Environmental and Earth Sciences

Abstract: This paper presents the results of research evaluating the potential of the Zharkent Depression in Southern Kazakhstan as a promising geological structure for identifying natural reservoirs within aquifer formations for long-term underground CO₂ storage. Based on seismic and well data integrated with modern geographic information sys-tems (GIS), digital surface models of reflecting horizons correlated with structur-al-stratigraphic complexes were constructed. Structural, lithological, and petrophysical modeling was performed, providing three-dimensional distributions of lithology and porosity, as well as reservoir saturation forecasts. The geological reservoir model was developed using geostatistical analysis principles. The three-dimensional geo-hydrodynamic model is based on numerical methods for estimating reservoir hydrodynamic parameters. The injection dynamics and under-ground gas storage models, including an economic efficiency assessment, were calcu-lated for a long-term period. It was established that the Jurassic aquifers, characterized by thick sandstone sequences with enhanced reservoir properties (porosity and permeability), represent the most favorable environment for carbon dioxide injection and storage. Simulation of the in-jection and storage processes yielded predictive saturation cubes and quantitative characterization of CO₂ volume and distribution within the trap under specified injec-tion conditions. The potential volume of injected gas was calculated, and an optimal CO₂ storage strategy in the aquifer was determined through the year 2126. The findings indicate that the storage reservoirs in the Zharkent Depression possess a CO₂ seques-tration potential comparable to existing global large-scale carbon capture and storage projects.

Abstract: Petrology and geochemistry reconstruct from plutons and eruptive products the underground chemical and thermodynamic conditions of magma at the time of crystallization. Accretionary layers in crystals record the composition of the surrounding melt, as well as the confining pressure and temperature. Such a backward reconstruction should be paired with a forward computation of the solidifying crystals during their transport in convective motions inside a refilled magmatic reservoir. This work develops a framework for the solution of magma fluid-dynamics and for the related Lagrangian trajectories of suspended crystals. Episodes of magma mixing due to injection of fresh magma into a shallow chamber are simulated at first in Eulerian reference system. Afterwards, the Lagrangian trajectories of passive tracers are computed, tracking the magma composition, pressure and temperature through which these particles move. On the base of the compositional, pressure and temperature conditions, the crystallizing phases are computed with the MELTS code. The history of accretionary layers is thus obtained by interface-controlled growth and solid-state diffusion. Results show that crystals residing in different parts of the underground system acquire a distinctive signature and are well mixed together. A small population will register the successive refilling episodes, while a substantial one will record each fresh injection.

Abstract: Soil organic carbon (SOC) is a key indicator of soil quality and an essential component of climate change mitigation strategies. However, long-term SOC monitoring based exclusively on field measurements is often limited by data scarcity, spatial heterogeneity and the high cost of repeated sampling. This study assesses the potential of combining field observations, remote sensing-derived proxies and process-based modelling to simulate SOC dynamics in the Guadiamar Green Corridor, Seville, Spain, a restored area affected by the 1998 Aznalcóllar mining spill. SOC dynamics were simulated using the RothC model under a set of boundary condition scenarios, in which field-measured or locally observed input variables were progressively replaced by alternative data sources. The reference scenario was based mainly on field SOC and clay measurements and local meteorological records, while the remaining scenarios tested the use of NDVI-derived carbon inputs, remote sensing or reanalysis-based climatic variables, and SoilGrids-derived estimates of SOC and clay content. The simulations were evaluated against field observations collected over the restoration period. Overall, RothC reproduced the general decreasing trend in SOC observed in the study area, although most scenarios did not fully capture the magnitude of SOC loss measured in the field. Differences among boundary conditions highlighted the sensitivity of RothC to specific input variables, particularly initial SOC, clay content and evapotranspiration. Scenarios based on alternative soil datasets showed that global soil products can provide useful information where field data are limited, but their performance depends strongly on their agreement with local soil conditions. Similarly, the replacement of local climatic data with remote sensing or reanalysis products affected model outputs, especially when evapotranspiration was substituted. These results suggest that remote sensing and open-access environmental datasets can complement field measurements in SOC modelling, particularly in data-scarce restoration contexts. However, their use requires careful validation, as model accuracy depends on the variable being replaced, the quality of the proxy dataset and the calibration of RothC to site-specific conditions.

