Journal of Earth System Science最新文献

Landslides are among the most dangerous and catastrophic natural hazards with countless concerns. In disaster rescue operations, fast and precise identification of landslides is necessary for timely and effective preventive actions. The landslide risk is anticipated to be reduced through their prediction, monitoring, and accurate detection using remote sensing technology. Moreover, deep learning algorithms have shown excellent improvement in various remote sensing applications. Recent scientific and intelligent technological innovations are needed to be applied to disaster management and assessment, particularly landslides. Therefore, this study aims to extract the landslide hazard information from multiple data sources, i.e., satellite and unmanned aerial vehicle (UAV) images, using a single staged object detection model, i.e., YOLOv5, YOLOv6, YOLOv7, and YOLOv8. The data from distinct platforms are utilized to infer the synergies between them. The results of each database are evaluated quantitatively using standard methods, i.e., precision, recall, f-score, and mean average precision, whereas visual analysis of results is conducted for qualitative assessment. Based on the experimental results, the highest f-score is represented by YOLOv7 (0.995) and YOLOv5 (0.921) for satellite and UAV-based data, respectively. The quantitative results are further compared with previous research work to exhibit the novelty and competence of the proposed research. Our work demonstrates the application and feasibility of the YOLO model in landslide information extraction for quick hazard recovery operations.

Liquefaction is one of the natural hazards that occurs due to earthquakes and has a significant impact on the loss of human lives and various civil infrastructures. In this study, metaheuristic ANN with optimization techniques (i.e., ANN-GWO, ANN-GTO, ANN-GAO, ANN-HHO, ANN-SSA, and ANN-SMA), machine learning techniques are used to predict the probability of liquefaction (({P}_{L})) from the SPT-based dataset. A dataset of 834 case histories, including seven geotechnical and seismic parameters, was used for training and testing different metaheuristic algorithms. The performance of the proposed machine learning algorithm used at every stage of analysis includes statistical parameters evaluation, score analysis, actual vs. predicted curve, error matrix, Taylor diagram, OBJ criteria, DDR criteria, and AIC criteria. The ANN-GTO model has been found to be the best model for the prediction of the probability of liquefaction potential of soil. However, all proposed models can successfully predict the liquefaction potential of soil with reasonably good accuracy. The proposed models can be used as a key tool in the prediction of the liquefaction susceptibility of any soil deposit.

The landform of the region highly influences the dynamics of the flood and plays a crucial role in directing the water flow, affecting the speed and volume of runoff. Assam, located in northeast India, experiences floods yearly due to adverse climatic conditions and complex terrain features. The objective of the present study is to understand the landform classification of Assam using the topographic position index (TPI) and geomorphon-based automated classification of landform (ACL) method and its spatial distribution with slope, geology, soil, LULC, and flood inundation. The ACL method shows that gentle slopes or flat areas occupy the maximum area ranging from 56.17 to 68.10% for TPI‐based slope position classes, and for geomorphon, slope feature occupies 20.61–25.39% of the total area. The spatial distribution of TPI and geomorphon-based landform classification was different because TPI compares the elevation of a point to the average elevation of its neighbourhood, while geomorphon classifies the landscape into predefined landform classes based on terrain shape and the spatial arrangement of elevation values. In both models, valleys are the most dominant landform class and are mainly present in the Central and Barak valley of Assam. The built-up areas and waterbodies on vulnerable landform classes increase their flood susceptibility. About 38.08% of the inundated area was found in wide valleys and 31% of the inundated area lies under flat landforms. The present study can be effective in land use planning, sustainable natural resource management, disaster risk management, and mitigation strategies.

Te–Bi bearing minerals are commonly present in many hydrothermal gold deposits and can provide important physicochemical constraints on their mineralization. The gold mineralization in the Xiejiagou gold deposit is hosted in the Mesozoic Linglong granite and consists of auriferous quartz veins and subordinate disseminated ores in the vein-proximal alteration zone. Three types of pyrite were identified that formed in stage I, II, and III. Gold occurs mostly as native gold and electrum (Ag > 20 wt.%) inclusions in or filling microfractures in pyrite. The abundant auriferous pyrite-quartz veins contain an assemblage of tsumoite and hessite. The tsumoite occurs as irregular inclusions in the Py₁ and is intergrown with the chalcopyrite. The hessite occurs as irregular inclusions or along the margins of the Py₂, and it coexists with the gold and galena. The tellurium fugacity continually decreased from stage I (log fTe₂ = −8.8 to −10.7) to stage III (log fTe₂ = −13.8 to −17.0). The sulfur fugacity increased from stage I (log fS₂ = −8.6 to −11.4) to stage II (log fS₂ = −7.2 to −11.4), and then, it decreased from stage II to stage III (log fS₂ = −9.8 to −13.0). These data indicate the conditions of the gold precipitation during the ore formation process. A detailed study of gold distribution in texturally different pyrite and the paragenetic association of tellurides provide valuable information on the distribution pattern of gold and in understanding the processes of gold deposition and evolution.

