Geological Setting Research Articles

Simulating groundwater flow and contaminant transport

This study presents a comprehensive simulation of groundwater flow and contaminant transport using advanced numerical modeling techniques. The research focuses on understanding the complex interactions between groundwater movement and pollutant dispersion across various geological settings. By applying the governing equations of fluid dynamics and solute transport, the model incorporates essential parameters such as hydraulic conductivity, porosity, and dispersivity, enabling the accurate prediction of contaminant behavior over time. Simulations were carried out under a range of scenarios to assess the effects of differing recharge rates and contaminant source configurations on groundwater quality. The results provide critical insights into the mechanisms governing contaminant migration, highlighting the importance of soil properties, boundary conditions, and flow dynamics in influencing transport behavior. Moreover, the study demonstrates how variations in geological features and pollutant sources can significantly alter contaminant dispersal patterns. This work contributes to the development of robust groundwater management strategies and emphasizes the role of simulation tools in assessing environmental risks tied to groundwater contamination.

Shale pore pressure seismic prediction based on the Hydrogen generation and compaction-based rock physics model and Bayesian Hamiltonian Monte Carlo inversion method

The seismic prediction of formation pore pressure is critically important for the exploration and appraisal of shale oil and gas reservoirs, as well as for identifying optimal drilling targets within these formations. In particular, the complex pressure regimes of deep shale reservoirs introduce substantial challenges to traditional seismic prediction methodologies. A Bayesian Hamiltonian Monte Carlo pre-stack seismic inversion approach is proposed to enhance the accuracy of pore pressure estimations based on an advanced rock physics model that integrates compaction and hydrocarbon generation effects. Initially, a comprehensive petrophysical model is developed to account for the effects of hydrocarbon generation and compaction, establishing a robust framework for the normal compaction trend simulation. Subsequently, a novel formation pressure prediction model is derived based on integrating the Eaton model and the Bower stress hypothesis. Then, the Bayesian HMC seismic inversion method is proposed to predict formation pore pressure in shale reservoirs with elastic impedance data set. The proposed methodology is validated through application to actual well log and seismic data from the Fuling shale oil and gas reservoirs in the Sichuan Basin, which demonstrates significant potential of this method for enhancing the prediction of pore pressure in complex geological settings.

Risk assessment of potential rock collapse in Fenghuang Mountain, three gorges reservoir area, China

On 8 October 2017, persistent heavy rainfall triggered a rock collapse on Fenghuang Mountain in Wuxi Town, located within the Three Gorges Reservoir region of China. Subsequent field investigations and monitoring identified several potentially unstable rock masses in the area, posing a significant threat to the safety of nearby residents and their property. In this study,the Rapid Mass Movement Simulation (RAMMS) numerical tool was used to perform a back analysis of the rock collapse event. The well calibrated numerical model was then used to assess the risk of the potential unstable rock masses in the study area. The rock collapse on Fenghuang Mountain descended rapidly along the slope, with the dislodged material accumulating at the base and obstructing the road at the foot of the slope. Some debris breached the embankment and entered the Daning River. The computed maximum velocity during the rock collapse event was approximately 9.14 m/s, with an average maximum deposit thickness of around 4.48 m. The back-analysis of the rock collapse event closely aligns with the observed failure process and deposit morphology documented through field investigation. Using the well calibrated numerical model, a dynamic analysis was conducted on the potential unstable rock mass. The risk assessment indicates that the potential unstable rock mass is prone to instability, with a high likelihood of a subsequent rockfall under extreme rainfall conditions. The computed average maximum velocity for the potential rockfall is 33.83 m/s, with an average maximum deposit thickness of 2.20 m. The computed maximum impact pressure is about 164 kPa, which would result in significant damage to the road below. Additionally, a maximum wave height of 1.38 m from the surge caused by potential rockfall entering the Daning River was calculated by a semi-empirical model. This research offers a novel approach and methodology for assessing the risk of such hazardous events in similar geological setting globally.

