Geological Journal最新文献

Bangladesh in the Bengal Delta faces complex environmental issues, including sea-level rise, coastal flooding, high population density, and widespread poverty. These factors lead to severe land loss, saltwater intrusion, water scarcity, and biodiversity decline, further exacerbated by climate change. These challenges significantly risk groundwater availability and increase the likelihood of natural hazards such as subsidence, landslides, and flooding. This study quantitatively maps the spatial distribution of subsidence in urban and agricultural settings by utilising Differential Interferometric Synthetic Aperture Radar (DInSAR) and Persistent Scatter Interferometric Synthetic Aperture Radar (PSI) techniques with ascending Sentinel-1 satellite data. We analysed 55 pairs of images with DInSAR and 142 pairs with PSI from March 2017 to October 2022, focusing on five target locations for DInSAR and urban Dhaka for PSI. Findings reveal consistent subsidence in urban Dhaka at an average rate of 16 mm/year, along with semi-seasonal subsidence variability in five agricultural locations. Specific rates are 7 mm/year in Dhaka, 8 mm/year in both Rajshahi and Mymensingh, and 9 mm/year in Rangpur. Sylhet subsides at a rate of 5 mm/year, potentially linked to the fold and thrust belt and the Dauki Fault. Our research highlights the significant environmental impacts of human activities like groundwater withdrawal and land-use changes, which contribute to subsidence and groundwater depletion via the Bengal Water Machine. While further study is required to comprehensively understand the relationship between LOS indicated subsidence rates, geological factors, and geomorphological changes, our findings offer crucial insights into the current impacts of climate change and ongoing environmental degradation in the region.

In the Garhwal–Kumaun Himalayas along the Alaknanda Valley, the Inner Lesser Himalayan (iLH) belt represents the late Paleoproterozoic magmatic arc setup for the active northern Indian margin. The Berinag Group of the iLH belt, representing the rifted back-arc basin, comprises the ubiquitously distributed volcano-sedimentary sequences of chlorite schist and quartzite intercalations. Previously published maximum depositional ages (MDAs) of the Berinag Group are based on various detrital U–Pb geochronology statistical methods. These ages vary over a broad time spectrum and do not represent the true depositional age (TDA) of the Berinag Group. In this study, we employ outcrop-to-microscopic scale observations, U–Pb magmatic and detrital zircon geochronology, and zircon grain shape analyses to characterise the penecontemporaneous volcano-sedimentary sequences. The TDA of the Berinag quartzite at 1879 ± 16 Ma is constrained from the crystallisation age of the mafic volcanic flows, represented by the interlayered meta-volcanic chlorite schist. The YGC2σ method accurately estimates the MDA of the same quartzite at 1871.3 ± 5.3 Ma, which closely approximates the TDA and is also consistent with the average crystallisation age of orthogneiss in the proximal northern magmatic arc at 1858 ± 15 Ma. Therefore, we argue that the active northern Indian margin witnessed a rapid and short-lived synchronous mafic volcanism, sedimentation, and felsic plutonism at ~1.87–1.86 Ga. Mafic volcanism constrained the sedimentation in the rifted back-arc basin. At the same time, the felsic plutonism in the nearby northern magmatic arc predominantly contributed to the detritus in the basin with minor input from the distant southern sources.

The exploration and extraction of deep salt lake resources are of great significance for alleviating potassium and lithium resource shortages. However, large-scale industrial extraction has not been achieved due to technical challenges and cost constraints. This study focuses on the Mihai Mining District in Qaida Basin, Qinghai Province, investigating the exploration and development of deep confined brine enriched with potassium and lithium. The Mihai Mining Area boasts vast reserves of deep confined brine, however, they are not well-utilised because of their heterogeneity, low water yields, and extremely low mining efficiency. Previous studies have revealed that the high-yield wells in mining areas are located within fault zones. Therefore, based on the theoretical models of ‘brine circulation and potassium formation in fault zones’ and ‘storage of brine in steeply inclined water-collecting corridors’, an efficient exploration technique is proposed, which focuses on identifying active fault zones within areas with well-developed high-quality brine reservoirs as key target areas. This study employed radar remote sensing interpretation of pre- and post-seismic deformation intensity to infer fault zone development areas. Integrated with radon gas measurements, the results confirm the presence of a NE-striking active fault within the key target zone. Functioning as the sedimentary centre, this zone exhibits highly concentrated brine with co-enrichment of K⁺ and Li⁺ (both elements showing anomalously high concentrations), indicating high potential for K-Li-rich brine discovery. Guided by the exploration theory, verification drilling revealed brine with elevated K-Li concentrations (approximately double those in adjacent areas) and high-yield water flows in most boreholes, demonstrating significant exploration success. The distribution pattern of the discovered high-yield, water-rich areas and the exploration prediction method are of great significance for the development of deep brine in the Mihai Mining Area and have considerable potential for promotion in the Qaida Basin.