Abstract: The Semi-airborne frequency tipper sounding method (SFTS) based on tipper characteristics exhibits high lateral and vertical resolution, making it a promising technique for deep mineral exploration, particularly in challenging terrains where conventional ground-based electromagnetic methods face accessibility constraints. Currently, interpretation techniques for this method remain underdeveloped, limiting its practical application in mineral prospecting. To accelerate the application of SFTS in mineral exploration, this paper defines the full-zone apparent resistivity formula based on the complex implicit function relationship between tipper and background resistivity parameters, and iteratively calculates it using the binary search algorithm. An explicit calculation expression for the apparent depth of SFTS was constructed through data fitting, enabling fast full-zone apparent resistivity-depth imaging. The effectiveness of the imaging method is evaluated using one-dimensional layered models that simulate common geological scenarios in mineral exploration, including conductive sediment-hosted and resistive basement settings. Furthermore, the imaging characteristics of single and multiple anomalous bodies are analyzed using a three-dimensional synthetic model, with the anomalous bodies designed to represent typical mineral deposit types, such as massive sulfide orebodies (low resistivity) and porphyry systems (high resistivity). The proposed imaging method can achieve high-resolution and accurate positioning of subsurface anomalies, providing valuable guidance for targeting deep-seated mineral resources. The imaging results lay a foundation for data processing and inversion interpretation of SFTS in mineral exploration campaigns.

Abstract: Artificial intelligence (AI) is transforming environmental geophysics and geotechnical engineering (EGGE), shifting practice from empirical and deterministic workflows toward data-rich, physics-consistent, and decision-oriented subsurface intelligence. This review synthesizes advances in machine learning, deep learning, physics-informed and theory-guided modeling, multimodal data fusion, uncertainty-aware and explainable AI, and intelligent sensing for near-surface, environmental, and geotechnical systems. It presents an integrated framework linking physics-informed AI, multimodal fusion, and regulatory pathways for deployment in EGGE, bridging methodological innovation and operational adoption. These advances enable high-resolution subsurface characterization, lithological and geotechnical profiling, hydro-geomechanical parameter estimation, groundwater assessment, geohazard forecasting, and infrastructure monitoring, marking a transition to field-validated decision-support systems for early warning, risk-informed design, and climate-resilient management. Key challenges include data heterogeneity, cross-scale inconsistency, limited ground truth, fusion complexity, ill-posed inversion, weak generalization across geological and climatic regimes, and insufficient interpretability, uncertainty quantification, and validation for engineering decisions. Regulatory gaps further constrain adoption. A roadmap emphasizes scalable physics-integrated AI, next-generation multimodal fusion, interpretable and uncertainty-aware modeling, edge-cloud digital twins, adaptive data acquisition, and standardized benchmarking. AI can redefine EGGE through resilient, sustainability-aligned subsurface intelligence if scientific rigor and governance advance in parallel.