Analysis of structural architecture in western Saudi Neom City area, northwestern Arabian Plate: Field investigation

Saudi Neom City is being constructed as an urban mega-project in the northwestern part of the Arabian plate at the southeastern part of the NNE-oriented sinistral Dead Sea continental transform fault that links the NNW-oriented Red Sea Rift to the Zagros collision through the NE-oriented sinistral East Anatolian Fault. The present study aims to detect and analyze the different structural elements in the western Neom city. It also attempts to provide valuable data to help decision-makers for better achievement of such vital projects. From field investigation, the study area mainly comprises Neogene sedimentary sequence and exhibits a complex structural architecture represented by extensional- and wrench-style deformations. Different fault orientations (NNW–SSE striking extensional faults, WNW–ESE and ENE–WSW striking oblique-slip faults, and NNE–SSW and NNW–SSE striking strike-slip faults) dissect the western Neom area. Individual NNW-oriented Red Sea Rift-related extensional faults and fault blocks are antithetically tilted to the northeast. The sinistral movement onset along the Dead Sea Transform Fault postdates the Lower Miocene Burqan Formation deposition. Furthermore, the sinistral strike-slip regime of the western Neom area involves extensional structures, including normal faults, contractional structures (folds and reverse faults), and structures representing horizontal shear on near-vertical planes. NNW-oriented negative flower structures and forced folding occur along a synthetic NNW-oriented sinistral strike-slip fault set. Contractional structures are expressed by sets of NE-oriented left-handed en echelon-arranged folds in the Middle Miocene deposits. A proposed strain ellipse reveals these structures are associated with the NNE-oriented sinistral Dead Sea Transform Fault. The complexity of the structural architecture of the western Neom area can be attributed to its geologic setting under the influence of the Red Sea extensional rifting followed by the tectonic activity along the Dead Sea transform faulting. It is recommended that the structural attributes investigated, especially the geographic distribution of brittle structures (faults and fractures), be considered during the construction of Neom City.

Raman scattering of omphacite at high pressure: Toward its possible application to elastic geothermobarometry

Abstract Due to their widespread occurrence in several geological settings, omphacite inclusions could be used for elastic Raman geothermobarometry. However, the Raman scattering of complex silicate minerals entrapped in a host depends on both the chemical composition and elastic strain developed during the metamorphic pathway, which makes the task very challenging. Here, as a very first step to probe the potential of omphacite to be used as a mineral inclusion in elastic geothermobarometry, we report the pressure dependence of the Raman spectra of omphacite crystals with the same composition, approximately Jd43Di57, but having different symmetry because of the existence (P2/n) or absence (C2/c) of chemical order at the six- and eight-coordinated cation sites. The experimental results are complemented by ab initio quantum mechanical simulations on fully ordered omphacite (Jd50Di50). We demonstrate that the position of the well-resolved Raman peak near 688 cm–1, arising from Si-O-Si bond bending, is very sensitive to pressure but independent of the state of chemical order, which makes it promising to be utilized in Raman geobarometry. The width of this peak varies with chemical order but not with pressure and therefore can be used to constrain the temperature of inclusion entrapment, because the chemical order is indicative of the closure temperature of the cation-exchange reaction. However, further detailed analyses on the compositional variation of the Raman spectra of omphacite is required before considering omphacite-in-garnet systems to be suitable for Raman elastic geothermobarometry.

Seismic super-resolution method: Enhancing reservoir delineation and characterization through high-resolution seismic data

Seismic resolution plays a crucial role in the successful development of both conventional and unconventional assets. The accuracy of structural and stratigraphic interpretations, as well as reservoir characterization, hinges on the resolution and quality of seismic data. In this study, we introduce an inversion-based method for enhancing seismic resolution, drawing inspiration from super-resolution techniques in computer vision. This method, termed seismic super-resolution (SSR), utilizes seismic data coupled with regularizations to drive the inversion, thereby avoiding bias from well data. Our approach has been adopted globally, ranging from conventional assets in Alaska and China to unconventional assets in the Eagle Ford Shale and Permian Basin, significantly impacting field development. This includes improved well planning, reduced drilling uncertainties, and enhanced reservoir delineation. This paper highlights the efficacy and practical applications of the SSR method across various geologic settings.