The conjunction of the Qinling-Qilian Orogenic Belt is a critical area for understanding the tectonic relationship between the eastern and western sections of the Central China Orogenic Belt. It serves as an important region for studying the tectonic framework of the northern margin of the Proto-Tethys Ocean. This paper identifies a series of meta-volcanic rocks in the Fanjiaqu area of Fenggeling Town, situated in the eastern section of the North Qilian Orogenic Belt. To determine the formation age, petrogenesis, and tectonic environment of these volcanic rocks, we conducted systematic studies that include petrology, geochemistry, isotope geochronology, and zircon Lu-Hf isotope analysis. Petrographic analysis reveals that the protoliths of the meta-volcanic rocks comprise an assemblage of basalt, andesite, dacite, and rhyolite. LA-ICP-MS zircon U–Pb dating indicates that the protoliths of the meta-basaltic andesite and meta-dacite were formed at 441.9 ± 4.1 Ma and 443.9 ± 2.6 Ma, respectively. Geochemical characteristics show that the meta-basalt-andesite has medium SiO₂ contents (47.10% to 59.64%), higher TFe₂O₃ and TiO₂ contents, lower Cr and Ni concentrations, Mg^# values ranging from 43 to 53 and La/Nb ratios between 4.53 and 5.97. In contrast, the meta-dacite and meta-rhyolite exhibit high SiO₂ (67.62% to 75.18%) and Al₂O₃ contents, along with low TFe₂O₃, TiO₂ and P₂O₅ contents, as well as Mg^# values ranging from 42 to 55. Overall, the meta-volcanic rocks display notable negative Eu anomalies, with Eu/Eu* values varying from 0.76 to 0.83 for basic rocks and from 0.47 to 0.79 for acidic rocks. The meta-volcanic rocks are enriched in large-ion lithophile elements (LILE), such as Th and U, while being depleted in high-field-strength elements (HFSE) like Nb, Ta and Ti, indicating geochemical characteristics similar to those of arc magmatic rocks in subduction zones. The zircon ε _Hf (t) values of the meta-dacite range from +4.5 to +13.1, while the two-stage model ages (T_DM2) range from 1135 to 588 Ma. These characteristics suggest that the meta-basalt-andesite derived from partial melting of the mantle, metasomatised by subduction fluids, whereas the meta-dacite and meta-rhyolite are products of crust–mantle mixing. Integrating regional geological data, this study proposes that the meta-volcanic rocks in the Fenggeling area formed in a continental margin arc environment, while the eastern section of the North Qilian Orogenic Belt was in the subduction setting of back-arc oceanic crust during the Late Ordovician to Early Silurian.

The Ashawayi orogenic deposit is the third largest gold deposit in the southwestern Tianshan Orogen, Xinjiang, China. Based on an extensive field geological survey, fresh wall rocks, altered wall rocks and mineralised rocks were systematically collected to study the alteration zoning patterns. Major and trace element compositions of these rocks were analysed to calculate the elemental mass transfer during mineralisation processes. The degree of primary alteration, such as pyritisation, silicification and sericitisation, escalates progressively from peripheral fresh wall rocks towards the central mineralised zone. Notably, the pyritisation can serve as the most direct prospecting signal. During the mineralisation, K₂O, MnO and SiO₂ moved substantially into the central mineralised zone, whereas FeO, MgO and Na₂O migrated out of the central zone. In addition, Pb and Rb were significantly enriched in the central mineralised zone by external input. In contrast, the moderate or significant depletion in Nb, Ni, Ga and Cu indicated a mass loss of these elements from the altered or mineralised rocks. However, indistinguishable patterns of the other trace elements and rare earth elements (REEs) between fresh, altered and mineralised rock samples indicated that the ore-forming materials were probably sourced from the ore-host strata. Reduced gold (Au⁰) in the fresh wall rocks was reactivated by K-rich ore-forming fluids, which transported the gold in the form of Au(HS)²⁻. Subsequently, the destabilisation of the Au(HS)²⁻ complex as a result of intense sulfidation and sericitisation during structural deformation triggered the precipitation of Au from the fluids, leading to the formation of gold orebodies. The deposit is located at the summit of a regional thrust-nappe structure and is controlled by ductile-brittle deformation structures. The mineralisation was associated with the tectonic transition from compression to strike-slip extension.

The Yamne Valley, located in the southern part of the Eastern Syntaxial Bend (ESB) in Arunachal Himalaya, is seismically active and results in significant tectonic movements that shape the rugged terrain. The landscape is defined by steep slopes and fragile rock formations, continuously being uplifted by tectonic forces. The active tectonics and continuous rainfall in this region have significantly affected drainage patterns and landforms, leading to increased hillslope processes and the initiation of landslides. In this study, we have assessed the active tectonics in the region utilising ALOS PALSAR (12.5 m resolution) digital elevation model (DEM), geomorphic parameters (asymmetry factor, basin shape index, hypsometric integral, stream-length gradient, stream sinuosity, channel steepness index and valley floor width to valley height ratio) and various morphometric parameters. Subsequently, an index of relative active tectonics (iRAT) map was created exhibiting very high, high, moderate, and low areas of relative tectonics. The distribution of landslides in the area aligns with the prepared iRAT map, indicating the clustering of landslides in the areas having very high and high relative active tectonics, particularly between the Main Central Thrust (MCT) and Tiding Thrust (TT). Thus, quantitative geomorphometry may serve as a valuable tool for identifying potentially active zones susceptible to slope failures, thereby guiding decision-makers in regional planning and efforts to mitigate landslide hazards, particularly in the Yamne Valley, supporting infrastructure and tourism activities.