Abstract: This paper presents the results of research aimed at identifying deep-seated natural sealed reservoirs for the isolation of chemically active gases, including CO₂. A comprehensive analysis of geological, geophysical, and hydrogeological data was conducted to identify potential structures within the aquifers of Almaty and the Almaty region (Southern Kazakhstan) capable of capturing and storing carbon dioxide under geographically favorable and economically viable conditions. The study utilizes seismic survey results and drilling data from the eastern part of the Ili Basin, demonstrating the efficacy of seismic exploration in identifying stratigraphic horizons and their structural-tectonic settings. Based on an integrated analysis of available geo-physic information, the lithological and stratigraphic characteristics of the sedimentary cover in the Ili Basin are substantiated. Key caprock sequences and reservoir units for potential storage sites are identified, and recommendations for further geological exploration are provided. Five reflecting horizons were identified within the geological section of the troughs. It was established that the Miocene-Paleogene and Jurassic horizons contain sandstone reservoirs with a thickness exceeding 10 m and enhanced filtration properties. Clay complexes are prevalent in the Upper Jurassic deposits, which can serve as a caprock for these reservoir rocks. Furthermore, the Upper Cretaceous clay sequence may act as a fluid seal for the Neogene-Paleogene sandy horizons. Such conditions meet the requirements for sealed reservoirs for the isolation of chemically active gases, including CO₂. According to hydrogeological studies, seven aquifer complexes are distinguished: Permian, Triassic, Jurassic, Cretaceous, Paleogene, Neogene and Quaternary. The novelty and practical significance of this research lie in obtaining new information on the geological structure of deep horizons in poorly studied areas of the Ili Basin and establishing favorable geological factors for identifying potential sites suitable for carbon dioxide sequestration.

Abstract: Swelling soil landslides pose severe challenges in geotechnical engineering due to non-linear deformation and strength degradation. Accurate characterisation of pore structure parameters remains the core difficulty. This study proposes a Physics-Informed Neural Network (PINNs) framework that utilises Mercury Intrusion Porosimetry (MIP) data to simultaneously invert three key physical parameters: pore fractal dimension (Ds), surface tension (γ), and contact angle (θ). By embedding the Washburn equation and fractal pore theory into the neural network loss function, the framework achieves high-precision inversion without requiring complete prior information. Validated on three expansive soil samples, the inverted Ds values were 2.47, 2.53, and 2.58, showing excellent agreement with classical models (R² > 0.99) and an average relative error below 2.3%. The inverted γ ranged from 0.476 to 0.480 N/m and θ from 142.3° to 144.2°, both satisfying physical plausibility requirements. Five-fold cross-validation confirmed the absence of overfitting (ΔR² < 0.001). Sensitivity analysis identified Ds as the dominant parameter controlling pore volume distribution; Ds exceeding 2.55 indicates elevated landslide susceptibility. This framework provides a rapid, automated approach for extracting pore structure parameters, offering parametric support for preliminary risk assessment of expansive soil slopes.

Abstract: The study investigates the seismic ambient noise within the Campi Flegrei caldera (Na-ples, Italy) to improve the detection capability and reliability of the local earthquake moni-toring system managed by the INGV, Osservatorio Vesuviano. It focuses on the spectral characteristics and spatial variability of the ambient noise field, aiming to identify the dominant frequency bands that control signal detectability and to provide key information for the optimization of the monitoring network. Due to the dense urban environment sur-rounding the caldera, seismic recordings are often contaminated by high anthropogenic noise, which can mask low-magnitude volcanic or seismic signals. Power Spectral Density (PSD) analysis was applied to evaluate background noise levels at several broadband sta-tions belonging to both the permanent and temporary seismic networks over the period January 2022 to January 2023. The resulting PSD estimates were compared with the global Peterson noise models to assess station performance and environmental conditions. Re-sults show significant variability among stations, related to local human activity, proxim-ity to infrastructure and different installation settings (buried vs. surface). The study em-phasizes the importance of continuous noise monitoring to ensure high-quality seismic data, support optimal station siting, and refine monitoring strategies in densely populated volcanic regions such as Campi Flegrei, where the reliable detection of low-amplitude seismic and volcanic signals is essential.