Improving subsurface structural interpretation in complex geological settings through geophysical imaging and machine learning

This study employs seismic refraction tomography (SRT) and electrical resistivity tomography (ERT) to assess subsurface geological conditions along the proposed Porsgrunn Highway in Norway. The primary objective is to analyze SRT and ERT tomograms to identify subsurface geological structures. However, interpreting tomograms is often limited by smoothed boundaries and reduced resolution. To address these challenges, we apply k-means clustering, a machine learning technique that groups data based on similarities in physical properties, to post-process the geophysical tomograms. This study pioneers the use of k-means clustering to interpret tomograms from SRT and ERT data in complex geological settings. We first evaluate the effectiveness of clustering techniques using numerical modeling for two geological scenarios: a horizontally layered case and a layered case with undulation and a fault structure. Utilizing automated methods (Elbow and Silhouette), we objectively determine the optimal number of clusters for each geophysical tomogram. Subsequently, we compare the performance of the k-means clustering algorithm with subjective expert interpretations and the Laplacian edge detection method. Borehole data validate the clustering results and confirm the effectiveness of optimal cluster selection techniques. The findings of this study demonstrate that k-means clustering significantly enhances the detection of geological structures by establishing clearer boundaries and minimizing noise interference, enabling more accurate fault zone delineation. Compared to traditional edge detection and subjective interpretation methods, k-means clustering offers a systematic and objective approach that improves consistency and reliability across diverse geological settings. Moreover, its automated classification of geophysical data into meaningful clusters enables efficient analysis of large datasets. This study underscores the value of integrating machine learning techniques with geophysical methods such as SRT and ERT to improve interpretability and accurately identify subsurface geological structures, particularly in fault zone identification.

Metal concentration in bottom sediments for a tropical river, geological or anthropogenic source?

The study analyzes data from a survey of metal concentrations in bottom sediments from both margins (left and right) of the lower main channel of the Orinoco River. This study aims to analyze metal concentrations in the bottom sediments of the lower Orinoco River with different geological settings and anthropological sources. El Almacen (ALM), Las Galderas (G), Los Castillos de Guayana (C), Ciudad Bolivar (CB), and Ciudad Guayana (CG) were the sampling sites. Freshwater physicochemical parameters pH, conductivity, and DO were measured in situ at each sample site. Twenty-four bottom sediment samples were collected with an Eckman grab, dried, and sieved. Trace elements Pb, Cr, Cu, Cd, Ti, and Co were measured in sediments with grain sizes <63μm. Bottom sediment analysis followed EPA method 3050B (digestion with HNO3 + HCl + H2O2). Statistical analyses included the Shapiro-Wilk test, Spearman correlation, Kruskal-Wallis test, and principal component analysis (PCA). Physicochemical parameters highlighted the marked difference between the left and the right river margins, reflecting the cross-channel heterogeneity due to the dissimilar geology and low horizontal mixing. Metal concentrations were generally low, with higher variations associated with urban and industrial sources. Pb, Cr, Cu, Cd, Ba, Ti, and Co concentrations did not generally indicate significant pollution, but potential contamination from sewage and industrial effluents was noted. The differences between the two river margins were common in all the sampling sites, reflecting the sources of water biochemistry related to geochemical origins in the Andes and the Guiana Shield, respectively. The study emphasizes the importance of sampling location within the river channel.