Conodonts and ostracods in the orogenic belt can provide valuable insights into the timing and effects of tectonic events. In this study, four species of conodonts and 24 species of ostracods are reported for the first time in the Hongliugou and Kalamaili Formations in the Harlik arc, southwestern Central Asian Orogenic Belt (SW CAOB). Among them, conodont species Caudicriodus woschmidti is the significant element in the lowermost Devonian conodont zone, indicating that the upper part of the Hongliugou Formation dates to the earliest Devonian (Lochkovian) age. The ostracod assemblages identified in the Hongliugou and Kalamaili Formations are characterised by the dominance of Palaeocopid and Metacopina elements, respectively, suggesting a change in environment from inner-shelf to hemipelagic outer-shelf. This transgression is associated with regional extension during the Early Devonian, likely subjected to the retreating subduction of the Paleo-Asian Ocean. Furthermore, paleobiogeographic analysis of Devonian ostracods in the Harlik arc shows a prominent correlation with eastern North America and southern Europe. This suggests that the Harlik arc was probably located in the mid-latitude region of the northern hemisphere, adjacent to the northeastern part of the Laurentia-Siberia continent, during the Early to Middle Devonian.

Determining and characterising locations vulnerable to flooding can help in reducing damage and the number of fatalities. During the monsoon season, East India's Subarnarekha River frequently floods to a significant degree. In current work, we suggest a unique hybrid strategy for preparing the entire catchment's Flood Susceptibility Mapping (FSM). The study area's FSM was conducted by considering 10 flood conditioning factors utilising the Best-Worst Method (BWM) and a multi-parametric Analytical Hierarchy Process (AHP) as per expert knowledge. Meanwhile, the proposed strategy incorporates a Decision Making Trial and Evaluation Laboratory (DEMATEL) for examining causal linkages and dependencies between different elements affecting the flooding process. Several statistical matrices were used to compare the suggested strategy of BWM and AHP. Based on our findings, we concluded that the integration of DEMATEL with AHP and BWM (ID BWM, ID AHP) was more effective than alternative strategies. The findings show that out of 10 flood conditioning factors, slope, elevation, distance from the river, drainage density, Topographic wetness Index (TWI), Land Use Land Cover (LULC), Normalised Difference Vegetation Index (NDVI), precipitation, soil texture, and curvature, factors that have the biggest effects on the local flooding phenomenon are elevation, slope, precipitation, and distance from the river. For validating the efficacy of the flood susceptibility map, Area under the Receiver Operating Characteristic Curve (AUC-ROC) was adopted and demonstrated, showing a pretty high accuracy of (0.92 or 92% and 0.94 or 94%) for ID AHP and ID BWM, respectively. Our research findings provide a highly affordable and useful answer to the flooding problems of basin Subarnarekha.

Landslides pose significant hazards in the mountainous region of Sikkim, India, necessitating accurate susceptibility mapping to mitigate risks. This study applies four machine learning models: Boosted Tree (BT), Gradient Boosting Machine (GBM), K-Nearest Neighbour (KNN), and Multilayer Perceptron (MLP) to develop a detailed landslide susceptibility map. Feature selection was performed using correlation analysis, the Boruta model, and multicollinearity tests, which identified 13 key landslide conditioning factors based on 1456 landslide inventory points. The GBM model demonstrated the highest predictive performance with an AUC of 0.99, followed by BT (AUC: 0.965), MLP (AUC: 0.940), and KNN (AUC: 0.895) in the testing dataset. The confusion matrix validation confirmed that GBM outperformed other models, achieving the highest F1 score (0.894) and accuracy (89.4%), followed by BT with an F1 score of 0.874 and accuracy of 87.8%. KNN and MLP displayed lower performance, with KNN showing an F1 score of 0.724 and accuracy of 72.6%, and MLP significantly underperforming with an F1 score of 0.096 and accuracy of 48.6%. Statistical significance testing using the Wilcoxon Signed-Rank Test revealed significant differences between BT and MLP (p = 0.018), while other model pairs exhibited no statistically significant performance differences. Additionally, the variable importance analysis highlighted Diurnal Temperature Range (DTR) as the most critical factor influencing landslide occurrence (43.99%), followed by elevation (21.59%). These findings provide valuable insights for policymakers and government authorities, enabling them to take necessary measures for effective landslide management in the vulnerable areas of Sikkim, confirming the efficacy of machine learning models for geohazard assessments.