Subsurface characterization is a crucial aspect of geophysical exploration, enabling the identification and understanding of valuable geological bodies and resources. In this context, joint inversion of gravity and magnetic data has emerged as a powerful geophysical exploration technique, allowing for a more coherent and consistent interpretation of subsurface structures. The study focuses on understanding residual gravity and magnetic anomalies by employing the gradient descent-based joint inversion approach. A MATLAB program was developed to determine the inverse gravitational and magnetic anomalies using the gradient descent approach. We explored the potential of 2D rectangular prisms as a popular geometry to represent mineralized bodies and oil and gas structures. To overcome the non-uniqueness issues, we designed code for joint inversion of gravity and magnetic data. Synthetic data was inverted using the gradient descent technique and compared with the least-squares approach. Numerical simulations and real data application successfully reconstructed the geometry of the prisms. An illustrative example of a prism fault was used for further evaluation. Real data from the Oka complex in Quebec, Canada, was collected from the literature and subjected to joint and individual gravity and magnetic modelling. The results highlighted the influence of heterogeneous mass distribution on matching forward anomalies. The high gravity anomaly in the Oka complex was attributed to carbonatite and silicate rocks. The presence of two intrusive centres within the complex caused the magnetic high. This work demonstrates the effectiveness of the gradient descent approach as it consistently outperformed the conventional method, offering a robust solution for subsurface characterization in geophysical exploration.

Deep biospheric anaerobic microbial sulfate reduction and oxidative sulfur cycling have been studied in long sediment cores mainly acquired as part of IODP explorations. The most remarkable observation in many of these studies is the existence of an active sulfur cycle in the deep subsurface sediments that have very low organic carbon content and are presumably refractory. Here, we investigate the interstitial sulfate concentrations and sulfur isotope ratios in a 290 m-long sediment core collected from the eastern Arabian Sea at a water depth of 2663 m. Continuous decrease in porewater-sulfate concentrations with depth (up to 75 mbsf) coupled with enrichment in δ³⁴S_SO4 values suggests organoclastic sulfate reduction (OSR) processes attributed to the activity of sulfate-reducing bacteria (SRB) and retention of labile organic substrates amenable to the SRBs. Below a depth of 75 mbsf, the absence of further reduction in sulfate concentration indicates insufficient labile substrate to drive sulfate-reduction activity. An increase in sulfate concentrations at the deeper subsurface (below 128.5 mbsf) coupled with decreasing δ³⁴S_SO4 values may be attributed to the oxidation of Fe-sulfide to sulfate. The increase in porewater alkalinity in the lower part of the core has been linked to the silicate degradation process by CO₂ produced via the dissolution of CaCO₃. Compilation of previous studies from this core, along with our investigation, intrigues future research on organic matter reactivity and microbiological activity in deeper subsurface under oligotrophic depositional regimes.

Investigating the geochemical composition of bulk sediments stands as a crucial method for unraveling the complexities of various sedimentary processes. However, the intricacies arising from extensive datasets and alterations in sediment due to diverse factors often impede the clear identification of underlying patterns in geochemical fluctuations. In addressing these, employing multivariate statistical analyses has proven to be an invaluable tool for elucidating intricate patterns within large dataset. In this study, we focus on the utilization of Principal Component Analysis (PCA), a multivariate statistical technique, to uncover the underlying sedimentary processes influencing distinct geochemical dataset. Specifically, our attention is directed towards the examination of geochemical data from the previously published geochemical data of metasediments from Shimla and Chail group (referred to as SCM) and the mudflat sediments of Diu Island (referred to as DMS). Our PCA outcomes reveal that the initial three principal components (PC1, PC2, and PC3) account for 52.51% and 79.30% of the total variance within the SCM and DMS geochemical data, respectively. Notably, the negative loading of SiO₂, alongside positive loadings of incompatible elements and those associated with mafic rocks on PC1 within the SCM dataset, indicates sediment origins ranging from felsic to intermediate sources. Additionally, the coexistence of Th, U, Zr, and Sc, exhibiting positive loadings in PC1 and PC2, suggests a significant influence of reworking and recycling from felsic to intermediate sources. In the context of the DMS dataset, PCA analysis highlights the dominant influence of in-situ productivity and mafic sediment sources along the positive axis of PC1. Conversely, the negative axis of PC1 is shaped by intermediate and potentially other sources. Further granularity in interpretation reveals the positive axis of PC2 being attributed to weathering proxies, while the dominance of plagioclase minerals in the clayey fraction controls the positive axis of PC3. Through this investigation, our study underscores the essential role of PCA-assisted geochemical data analysis in unraveling the intricate web of processes contributing to the variance observed within sedimentary systems. By effectively distilling the multifaceted factors driving geochemical variability, this approach emerges as a pivotal asset in enhancing our understanding of sedimentary dynamics.