Innovative airborne geophysical strategies to assist the exploration of critical metal systems

Critical metals are essential in sustaining the high technology and the green energy transition of modern societies. The future discovery of new critical metal deposits will likely be made at increasing depths and under thick cover sequences. The key roles of the four airborne geophysical exploration methods, gravity, magnetometry, electromagnetism and gamma-ray spectrometry, are reviewed in this article. The measured data from airborne magnetic, gravity and electromagnetic surveys can be inverted to reveal the distribution of underlying mineral prospects in terms of magnetic susceptibility, density and electrical resistivity/conductivity beneath the surface.The interpretation of geophysical data is important in relating geophysical responses to the lithology and geophysical anomalies to potential exploration targets that are concealed under cover. Gamma-ray spectrometry can identify near-surface hydrothermal alteration zones and uranium systems. Structural complexity maps can provide additional key parameters for the exploration targeting of structurally controlled critical metal systems. We briefly discuss the application of airborne geophysical methods to efficiently guide the exploration of concealed critical metal deposits. A robust understanding of the geological setting of the respective mineral prospect is the most relevant factor in choosing the most efficient geophysical exploration strategy. Geophysical tools will likely play an increasingly important role in guiding the future discovery of concealed critical mineral systems.

Characterizing variability in geochemistry and mineralogy of western US dust sources

Dust events originate from multiple sources in arid and semi-arid regions, making it difficult to quantify source contributions. Dust geochemical/mineralogical composition, if the sources are sufficiently distinct, can be used to quantify the contributions from different sources. To test the viability of using geochemical and mineralogical measurements to separate dust-emitting sites, we used dust samples collected between 2018 and 2020 from ten National Wind Erosion Research Network (NWERN) sites that are representative of western United States (US) dust sources. Dust composition varied seasonally at many of the sites, but within-site variability was smaller than across-site variability, indicating that the geochemical signatures are robust over time. It was not possible to separate all the sites using commonly applied principal component analysis (PCA) and cluster analysis because of overlap in dust geochemistry. However, a linear discriminant analysis (LDA) successfully separated all sites based on their geochemistry, suggesting that LDA may prove useful for separating dust sources that cannot be separated using PCA or other methods. Further, an LDA based on mineralogical data separated most sites using only a limited number of mineral phases that were readily explained by the local geologic setting. Taken together, the geochemical and mineralogical measurements generated distinct signatures of dust emissions across NWERN sites. If expanded to include a broader range of sites across the western US, a library of geochemical and mineralogical data may serve as a basis to track and quantify dust contributions from these sources.

Bacterial communities forming yellow biofilms in different cave types share a common core

The walls of different types of caves under diverse geological settings (limestone, gypsum and volcanic) are colonized by biofilms of different colors: white, yellow, pink, grey, green to dark brown, but only a few colored biofilms such as the white, yellow and grey ones have been extensively studied. However, an assessment among the microbial communities originating these biofilms in different lithologies is lacking. Here we compare the yellow biofilms from two caves, Covadura and C3, in the Gypsum Karst of Sorbas in Spain, with those from two Spanish limestone caves (Pindal and Santian), and four volcanic caves in Spain and Italy (Viento, Honda del Bejenado, Grotta del Santo, Grotta di Monte Corruccio). The structure of yellow biofilms in gypsum caves closely resembles that found in other Spanish and European limestone caves. However, volcanic cave biofilms exhibit greater variability in their microbial community structure and morphologies. Biofilms from gypsum, limestone and volcanic caves were characterized by the abundance of the genera Crossiella and the gammaproteobacterial wb1-P19. The uncultured Euzebyaceae were abundant in gypsum and Spanish volcanic caves, while in the limestone and Italian volcanic caves, they were rare or absent. Nitrospira was also abundant in limestone and volcanic caves, but not in gypsum caves. Due to the abundances of Crossiella, gammaproteobacterial wb1-P19, and uncultured Euzebyaceae, in many different ecosystems, not only in caves, as recently reported, understanding the functional diversity in which these lineages are involved seems critical. Although we have studied a limited number of yellow biofilms from caves in Spain and Italy, data from other caves in USA and Russia also point out the existence of a similarity among the most abundant members composing the structure of yellow biofilms, suggesting that they share a common core.

