using a machine learning approach—an enhanced random forest (RF) prediction model based on Shapley additive explanations (SHAP) values (also referred to as the SHAP-enhanced RF model). Accordingly, an optimal combination of geological parameters for high production was determined, followed by the identification of sweet spots. The results demonstrate that the SHAP-enhanced RF model allows for the effective quantification of the relative importance of various factors. The most critical factors affecting the gas production in the Maokou Formation include burial depth, natural gamma-ray value, the spatial distance of bright spots (defined as the time thickness of bright spots relative to the base of the second member of the Maokou Formation), and paleogeomorphology. High-yielding wells typically feature a joint advantage of multiple critical factors. A strong synergistic effect is prone to occur when several optimal factors fall within their optimal ranges simultaneously. In this case, high production capacity might be achieved. Compared to an equal-weight RF model, the SHAP-enhanced RF model yielded a mean absolute error (MAE) decreasing by 18.6%, thereby enhancing prediction accuracy and reliability. The findings of this study can serve as a valuable guide for production planning and well placement optimization in the Maokou Formation.
Selenium (Se) serves as a trace element essential for the human body owing to its significant physiological functions and extensive pharmacological effects. The Se required by the human body is primarily obtained from soil-derived foods. This study revealed Se-rich soils covering a certain area and Se-rich edible wild mushrooms with high Se accumulation rates in Chuxiong, central Yunnan Province, China through a geochemical survey of soil quality. Furthermore, this study investigated the Se migration and transformation mechanisms in the soil-wild mushroom system, aiming to provide a scientific basis for the development and planning of Se-rich green foods in the study area. Using the geochemical data of samples collected from topsoils, deep soils, and wild mushrooms and their root soils in Nanhua County, Chuxiong, this study analyzed the Se contents in soils and wild mushrooms and their root soils and explored the mechanisms and influencing factors of Se enrichment in wild mushrooms. The results indicate that the topsoils in the study area exhibit Se contents ranging from 0.07 mg/kg to 0.95 mg/kg, with an arithmetic average of 0.25 mg/kg. The Se-rich soils cover an area of 356 km², which accounts for 13.07% of the total topsoil area. The wild mushrooms in the study area display Se contents varying from 0.004 mg/kg to 47.10 mg/kg, with a median of 0.977 mg/kg. The analyses of Moran’s index and semivariogram indicate that the Se content distributions in both topsoils and deep soils in the study area exhibit distinct spatial structures. Specifically, the semivariogram model for the Se content in the deep soils emerges as a Gaussian model, and the Se content exhibits a nugget-to-sill ratio of 21.72%, suggesting that the Se content in deep soils is primarily influenced by structural factors such as parent materials. Se in the soils originates primarily from soil-forming parent rocks, with the origin of the Se-rich soils closely related to Triassic black shales, thin coal seams, and metamorphic rocks in the Ailao Mountain area. The wild mushrooms in the study area enjoy significantly higher Se content than other reported naturally Se-rich agricultural products, with a Se accumulation rate of up to 92.31% and an overall over-limit ratio of Pb and Cd of merely 11.54%, suggesting that the study area has substantial potential for the development of naturally Se-rich green foods. The wild mushrooms in the study area exhibit bioconcentration factors (BCFs) of Se ranging from 0.02 to 157.00 (median: 4.26), with Se bioavailability decreasing in the order of Boletus edulis, Boletus aereus, Leccinum nigrescens, Ramaria botrytoides, and Russula virescens. For the Se absorption and enrichment in the wild mushrooms, the primary controlling factor is identified as the wild mushroom species. Furthermore, they are significantly influenced by the Se content in soils but are minimally affected by the physicochemical indicators of soils.