Abstract: Coastal strata contain various architectural elements, which differ from both typical marine and terrestrial deposits. These architectural elements represent novel habitats for marine benthos, and some may even serve as a gateway for colonization into aquatic environments on land. Here, I present vertical burrows from the Wulongqing Formation (Cambrian Series 2) in the Longbaoshan area, Yunnan, China, suggesting that early Cambrian burrowers colonized gravelly coastal channels within a fluvio-tidal system. The succession is dominated by mudstone (> 80% of total thickness), with conglomerate beds present at its basal part. The characteristics of the mudstone (lenticular sands, mud drapes, bioturbations) demonstrate tidal influence and benthic activity. The conglomerates are interpreted as coastal channel deposits, characterized by sheet-like geometry (width to thickness ratio > 500), trough cross-stratifications (CXt), and inclined gravel-sand stratifications (CXgs). Furthermore, statistical analysis reveals that the occurrence frequency of vertical burrows at the contact between mudstones and overlying conglomerates (4 in 7) is significantly higher (p-value< 0.01) than within mudstones (5 in 51), indicating that burrowers colonized these coastal channels. Given the similarity between these sheet-like conglomerates and pre-Devonian sheet-braided conglomerates, this colonization implies colonization at the marine end of fluvial systems during the Cambrian explosion.

Abstract: The Jiaduoling area is located in the northern segment of the Southwest Sanjiang Metallogenic Belt, a region characterized by complex geological structures and abundant mineral resources. This study systematically identifies the spatial correlation between subsurface magnetic bodies and tectonic structures by utilizing 1:50,000 high-precision aeromagnetic data. Advanced processing techniques—including upward continuation, vertical derivatives, total gradient modulus, and Euler deconvolution—were integrated to refine the structural framework and clarify the mechanisms of fault-controlled mineralization.The results indicate that the aeromagnetic anomaly pattern is predominantly governed by NW-trending faults. Specifically, the deep-seated major fault F1 (with a calculated depth exceeding 3 km) served as the primary migration channel for ore-forming fluids, while secondary faults created localized ore-hosting spaces. Physical property analysis reveals a significant magnetic contrast, where Mesozoic intermediate-acid magmatic rocks act as the essential source for mineralization, providing both material and thermal energy for the formation of porphyrite-type iron deposits.Based on these findings, a three-dimensional "aeromagnetic anomaly-structural framework-mineralization" correlation model was established. Finally, two high-potential metallogenic prospective zones (P1 and P2) were delineated, providing precise geophysical evidence and strategic guidance for regional mineral exploration and the targeting of concealed ore bodies.

Abstract: Rockfall from slope unstable rock masses, a typical geological hazard induced by brittle failure, is characterized by abrupt occurrence, negligible macroscopic deformation prior to failure, and extremely short lead time for early warning, posing a severe threat to the safety of mountainous transportation systems, water conservancy and hydro-power projects, and urban settlements. Conventional static analysis methods have sig-nificant limitations in real-time acquisition of damage evolution of structural planes and dynamic assessment of stability changes, which can hardly meet the practical re-quirements of early warning for unstable rock masses. The dynamic evaluation method for the stability state of unstable rock masses, based on the principles of structural dy-namics, establishes a correlation model between dynamic parameters (natural fre-quency, damping ratio, mode shape, etc.) and the damage degree of structural planes, providing a new paradigm for dynamic identification and quantitative evaluation of the stability of unstable rock masses. This paper systematically reviews the dynamic behavior mechanism and theoretical evaluation framework of slope unstable rock masses, and elaborates on the damage evolution of structural planes, the disturbance effect of environmental dynamic loads, and the key dynamic parameter system. The single-degree-of-freedom dynamic models and their theoretical derivation for three typical types of unstable rock masses (sliding-type, toppling-type, and falling-type) are thoroughly analyzed, and the cutting-edge advances such as multi-block chain collapse model and data-physics dual-driven surrogate model are reviewed. Meanwhile, the contact and non-contact monitoring methods based on Micro-Electro-Mechanical System (MEMS) and Laser Doppler Vibrometer (LDV) techniques, as well as the de-velopment status of cloud-edge collaborative intelligent early warning architecture, are systematically summarized. On this basis, the core challenges are pointed out, includ-ing the long-term evolution under multi-field coupling, high-fidelity inversion calcu-lation for large-scale rock masses, and the scientific correlation between early warning thresholds and failure probability. The full-life-cycle dynamic simulation based on digital twin is also prospected. The research results provide a systematic reference for the improvement of the theoretical system of dynamic evaluation of slope unstable rock masses and the engineering practice of disaster prevention and mitigation.