Tectonic implications of a rare metamorphic event in eastern China during the Earth's ‘middle age’

The Mesoproterozoic Era has long been considered a relatively stable, silent and even ‘boring' period in Earth history, during which the lithosphere was tectonically inactive. However, an increasing amount of evidence suggests that metamorphism and magmatism were much more widespread and intense than previously thought, implying that this period was dynamic. Here, we report metamorphism at c. 1.4 Ga along the southeastern margin of the North China Craton in eastern China. We conducted analyses using a TESCAN Integrated Mineral Analyser (TIMA) combined with electron microprobe analysis (EMPA) and zircon sensitive high-resolution ion microprobe (SHRIMP) and laser ablation inductively coupled plasma mass spectrometry U–Pb dating on garnet amphibolites and their country rocks. The detailed mineral composition of a representative garnet amphibolite sample was identified and determined by TIMA. Phase equilibria modelling using pseudo-sections combined with the EMPA data indicated the peak metamorphism stage (M 2 ) of the garnet amphibolites at a P – T condition of 10.6–11.2 kbar and 820°C. The SHRIMP zircon U–Pb analysis yielded an age of 1366 ± 17 Ma for the garnet amphibolites, which is slightly younger than that of the country rock schist (1376 ± 22 Ma). Integrating the metamorphic event with geochemical signatures, magmatic records and sedimentation patterns in analogous geological settings on other continents, our findings collectively demonstrate a protracted and pervasive orogenic process along the periphery of the Columbia supercontinent.

Comparative analysis of deep learning and traditional airborne electromagnetic data processing: A case study

State-of-the-art airborne electromagnetic (AEM) systems conduct large-scale surveys and produce thousands of line kilometers of data. These large volumes of AEM data are generally subjected to manual inspection for identifying and culling data corrupted by couplings from various sources, such as fences and power lines, as well as the assessment for late-time noise. Although effective, this traditional AEM data processing is subjective and time consuming. This study evaluates the efficacy of a recently proposed unsupervised deep-learning method for the automated processing of AEM data on a large-scale survey conducted in New Zealand. The automated system processes 6471 line kilometers of data in approximately 15 min — a task that could extend more than 5–16 days if done manually by a trained expert. Although manual processing is not the ultimate benchmark, both processing schemes have an agreement of approximately 88% for the low-moment data and approximately 96% for the high-moment data. The inversion results for manual and automated system processing also indicate comparable data fits and resistivity models. The automated system demonstrates strong generalization capabilities; however, it faces challenges when data affected by various sources, such as anthropogenic activities, mimic credible 1D responses. This underscores the need for manual inspection in contexts wherein it is necessary to distinguish between true anomalies and noise. This study not only validates the automated system’s applicability across diverse geologic settings but also emphasizes the importance of balancing automated and manual approaches for optimal AEM data processing.

Predictive modeling of river blockage severity from debris flows: Integrating statistical and machine learning approaches with insights from Sichuan Province, China

River blockages caused by debris flows pose serious threats to the environment and infrastructure. This study introduces the river blockage index (RBI) as a key measure of river blockage severity. We collected data from 60 debris flow events to build a comprehensive dataset. To enhance model robustness and accuracy, we optimized variable selection using multicollinearity analysis and the Akaike information criterion (AIC). Four statistical models were developed, including multiple linear, logarithmic, power, and exponential regressions. We also constructed models based on machine learning algorithms, including random forests and gradient boosting decision trees, and tested them using 5-fold cross-validation. After confirming that the training dataset met linear statistical assumptions, we built robust regression models. We tested the significance of the regression equations and coefficients using F-tests and t-tests. Hyperparameters of the machine learning algorithms were optimized through Bayesian methods. Model performance was evaluated using metrics such as R2, adjusted R2, mean absolute error (MAE), mean relative error (MRE), and root mean square error (RMSE). Results show that the most important factors influencing RBI are catchment area (A) and the discharge ratio between the debris flow and the main river (Q). Among the statistical models, the logarithmic and power models performed best due to their simplicity and efficiency. The random forest model demonstrated the highest predictive accuracy and stability overall. By combining statistical methods with machine learning, we improved prediction accuracy and provided practical guidance for disaster prevention strategies. This approach overcomes the limitations of numerical simulations and experimental studies, offering a more flexible and efficient method for RBI prediction. Future work will extend these findings to other geological settings to further enhance model adaptability and performance.

