Journal of Earth System Science最新文献

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.

Abstract

The seismic quality factor (Q) is an important physical parameter to characterize seismic wave attenuation. Therefore, analyzing its spatiotemporal variation is essential to better understand tectonic activity, characterize earthquake source mechanisms, and assess seismic hazards. In this study, we used a single-scattering model to calculate Q for coda waves (Q_c values) generated by earthquakes with epicentral distances <100 km and magnitudes M > 2.0 recorded in 2000–2023 in the northeastern margin of the Qinghai–Tibet Plateau (the study area). The calculated dependence of Q_c on the bandpass filter central frequency f in the study area was ({Q}_{c}=left(72.40pm 9right){f}^{left(1.10pm 0.06right)}), ({Q}_{c}=left(100.95pm 15right){f}^{left(1.03pm 0.07right)}), and ({Q}_{c}=left(128.76pm 20right){f}^{left(0.97pm 0.07right)}) within lapse-time windows of length 20, 30, and 40 s, respectively. To estimate Q_c in distinct active tectonic and fault regions, we divided the study area into two subregions, the Haiyuan and Qinling active tectonic zones. We determined a strong spatial correlation between the Q_c distribution and tectonic activity in the study area, with correspondingly low Q_c values. Finally, by analyzing the temporal evolution of Q_c, we established that nearly all strong earthquakes (M > 6.0) that occurred in the study area in 2000–2023 were preceded by a 10–27% decrease in Q_c values, a phenomenon possibly related to the ‘rock dilatancy’ theory.

Research highlights

1. 1.

   Characteristics of code-wave attenuation in the northeastern margin of the Qinghai–Tibet Plateau.

2. 2.

   Strong correlation between Q_c values and active blocks distribution in the northeastern margin of the Qinghai–Tibet Plateau.

3. 3.

   Most strong earthquakes in the northeastern margin of the Qinghai–Tibet Plateau were preceded by a decrease in Q_c values.

This study has estimated the missing values of minimum temperature, maximum temperature and rainfall records of longer lengths, respectively, from 1994 to 1996 in Hatiya and 2000–2003 in Sandwip islands of the Bay of Bengal with harmonic regression analysis to realize the past climate. The work has provided past climate of records, which have justified with the mean absolute error, root mean squared error and skill score respectively 1.50, 2.00 and 0.84 for minimum temperature, 1.66, 2.10 and 0.48 for maximum temperature, and 8.60, 14.69 and –0.43 for rainfall for the stations with known records. The mean of the two estimations varies respectively for temperature and rainfall from –0.69 to 0.64°C and –0.36 to 4.79 mm, where one estimation is done with the proposed harmonic analysis and another estimation has been done with inverse-distance-weighting technique for the stations with missing records. The study is advantageous because it uses data from its own station on the island rather than data from neighbouring stations on the continent. It has avoided the probability of mixing up the continental climate with the climate of the island and vice versa. Hence, the estimations provided are spatially unbiased and meaningful past climate.

Abstract

Hyperspectral data has a significant count of spectral channels with an enhanced spectral resolution, which provides detailed information at each pixel. This data can be used in numerous remote sensing (RS) applications, along with mineral mapping. Mineral mapping is an important component of geological mapping, which helps in investigating the mineralization potential of an area. This work can be completed effectively by applying machine learning (ML) techniques to RS data. This paper proposes a stacked ensemble-based framework for mineral mapping using the dataset obtained by the Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG). The study area is situated in Jahazpur, Rajasthan, India. The purpose of this stacked ensemble-based model is to enhance the performance of ML-based mineral mapping. The proposed stacked ensemble model consists of two major elements: a base learner (Naïve Bayes, KNN, artificial neural network, decision tree, and support vector machine) and a stacked learner (random forest). The results of the experiments show that the stacked ensemble-based model has a lot of potential for accurately mapping the minerals talc, montmorillonite, kaolionite, and kaosmec. The proposed model has obtained an overall accuracy of 98.96%, an average accuracy of 98.21%, and a Kappa coefficient of 0.9628.

Research highlights

• A stacked ensemble-based model for mineral mapping is proposed.

• The well-known five conventional machine learning models (called base models) are investigated for mineral mapping.

• The performance of the proposed model is evaluated on the AVIRIG–NG dataset. The study area is situated in Jahazpur, Rajasthan, India.

• The proposed method outperformed all base models.