Exploring the geological genesis of selenium (Se) in Se-rich soil of the Hetao Plain, a major grain production base of corn, wheat, potato, sunflower and zucchini in northern China, is vital for the rational development of Se-rich land resources and the environmental geochemical assessment. In this study, element geochemical characterization, geospatial analysis and mathematical statistics were employed to investigate the factors influencing the spatial distribution of soil Se in the Hetao Plain, focusing on the differences in soil chemical weathering intensity, rare earth elements distribution pattern and soil sedimentary sequence characteristics between the study area and typical surrounding parent materials. The results indicated that the soil Se content in the study area ranged from 0.07 mg/kg to 0.72 mg/kg, with an average value of 0.24 mg/kg, which was higher than the regional background values. Moreover, this suggested that local Se enrichment occurred in the soil of study area, and that the Se distribution was restricted by factors including soil clay minerals, constant elements and soil organic matter (SOM) contents. The characteristics of soil parent material in the study area differed significantly from those of the piedmont diluvial parent material, whereas were similar to those of the Yellow River floodplain and lacustrine parent materials, revealing that the soil parent material in the study area mainly came from the water system transportation. Through combining the characteristics of soil sedimentary sequences in the study area with the location of the Yellow River ancient channel, it is suggested that the origin of high Se soil in the central and northern belt zone of the study area is associated with the Yellow River ancient channel, where the enrichment pattern is “channel sedimentary type”. In addition, the development and utilization potential of Se-rich cultivated land was explored based on the biological effects of Se. It was concluded that the Se content of crops (corn, sunflower and zucchini) in the study area was higher than that of similar crops in China, that crop species and soil Se content were the dominant influencing factors of crop Se content in the study area, and that the crops growing in Se-rich areas had higher Se enrichment rate than those growing in non-Se-rich areas. This exhibited a high development and utilization potential of the investigated cultivated land for Se-rich crop production.
Coastal groundwater (CGW) systems in rapidly urbanizing regions face critical challenges in achieving Sustainable Development Goal (SDG), where anthropogenic pressures intersect with hydrogeological vulnerability. This study employs coupled isotopic-hydrogeochemical analysis and geostatistics to unravel hydrochemical driving forces compromising groundwater quality in the Jinjiang Downstream Watershed (DJW), Southeast China. The results indicated that groundwater was predominantly recharged from local atmospheric precipitation and lateral recharge from the adjacent boundaries. Hydrochemical distributions exhibited a distinct pattern, transitioning from HCO3-Ca to HCO3·Cl-Ca, and then to Cl-Mg·Ca/Na·Ca, reflecting processes ranging from freshwater recharge to seawater intrusion (SWI). Elevated nitrates were primarily attributed to domestic sewage leakage and septic tank leaching. Additionally, preferential flow posed a risk to deep groundwater quality, by facilitating the rapid transport of contaminants through rock fractures. Key driving forces of hydrochemistry included silicate dissolution with local runoff paths, SWI, and human activities. The study advocates for a governance paradigm integrating electrochemical sensor networks with machine learning-driven contaminant prediction and phased membrane bioreactor deployment, which synergistically reduce nitrate fluxes while maintaining aquifer freshening processes. This integrated approach establishes a scalable model for SDG-aligned groundwater management in vulnerable coastal zones, demonstrating how process-based insights can bridge scientific discovery and water security implementation.
Distributive Fluvial Systems (DFS) are critical sedimentary systems governing fluvial dynamics, sediment transport, and ecosystem sustainability in modern and ancient basins. Accurate quantification of DFS channel morphology is essential for advancing sedimentary modeling, optimizing water resource management, and mitigating fluvial hazards. Here, the authors present a novel automated framework that extracts DFS channel networks from remote sensing imagery by integrating multiscale image segmentation, fractal network evolution, and region-merging algorithms. Through hierarchically multiresolution feature processing, this method overcomes limitations of traditional single-scale analysis, enabling adaptive extraction while reducing segmentation heterogeneity. Specifically, the workflow consists of three stages: Image segmentation, feature extraction, and image classification. When applied to the Golmud fluvial fan (Qinghai, China), this approach achieves 90.2% overall channel extraction accuracy using 0.5 m resolution imagery, significantly outperforming traditional DEM-based (81.7%) and water spectral methods (85.4%) in resolving fine-scale channel networks. Crucially, the framework demonstrates robust adaptability to complex sedimentary environments with variable vegetation cover (<30% density) and spectral noise, providing a time-efficient, data-agnostic solution for DFS characterization.
On September 5, 2022, a magnitude 6.8 earthquake struck Luding County, Sichuan Province, China, at a depth of 16 km, triggering numerous co-seismic landslides. Using multi-temporal satellite imagery, the authors identified 7463 co-seismic landslides with varying spatial distributions. Focusing on the differences between the western and eastern sections of the Xianshuihe fault zone, the influences of elevation, slope, aspect, topographic wetness index (TWI), stream power index (SPI), distance to river, distance to seismogenic fault, seismic intensity, lithology, and land surface temperature (LST) on the co-seismic landslide distribution were analyzed. Random Forest-Adaptive Boosting (RF-AdaBoost), Support Vector Machine (SVM) and Back Propagation Neural Network (BP) models were used to assess landslide hazards in the region. The RF-AdaBoost model performed the best, achieving an Area Under the Curve (AUC) of 0.954. The zones of high co-seismic landslide hazard were primarily concentrated along the Xianshuihe fault zone and Dadu River. These findings provide valuable insights into seismic landslide hazard assessments and offer scientific guidance for disaster prevention and mitigation strategies.