Abstract: This paper focuses on the problem of anticipating the local occurrence of future large earthquakes. "Local" is defined as the probability of a large earthquake occurring with a defined circle of arbitrary radius surrounding a point of interest. The main (and for that matter, the only) assumption for all these works is that the Gutenberg-Richter (GR) magnitude-frequency relation holds. Here we describe a method for computing calendar time forecasts in a local area for large earthquakes of a target magnitude MT using a count small earthquakes MS < MT in the area. Using the idea that the GR relation is valid throughout the surrounding region, we define an ensemble of earthquakes in larger surrounding regions to be used in computing the forecast. What follows is simple data mining. The method has significant skill, as defined by the Receiver Operating Characteristic (ROC) test, which improves as time since the last major earthquake increases. The probability is conditioned on the number of small earthquakes n(t) that have occurred since the last large earthquake. The probability is computed directly as the Positive Predictive Value (PPV) associated with the ROC curve. The method is validated by comparison to the UCERF3 forecasts for the UCERF3-defined geographic boxes centered on Los Angeles and San Francisco. The method is then applied to a 125-KM radius circular area around Los Angeles, California, following the January 17, 1994 magnitude M6.7 Northridge earthquake, and short term forecasts (1 year and 5 year ) are computed.

Abstract: This study presents a comprehensive geospatial framework for landslide risk assessment across the 4,523 km road network of the Region of Epirus in Greece. Utilizing a field-verified inventory of 295 active landslides, the research evaluates five key predisposing factors—lithology, slope inclination, elevation, land use, and cumulative annual precipitation—using the bivariate Frequency Ratio (FR) statistical model. Among six tested scenarios, the most robust model integrated all factors, achieving high predictive accuracy by classifying nearly 80% of the study area within Moderate to Very High susceptibility zones. The resulting Landslide Susceptibility Index (LSI) was converted into a Landslide Hazard Index (LHI) and integrated with a weighted Road Vulnerability Map, which categorized road sectors based on functional importance and traffic volume. The final Landslide Risk Map indicates that the most critical risk zones are clustered along major transportation corridors that traverse geologically weak formations, moderate to high precipitation areas and steep mountainous sectors. This quantitative approach provides a vital decision-support tool for regional authorities, enabling the prioritization of geotechnical monitoring and the strategic allocation of resources for infrastructure stabilization. The methodology offers a replicable workflow for enhancing the resilience of transportation networks in landslide-prone Mediterranean regions facing evolving climatic threats.

Abstract: The color origin of precious coral, a highly valued organic polycrystalline gemstone, has long remained elusive. In this study, an integrated approach employing spectrophotometry, Raman, FTIR, and UV-Vis spectroscopy, coupled with Spearman correlation analysis, was utilized to investigate a color-graded series of precious coral samples ranging from white to red. The results demonstrate that the calcareous skeleton consists exclusively of calcite. The actual chromophores are identified as a blend of multiple distinct polyene species, characterized by Raman shifts at 1126 and 1515 cm⁻¹. Inherently exhibiting a red-orange hue, the progressive accumulation of these polyenes drives a systematic color transition from orange to red.Both absorption bands at 314 nm and 532 nm in the UV-Vis spectra originate from the polyene pigment molecules. Specifically, the broad 532 nm band is dominated by π-π* electronic transitions. As the pigment concentration increases, this band exhibits pronounced broadening and enhancement, accompanied by a redshift of the maximum absorption peak. This spectral evolution leads to an intensified absorption in the yellow-orange region, elucidating the intrinsic mechanism underlying the color transition of precious coral from orange to red with increasing pigment content. This work lays a solid foundation for the non-destructive identification of precious corals and future research on their color genesis.