Enhancing Groundwater Potential Mapping Through Integrated Validation and Aquifer Characterization in Diverse Geologic Settings: A Case Study of Central Ethiopian Highlands and Adjacent RIFT

Enhancing Groundwater Potential Mapping Through Integrated Validation and Aquifer Characterization in Diverse Geologic Settings: A Case Study of Central Ethiopian Highlands and Adjacent RIFT

Estimating soil strength using ultra high resolution seismic: A case study from a shallow water windfarm

The increasing global reliance on offshore wind farms as a sustainable energy source necessitates precise soil assessment for foundation design, due to their high foundation costs and complex integration into marine environments. This study explores the utilization of ultra-high-resolution seismic data in conjunction with cone penetration test data for soil strength estimation in offshore wind farm development. We propose a convolutional neural network-based approach that leverages ultra-high-resolution seismic data for direct soil strength estimation, aiming to address the challenges of limited cone penetration test measurements and potential overfitting in complex geological settings. Our methodology involves preprocessing ultra-high-resolution seismic and cone penetration test data, interpreting and integrating geological unit information, and applying repeated k-fold cross-validation to evaluate model performance. The field data results demonstrate the model's efficacy in accurately predicting cone penetration test curve trends, albeit with some amplitude discrepancies. We highlight the significance of geological interpretation in predicting soil strength and identify the dependency on valid data within each geological unit as a limitation.

Love wave along the corrugated interface between an elastic half-space and the corrugated porous layer containing two fluids

This article presents a comprehensive analysis of Love wave propagation along the interface between a porous layer saturated with two immiscible fluids and an underlying elastic half-space. The study places particular emphasis on the corrugated nature of both the interface and the upper boundary of the porous layer. A complex dispersion relation, governed by appropriate boundary conditions, is derived and subsequently simplified into four distinct subcases. The effects of corrugation parameters at the upper boundary of the porous layer and the interface between the layer and the elastic half-space are investigated. The influence of the undulation parameter, which represents the combined effects of wave number and porous layer thickness is also analyzed on the phase speed of the Love wave. In the numerical discussion, a geological structure analogous to oil-contaminated sandstone aquifers in contact with sedimentary bedrock, a common formation in coastal regions, is considered as a realistic example. The key findings reveal that the phase speed of Love waves is highly sensitive to both the corrugation and undulation parameters. Increased corrugation at the upper boundary of the layer enhances phase speed, while corrugation at the interface decreases it. An increase in the undulation parameter decreases the phase speed of the Love wave. Additionally, the presence of oil and water within the porous sandstone significantly reduces phase speed, particularly at lower frequencies, due to higher viscosity and internal friction. This study provides valuable insights into wave propagation dynamics in complex geological settings, with implications for hydrogeology, environmental monitoring, and seismic risk assessment.

Subsurface microbial community structure shifts along the geological features of the Central American Volcanic Arc.