Abstract

Chandarpur–Raipur sequence in Chattisgarh basin is represented as siliciclastic-dominated Chandarpur Group and carbonate-dominated Raipur Group. Here, we introduce ‘Devsagar Sandstone Member’, the only sandstone-dominated member in the carbonate-dominated Charmuria Formation of Raipur Group, that marks a period of rapid siliciclastic deposition identifying a phase of forced regression between two carbonate platforms of Charmuria–Chandi formations, thereby indicating a drastic change in palaeogeography of Raipur Group. In addition, this study revised the litho-stratigraphy of Mesoproterozoic Chattisgarh basin to clarify the confusion raised due to the existence of different stratigraphy in different basinal parts and different nomenclature for the same lithologic units. Detailed geological mapping with facies analysis in the eastern part of the basin manifests the entire basin-fill succession as part of the Chattisgarh basin itself, rather than sub-dividing some parts as Baradwar sub-basin and Singhora proto-basin. Singhora Group deposited in Singhora proto-basin has already been presented as equivalent of Chandarpur Group. Here we propose, Bamandihi–Saradih–Raigarh formations of Raipur Group in Baradwar sub-basin, as lateral equivalent of Gunderdehi–Chandi–Tarenga formations of Raipur Group and Sarnadih–Nandeli formations of Kharsiya Group in Chattisgarh basin. Inferred depositional environment and tectonic setting of Chattisgarh basin support the lithostratigraphic revision, which will help in basin analysis as well as intrabasinal–interbasinal correlation in regional and global contexts.

Research highlights

• Devsagar Sandstone Member introduced as the only sandstone-dominated member in carbonate-dominated Charmuria Formation of Raipur Group.

• Devsagar Sandstone Member represents a tidal shelf in between two carbonate ramp platforms (Charmuria and Chandi), marking a period of rapid siliciclastic deposition and the only phase of forced regression in overall sea-level rising scenario of the carbonate-dominated Raipur Group.

• Stratigraphy of Chattisgarh basin revised. The entire Chattisgarh succession is represented as deposits of Chattisgarh basin only, without further subdivision into sub-basin and/or proto-basin, thus resolving the stratigraphic and basinal correlation problem.

Abstract

This study analyses surface wind speed trends over the north Indian region covering Delhi–National Capital Region (NCR) and adjoining areas (lying within latitude 25°–30°N and longitude 75°–80°E) for the recent 40-year period (1981–2020). The analysis reveals an annual stilling of 9.83 × 10⁻³ m/s/year for the study period. The seasonal analysis indicates the highest stilling in the summer by 14.57 (times {10}^{-3}) m/s/year in absolute terms. The daytime and night-time wind speed variation analysis revealed a significant difference between daytime and night-time wind speeds over the region. However, declining trends for daytime and night-time wind speeds could not be differentiated statistically, i.e., daytime and night-time speeds had been declining at an almost equal rate over the study period in the study region. Further, the dust concentration analysis revealed a significant rise in dust concentration of 0.72 µg/m³/year; the highest trend has been observed for the winter season. The increase in dust concentration and the stilling together make it a significant concern from a health perspective. The stilling may have further implications on the hydrological cycle, wind energy reliance, and other concerns, which affect the climate at the micro-scale. Rapid urbanisation seems to be the most prominent factor for stilling due to an increase in surface roughness, pointing towards a need for attribute analysis in future. The study further identifies challenges in meteorological studies, which include inherent cyclicity in the meteorological variables (such as wind speed and temperature), parameterisation (choice of the independent variable), the need for sophistication in data retrieval processes, including validation (training and testing) and a lack of adequate understanding about atmospheric phenomena for the region under study. These challenges must be systematically addressed in future research to achieve better and more consistent inferences from meteorological analyses.

Research Highlights

• An annual surface wind speed decline of 9.83 × 10⁻³ m/s/year has been observed over Delhi-NCR and adjoining areas since 1981.

• The declining effect is most pronounced in the summer season, amounting to 14.57 ×10⁻³ m/s/year.

• Dust concentration has been on continuous rise at the rate of about 0.72 µg/m³/year since 1981.

• The co-occurrence of dust concentration rise and wind speed decline may be a significant cause of deterioration of air quality in the region.

• The study envisages the need for systematic and holistic urban and built environment plan-ning.

Accurate land use-land cover mapping is essential to policymakers for future planning. This study aims to assess the land use-land cover dynamics and estimate its future projection in the Jagatsinghpur district of Odisha state from India. In recent years, cloud-based platforms like Google Earth Engine and domains like machine learning have attracted considerable attention from researchers. In this study, five machine learning algorithms, such as Classification and Regression Tree, Naive Bayes, Support Vector Machine, Gradient Tree Boost and Random Forest are experimented on the multitemporal Sentinel-1 C-band dataset from Google Earth Engine. The results are evaluated based on metrics like overall accuracy and Kappa statistics. The performance metrics indicate that Random Forest with 60 trees outperforms others. Next, the land use-land cover maps of the study area are generated with Random Forest classifier for the years 2017 and 2021. The results are compared to ESRI land cover maps and ESA world cover maps. The 2017 and 2021 maps are exported to QGIS, and these maps are used to generate a simulation map for 2021. The simulated land use-land cover map for 2021 indicates promising results with an overall Kappa value of 0.97 and a percentage of correctness of 98.21%. The simulated map is validated against a factual map. Finally, future projections of land-use changes are forecasted for the years 2030 and 2050 using QGIS-MOLUSCE. The predicted maps project a significant rise in agricultural and built-up areas. These findings will assist policymakers in future planning.