Abstract: Three-dimensional geological structural modelling provides the geometric framework for subsurface exploration and development. However, conventional workflows, driven primarily by seismic interpretation, often lack explicit constraints from expert knowledge and are difficult to update when interpretations evolve. This study proposes an intelligent modelling methodology guided by a geological structure knowledge graph. The method includes: (i) a Three-tier Knowledge Architecture (TKA) that formalises domain knowledge in entity, relationship and inference layers using RDF/OWL; (ii) a Knowledge-driven Intersection Line Generation Algorithm (KILGA) coupled with a hierarchical adaptive mesh refinement scheme based on a posteriori error estimation (HAMR-APEE) to integrate geological constraints and mitigate boundary aliasing; and (iii) a bidirectional linkage mechanism between the knowledge graph and 3D models to support incremental updates following knowledge revision. The approach is validated in three petroliferous basins in China (Ordos, Qaidam and Sichuan), representing micro-amplitude, thrust nappe and deep complex structural styles. Compared with a conventional Petrel-based workflow, the proposed method reduces modelling RMSE from 15–20 m to 5–8 m, improves geological reasonableness from ~85% to >95%, and shortens modelling cycles from months to weeks.

Abstract: Gas hydrates are ice-like compounds formed from water and methane under high-pressure, low-temperature conditions in marine sediments. They influence sediment stability, fluid flow, and hydrocarbon distribution in continental margin settings. This study employs advanced seismic attribute analysis to investigate the gas hydrate stability zone (GHSZ) in the Gulf of Mexico and to assess the relationship between hydrate presence, subsurface fluid flow, and sediment deformation.Seismic attributes, including coherence, amplitude, and spectral decomposition, were applied to 3D seismic reflection datasets covering structurally complex regions of the northern Gulf of Mexico. These attributes were used to map bottom-simulating reflectors (BSRs), gas chimneys, and fault/fracture systems. Results indicate that gas hydrate stability zones are strongly associated with structural highs, fault intersections, and areas of enhanced deformation.The study finds that fault-controlled fluid pathways significantly influence hydrate distribution and sediment deformation patterns, highlighting the need to integrate seismic attribute analysis in hydrate resource assessment and geohazard evaluation. These findings provide new insights into fluid migration mechanisms and sediment dynamics in hydrate-bearing marine environments.

Abstract: The sound speed profile (SSP) is a core environmental parameter for underwater acoustic detection, navigation, communication, and other applications. However, its accurate acquisition is constrained by the sparsity of observational data and the ill-posed nature of inversion problems. This paper systematically reviews the research progress of SSP inversion under sparse observation constraints: it combs the technical evolution from physical model-driven methods (Matched Field Processing, MFP; Compressed Sensing, CS) to data-driven approaches (Dictionary Learning, DL; Machine Learning, ML), and classifies and compares the principles, applicable scenarios, advantages, and disadvantages of mainstream methods. It integrates typical measured cases from existing studies (including mesoscale eddy monitoring, underwater navigation and positioning, etc.) and quantitatively analyzes the inversion accuracy and practical value of different technical routes. The research shows that fusing physical constraints with multi-source sparse data (remote sensing, in-situ discrete measurements) is the core direction to balance inversion accuracy, efficiency, and cost. This paper provides a comprehensive reference for technical selection in fields such as marine national defense and resource exploration.