Subduction of the Cocos and Nazca oceanic plates beneath the Caribbean plate drives the upward movement of deep fluids enriched in carbon, nitrogen, sulfur, and iron along the Central American Volcanic Arc (CAVA). These compounds fuel diverse subsurface microbial communities that in turn alter the distribution, redox state, and isotopic composition of these compounds. Microbial community structure and functions vary according to deep fluid delivery across the arc, but less is known about how microbial communities differ along the axis of a convergent margin as geological features (e.g., extent of volcanism and subduction geometry) shift. Here, we investigate changes in bacterial 16S rRNA gene amplicons and geochemical analysis of deeply-sourced seeps along the southern CAVA, where subduction of the Cocos Ridge alters the geological setting. We find shifts in community composition along the convergent margin, with communities in similar geological settings clustering together independently of the proximity of sample sites. Microbial community composition correlates with geological variables such as host rock type, maturity of hydrothermal fluid and slab depth along different segments of the CAVA. This reveals tight coupling between deep Earth processes and subsurface microbial activity, controlling community distribution, structure and composition along a convergent margin.

Spatiotemporal Variation in Mature Source Rocks Linked to the Generation of Various Hydrocarbons in the Fuxin Basin, Northeast China

The assessment of highly mature source rocks linked to hydrocarbon generation remains a challenge in oil and gas exploration. However, substantial terrigenous influences and thermal variations have complicated the formation and evolution of source rocks. This study presents an integrated assessment of highly mature source rocks in the Fuxin Basin, based on sedimentological, geochemical, and organic petrological analyses. Two types of oil- and coal-bearing source rocks were deposited in the semi-deep lake and shore–shallow lake facies during the Jiufotang and Shahai periods. The development of source rocks migrated eastward alongside the lacustrine depocenter, influenced by basin evolution related to extensional detachment tectonism. Furthermore, a gradual increase in thermal records was detected from the western to eastern basins. Consequently, thermal decomposition of source rocks in the Jiufotang formation reduced the organic matter (OM) abundance in the central and eastern basins. Meanwhile, OM types of source rocks range from kerogen type-II1/-I to type-II2/-III, with intense hydrogen generation observed from the western to eastern basins. Consequently, the quality and hydrocarbon accumulation of source rocks are influenced by sedimentation and thermal maturity variation. The spatiotemporal variation in mature source rocks enhances the potential for exploring conventional petroleum, coalbed methane, and shale gas across different strata and locations. Our findings illustrate the significance of the sedimentary and thermal effects in characterizing the evolution of highly mature source rocks, which is relevant to determine oil and gas exploration in similar geological settings.

A Tale of Evolution of the Central Indian Tectonic Zone (CITZ) With Glimpses on Pre‐Suture Plate Margin Depositional History

ABSTRACTComprehending pre‐suture geological setting and post‐suture intricate tectono‐thermal chronicle in a crustal‐scale orogeny and discerning the temporal connections among different tectonic domains within an orogen yields valuable insights into worldwide tectonic phenomena leading to the reconstruction of paleogeographic configurations of fragmented crustal components within erstwhile supercontinents. Since the tectonic evolution of the Indian subcontinent during the Proterozoic remains a topic of discussion due to multiple controversial views, the Central Indian Tectonic Zone (CITZ) offers a great archive for the protracted geological history of subduction, collision, and suturing of northern Indian cratonic blocks (NIC; composed of Bundelkhand and Aravalli cratons) and southern Indian cratonic blocks (SIC; composed of Bastar, Singhbhum, and Dharwar cratons). Events belonging to two Supercontinents namely, Columbia (ca. 2100 to 1800 Ma) and Rodinia (ca. 1200 to 900 Ma) are embedded within the geological history of CITZ, as it records multiple events that occurred between the Paleoproterozoic and Neoproterozoic. In this backdrop, the present contribution attempts (i) the deconvolution of pre‐suture (pre‐CITZ formation) plate margin basin depositional history including tracking of detrital provenance, and (ii) a review of different stages of collision and suturing as the plate margin evolved in the form of an orogen subsequent to the closure of the basin. A back‐arc basin setup in a subduction margin is recommended for the Mahakoshal Basin which witnessed several tectonic pulses resulting in the formation of alluvial fans/fan deltas along the basin shoreline. It is suggested that the basin was open, at places, even after ca. 1700 Ma, and received sediments.