Journal of Earth System Science最新文献

Abstract

Understanding local-scale climate change is vital to developing adaptive strategies in the face of the century-old river of global warming posing a threat to humanity. This study focuses on assessing temperature and rainfall trends in Beni City, using monthly and yearly (1990–2020) weather station data. Climate variability was analysed using the standardised variable index, and rainfall concentration patterns were highlighted using the precipitation concentration index (PCI). The climate trends were analysed by using the Mann–Kendall test and Sen's slope estimator. The findings indicated that the T_min is 18.82±0.62°C, and T_max is 28.22±0.75°C, resulting in a mean temperature of 23.52±0.57°C. The annual and seasonal temperature trend analysis indicated that a significant warming trend was observed in both T_min and T_max. Beni City's precipitation trends also showed a mean annual rainfall of 1988.38±416.59 mm, with significant year-to-year variations. Annual rainfall analysis exhibited a slight upward trend; meanwhile, the seasonal trend analysis revealed an increase in rainfall during Mar–Apr–May (MAM) and Aug–Sep–Oct–Nov (ASON) seasons with roughly no discernible trend during Dec–Jan–Feb (DJF), and Jun–Jul (JJ) seasons. Overall, annual and seasonal analyses of specific temperature and rainfall patterns have shown pronounced warming and increased rainfall in the study area.

Research highlights

• The study reveals significant trends in temperature and rainfall in Beni city, Democratic Republic of Congo, over a 31-year period (1990–2020).

• Both minimum and maximum temperatures show significant warming trends, with the most recent decade witnessing substantial increases in maximum temperatures.

• Rainfall patterns exhibit variations, with a slight upward trend in annual rainfall, although the 1990s experienced a notable decrease in precipitation.

• Monthly analyses highlight specific temperature and rainfall patterns with some months experiencing pronounced warming and increased rainfall.

Abstract

The siliciclastics make the major part of the Proterozoic Kumaun Lesser Himalaya region. These share similar appearance, sedimentology, and petrography. In the absence of fossils and any other age constraint, these are named differently in literature, i.e., Nagthat, Bhowali, Lariakantha, and Berinag quartzites, based on their locality, sedimentological and petrographical characteristics. Their stratal disposition and their regional correlation are always debatable. The present work addresses the stratal disposition of these Proterozoic siliciclastics using the detrital zircon (DZ) U–Pb geochronology. The study suggests that the siliciclastics of Bhowali, Lariakantha, Nagthat and Berinag in the Kumaun region are equivalent and show the maximum depositional age (MDA) of ~1850 Ma. However, the Nagthat Formation in Garhwal Lesser Himalaya has a Neoproterozoic DZ depositional age (~850 Ma) and is a different identity. This study suggests that in the Kumaun Lesser region (inner and outer), only Palaeoproterozoic siliciclastic is present. Also, they can be grouped under Berinag with its implications about the inner and outer Lesser Himalayan division and the relationship with younger strata of Blaini Formation.

Research highlights

• U-Pb Detrital zircon geochronology from siliciclastics, Kumaun Lesser Himalaya (inner and outer) region

• Only Palaeoproterozoic detrital zircon ages for Kumaun Lesser region (both inner (ILH) and outer (OLH))

• Continuous Paleoproterozoic sedimentation in a single basin

The recent subsidence at Joshimath in the Indian State of Uttarakhand led to the displacement of thousands of residents. Large cracks developed in the buildings and on the ground. No extensive and comprehensive deformation measurement of this event has been made. In this study, we use both PS and PSDS InSAR time series to investigate the magnitude, spatiotemporal as well as kinematic evolution of this slow-moving landslide. Eighty-seven ascending Sentinel-1 scenes with a temporal baseline of 1056 days from 2020 to 2023 were stacked for interferometric analysis. StaMPS is employed to identify PS points by their amplitude and phase information. TomoSAR is utilized to stipulate a coherence matrix to form a dense PSDS network of interferograms to surge point density for suitable phase unwrapping. PS and DS points are coupled to develop slope velocity maps revealing mean displacement rates of –84 mm for PS and –107 mm for PSDS, respectively. Cross-section profiles drawn on the slopes of subsidence show target scatterers on CS1, CS2 and CS4, yield a cumulative displacement of 400 mm in the last 3 years. CS3 and CS5 show a total displacement of about 350 mm. This study applies PSDS time-series InSAR to decipher ground movement in traditionally decohered environments. It also seeks to establish the boundaries and intensity of subsidence to aid in the mitigation of failure progression.