Abstract: The pre-Cenomanian Nubia sandstone is considered as one of the most productive reservoirs in the Gulf of Suez, Egypt. Determination of its reservoir rock type (RRT) is a crucial process in reservoir characterization and modeling, especially when the reservoir is extremely heterogeneous. In this study, an effort was made to bridge the gap between various techniques to determining RRT, which include lithofacies, traditional methods (x-y crossplots), and machine learning (ML). To accomplish this, the objectives of this study were accomplished through the utilization of sedimentological core description, routine core analysis, and conventional logging data from two wells (well A and well B) in the southern Gulf of Suez. The results show that the complete Nubia interval in the southern Gulf of Suez can be distinguished into seven distinct lithofacies (LF1-LF7). The first six lithofacies are comprised of different types of sandstones, while the seventh is related to mudstone. The results show also that the fault-cutting, rather than stratigraphic reasons, was primarily responsible for the difference in Nubia thicknesses between the two studied wells. It is likely that the lower three lithofacies were separated from one another by unconformity surfaces. The traditional techniques used to predict the RRTs show that the normalized reservoir quality index (NRQI) was the most appreciated method to predict the Nubia rock types. On the other hand, K –means clustering and self-organizing maps (SOM) techniques based on raw logging data and principal component analysis (PCA) can properly predict the Nubia reservoir rock types when correlated with the Ward’s method, which is based on the core data. The reservoir rock quality ranged from poor to very good, with a domination of moderate reservoir quality in well A and very good reservoir quality in well B. This discernible difference in reservoir quality between the two wells was probably attributed to the consequences of the post-deposition diagenesis processes and the variation of the sandstone texture.

Abstract: Fracture networks strongly control fluid flow, reservoir connectivity, and production performance in carbonate systems, yet their multiscale architecture of complexity remains difficult to characterize from heterogeneous geological and geophysical datasets. Here, we introduce the Digital Transformer (DiT), a physics-informed computational framework that automatically analyzes and classifies fracture systems using spatially encoded visuonumerical primitives derived directly from physical measurements. Instead of relying on textual tokenization, the approach performs attention primitives tokenization of multiscale geophysical data. Clusters of absolute integer values act as computational tokens while preserving spatial topology and scale-invariant structure of the original system. The framework integrates two complementary environments: Muuk'il Kaab (MIK) for multidimensional metadata fusion and visualization, and SYM-Fractron, a hybrid binary-symbolic transformer for two-dimensional image analysis. Within this architecture, Digital Twins provide coupled visual and statistical representations of geological systems and their computational counterparts, enabling an interpretable taxonomy of natural fracture patterns while supporting well-trajectory optimization in the exploration of dolomitized carbonate reservoirs. In this view, fracture architectures become visionumerical primitives whose physics-informed tokenization opens a pathway from the architecture of natural complexity to its computational realization through Digital Twins.

Abstract: The Junggar Basin (NW China) is a polycyclic intracontinental basin formed within the Central Asian Orogenic Belt and characterized by multi-stage tectonic reactivation and composite petroleum systems. This study integrates tectonic evolution, source rock geochemistry, and basin modeling to clarify the spatial–temporal controls on hydro-carbon generation and accumulation. The basin evolved from Late Paleozoic rifting to Carboniferous–Permian collision, followed by Mesozoic thermal subsidence and Ce-nozoic inversion related to the uplift of the Tianshan. Major source rocks include Car-boniferous marine shales (total organic carbon 1.5–5%), Permian lacustrine deposits (up to 10–12% total organic carbon; hydrogen index up to 700 mg HC/g TOC), and Ju-rassic coal-bearing strata. Thermal maturity ranges from 0.6% to >2.0% vitrinite re-flectance, indicating multi-phase oil and gas generation and secondary cracking in deeply buried depocenters. Hydrocarbon accumulation differs across structural zones. Central depressions are dominated by deep gas generation and composite traps, whereas northwestern segments reflect lateral migration from Permian source kitch-ens. Cenozoic inversion significantly reactivated faults and controlled vertical migra-tion pathways. The results highlight that hydrocarbon distribution in the Junggar Ba-sin is governed by the synchronization of tectonic evolution and generation phases, providing predictive insights for exploration in polycyclic inversion basins.