This study explores the historical presence of the El Niño-Southern Oscillation (ENSO) phenomenon during the Holocene and its impact on the Indian Summer Monsoon (ISM) and the East Asian Summer Monsoon (EAM). This investigation sheds light on an area with limited prior understanding. The primary objective is to analyse ISM variations from ~ 6000 to 1700 calibrated years before the Present (cal yr BP) and decipher their connection with the EAM. Sediment samples were obtained from core SK-291/GC-15, collected off the coast of Goa, and underwent comprehensive analysis, including examination of major, trace, and rare earth elements (REEs). The findings from geochemical proxies reveal that variations in sample compositions are primarily attributed to shifts in chemical weathering intensity rather than alterations in the source rock composition, and the sediments were deposited under consistent anoxic conditions. A noteworthy shift in the chemical weathering pattern was identified, particularly during the ~6000–4400 cal yr BP period, coinciding with the onset of intensified ISM around ~5400 cal yr BP. This intensified monsoon phase, recognised as the 5.4 ka event, coincides with the development of the Harappan civilisation, highlighting its historical significance. Notably, an inverse relationship between the ISM and EAM was observed during this 5.4 ka event – a phenomenon explained by the influence of ENSO on the Asian monsoon system.

Abstract

Identification and characterisation of reservoir facies is a prime factor in delimiting the hydrocarbon potential zones of a reservoir for hydrocarbon exploration. The geophysical logs, which are physical parameters of reservoir facies measured in the vicinity of boreholes, play a crucial role in the interpretation of reservoir facies. The present study deals with the identification of the lithology of the Limbodara oil field in the Cambay basin using machine learning (ML) techniques on geophysical logs. The supervised techniques of machine learning, such as support vector machines (SVM), artificial neural networks (ANN), and k-nearest neighbours (kNN), are used as nonlinear classifiers for the identification of lithology from nonlinear geophysical logs. The hyperparameters of the ML model are optimised using the grid search cross-validation (CV) method to increase the performance of the model, as evaluated by confusion matrix, area under receiver operating characteristics curve (AUC), precision, recall, and F1 score. The ML model used five geophysical parameters of two wells with four known distinguished lithologies (Class-A, Class-B, Class-C, and Class-D) for optimisation and training of the model. The optimised and trained model for each lithology for kNN, SVM, and ANN shows an overall correct prediction of true values with 85.4, 87.0, and 88.9%, respectively, from the confusion matrix. Apart from this, the receiver operative characteristics (ROC) also show that the overall area under the curve for each lithology is greater than 90%, and other evaluation parameters such as precision, recall, and F1 score show accuracy greater than 84%, except for the cases of Class C and Class D from SVM and ANN. Thus, the accuracy of each model from evaluation parameters suggests that the combined analysis of different ML models offers to select the optimised ML model for better results and validation to achieve and model the lithology with better precision.

Highlights

• A way out for obtaining litholog supplements at uncored section in boreholes

• Established ML assisted mapping function between wireline logs and lithologs

• Predicted litholog sequence with secure level of accuracy (>80%)

Rainfall plays a vital role in the field of agriculture as it affects agricultural production and associated economy. However, the changing trend of rainfall has become a global concern. So the study of changes in the trend of rainfall is necessary. In the present study, an innovative trend analysis method was adopted to assess the changing trend in the state of Nagaland. Data of 40 years was taken for performing the trend analysis using ITA. The entire process of trend change analysis was automated using Python programming. The analysis indicated that out of the 11 stations considered, three stations indicated a rising trend, eight indicated falling trends (annual), four rising and seven falling (monsoon), 0 rising and 11 falling (winter). The extent of trend change varied from –34.5 to 1.1. The spatial distribution of the trend change was also performed. It was observed that the southeast part of Nagaland’s rising trend was more pronounced compared to the southwest. The change was more prominent during the winter season followed by pre-monsoon and monsoon. The trend analysis is important for making appropriate water management decisions, such as water conservation in areas with falling trends and soil conservation in areas affected by rising trends.

The global climate models (GCMs) are sophisticated tools for determining how the climate system will respond. However, the output of GCMs has a coarse resolution, which is unsuitable for basin-level modelling. Global climate models need to be downscaled at a local/basin scale to determine the impacts of climate change on hydrological responses. The present study attempted to evaluate how effectively various large-scale predictors could reproduce local-scale rain in 35 different locations in India using artificial neural networks (ANN), change-factors (CF), K-nearest neighbour (KNN), and multiple linear regression (MLR). The selection of predictors is made based on the correlation value. As potential predictors, air temperature, geo-potential height, wind velocity component, and relative humidity at specific mean sea-level pressure are selected. The comparison of four different downscaling methods concerning the reproduction of various statistics such as mean, standard deviation at chosen locations, quantile–quantile plots, cumulative distribution function, and kernel density estimation of the PDFs of daily rainfall for selected stations is examined. The CF approach outperforms the other methods at almost all sites (R² = 0.92–0.99, RMSE = 1.37–28.88 mm, and NSE = –16.55–0.99). This also closely resembles the probability distribution pattern of IMD data.