Developing predictive models for rock avalanche runout is crucial for hazard risk mitigation and management, while also deepening the understanding of rock avalanche dynamics. In this study, a database of 63 representative rock avalanches was compiled, and the geometric characteristics of these events were analyzed. Traditional regression methods were compared with neural network regression (NNR) approaches to find the optimal model. Model parameters, loss functions, and evaluation metrics were examined to determine optimal configurations. Based on correlation analyses and model performance, this study recommends the use of source area width as an alternative to failure volume in runout prediction models, effectively addressing the common challenge of estimating avalanche volume. Ultimately, an NNR-based model, utilizing mean squared logarithmic error as the loss function and incorporating source area width and fall height as input parameters, was identified as the optimal approach. This model achieved prediction errors within a -50% to 50% range with 95.2% probability, yielding a mean absolute percentage error of 23.22% and an R2 of 0.85. These findings enhance rock avalanche prediction methodologies, providing more accurate tools for assessing the potential impact zones of destructive geological events.
With continuous advancement in geological studies, three-dimensional (3D) geological modeling technology based on big data and artificial intelligence (AI) has become a prominent focus in the interdisciplinary field of earth and information sciences. Through the analysis and comparison of existing 3D modeling methods, this study introduces a high-precision 3D geological modeling approach. The new method leverages advanced computing technologies, including multisource heterogeneous data processing, integrated model databases, seamless splicing of local models, and cluster analysis. Furthermore, it enables unified 3D visualization of subsurface and surface conditions, providing new insights into disaster prevention, mitigation, and intelligent mineral exploration. To validate its practicality, this study conducts 3D geological modeling of the X area within the Sichuan Basin, China. The data sources include remote sensing images, geological maps, geophysical data, borehole data, and X-ray fluorescence (XRF) spectroscopy data. Preliminary exploration in the X area has successfully identified new mineralization belts, verifying the feasibility and effectiveness of the new 3D geological modeling method that integrates big data processing and intelligent techniques.
The Jiaodong Peninsula in North China Craton as the world’s third largest gold metallogenic region, holding gold deposits primarily of the altered rock type and the quartz vein type. The Linglong gold orefield, with 916 t of proven gold resources presently, is a typical birthplace of quartz vein-type gold deposits in the Jiaodong Peninsula and develops altered rock type gold deposits. This study elaborated on the characteristics of deposits and the spatial distributions of orebodies in the Linglong gold orefield, analyzing control factors, geochemical characteristics, mineralization era, and mineralization mechanism. Comprehensive research has found that: The altered rock-type gold deposits have gentle dip angle and large scale, fine grain size of the gold ore, low average ore grades, low metal sulfide content, which is about 1/3 compared to the quartz vein-type gold deposits. Their gold minerals primarily occur as intercrystalline gold. In contrast, the quartz vein-type gold deposits occur in the steeply dipping tensile fissures in the footwall of main faults. Their orebodies exhibit steep dip angles and small scales but high average ore grades and metal sulfide content, with gold minerals primarily occurring as inclusion gold. The Linglong gold orefield experienced gold mineralization at about 120 Ma, with ore-forming fluids belonging to a moderate- to high-temperature, low-salinity, and reducing H2O-CO2-NaCl±CH4 system. The ore-forming fluid mainly comes from the mixture of magmatic water and meteoric water. The ore- forming materials primarily originated from the lower crust, supplemented by some mantle-derived components. The comprehensive analysis leads to the following conclusions: Extensive crust-mantle mixing provided thermodynamic conditions, migration pathways, and partial fluid sources for ore-forming fluid activity in the Jiaodong region. The rapid magma ascent led to the formation of detachment faults and associated tensile structures in the shallow crust, which create favorable space for fluid migration and the enrichment and precipitation of ore-forming elements. The pressure fluctuation of fluids arising from changes in the dip angles of faults is the main cause of the stepped metallogenic pattern. The quartz vein- and altered rock-type gold deposits are the product of the same metallogenic event under different metallogenic modes and tectonic locations. Based on the deep metallogenic regularity, this study established a geological-geophysical prospecting model for altered rock-type gold deposits, of which the critical factors include ore-hosting faults, high-precision geophysical exploration, and a stepped metallogenic pattern.