In machine learning, ensemble learning methods (ELM) consist of combining several machine learning algorithms to obtain better quality predictions compared to a single model. The basic idea of this theory is to learn a set of classifiers and allow them to vote. In this paper, to correctly apply the ELM for enhancing of an artificial neural network (ANN) performances, a strategy was devised which is to divide the data to be classified into two categories, ‘easy-to-classify’ category and ‘difficult-to-classify’ category using a main ANN. Hence, reliable ANN and unreliable ANN are created and applied for the classification of ‘easy-to-classify’ data and for the classification of ‘difficult-to-classify’ data, respectively. The AdaBoost algorithm and Bagging algorithm are implemented separately on the unreliable ANN. To increase performance, the AdaBoost results and Bagging results are merged. The developed scheme is applied to remote sensing images from Meteosat Second Generation (MSG). The final results show very interesting performances in the case of the fusion of the results from AdaBoost-ANN and the results from Bagging-ANN (Ada/Bag-ANN). Indeed, the POD, FAR, CSI and Bias pass from 87.2%, 17.4%, 80.8% and 1.3 (ANN) to 96.8%, 06.8%, 92.7% and 1.1 (Ada/Bag-ANN), respectively. The same trend was observed in the case of precipitation estimates. The estimates obtained from the developed model (Ada/Bag-ANN) largely surpass those obtained from the use of ANN without ELM. Compared to ECST (Enhanced Convective Stratiform Technique), EPSAT-SG (Second Generation Satellite Precipitation Estimation), TAMSAT (Tropical Applications of Meteorology using SATellite), and RFE-2.0 (Rain Fall Estimate) which showed correlation coefficients of 87%, 81%, 76% and 71%, respectively, the Ada/Bag-ANN method shows significantly better results with a correlation coefficient of 94%.

In the current study, a seismic inversion based on a hybrid optimisation of genetic algorithm (GA) and pattern search (PS) is carried out. The GA is an approach to global optimisation technique that always converges to the global optimum solution but takes much time to converge. On the other hand, the PS is a local optimisation technique and can converge at local or global optimum solution depending on the starting model. If these two techniques are used together (here termed hybrid optimisation), they can enhance one's benefit and reduce the drawbacks of others. The present study developed a methodology to combine GA and PS in a single flowchart and utilise seismic reflection data exclusively to predict porosity and impedance volume in inter-well regions. The algorithms are initially tested on synthetically created data based on the wedge model, the coal coking model, and the 1D convolution model. The performance of the algorithm is remarkably acceptable, according to the error analysis and statistical analysis between the inverted and the anticipated results. After that, the field post-stack seismic data from the Blackfoot field, Canada, is transformed into impedance and porosity using a developed hybrid optimisation technique. The inverted/predicted sections show very high-resolution subsurface information with impedance varying from 6000 to 14000 m/s×g/cc and porosity varying from 5 to 40% in the region. The error decreases from 1.0 to 0.5 for impedance inversion, whereas it varies from 1.4 to 0.5 for porosity inversion within 3000 iterations, which cannot be achieved by a single optimisation technique. The findings also demonstrated a sand channel (reservoir) anomaly with low impedance (6000–9000 m/s×g/cc) and high porosity (12–20%) in between 1040 and 1060 ms time intervals. This study provides evidence that subsurface parameters like acoustic impedance or porosity may be promptly and affordably determined using seismic inversion based on hybrid optimisation. The developed methodology is very helpful in finding subsurface parameters in a limited time and cost, which cannot be achieved only by global or local optimisation.

Abstract

We report new Sm–Nd whole rock-mineral isochron ages of 2514 ± 13 Ma (MSWD = 0.79) and 2651 ± 95 Ma (MSWD = 7.4) from two east coast dykes (ECD) of Southern Granulite Terrain (SGT), India. The ages from the representative mafic dyke samples correspond to the time of intrusion of ECD into the eastern part of SGT, indicating the presence of an older Archean crust in SGT near the Pondicherry coast. The Sm–Nd ages obtained from the present study, along with geochronological information from Singhbhum Craton, suggest a magmatic linkage between SGT (including southern Dharwar Craton) and Singhbhum Craton during the Neoarchean period. The older ages obtained from the mafic dykes of the present study are comparable with the Sm–Nd ages of older mafic dykes from Nuggihalli green stone belt of Western Dharwar Craton (WDC), Pb–Pb ages of mafic dykes from Singhbhum Craton of India and the U–Pb ages from Pilbara and Kaapvaal cartons. These comparisons unlock a clue to Neoarchean (2.8–2.5 Ga) paleogeographic reconstructions of Pilbara, Kaapvaal, Singhbhum cratons, northern SGT (including southern Dharwar Craton) and also provide an opportunity for wide windows of research to be undertaken considering the dykes from SGT.

Research highlights

• Evidence of Neoarchean magmatism from East coast dykes near Pondicherry coast of Southern Granulite Terrain, India.

• Sm–Nd ages of 2514 ± 13 and 2651 ± 95 Ma represent the time of intrusion of east coast dykes in Southern Granulite Terrain.

• Isotope age indicates the presence of ~2.7 Ga older Archean crust near Pondicherry coast of Southern Granulite Terrain.

• Geochronological studies reveal a magmatic linkage between Southern Granulite Terrain and Singhbhum craton.

• The present study provides clues to the connection between Pilbara, Kaapvaal with SGT and Singhbhum cratons.

Abstract

The method of target classification known as hyperspectral imaging (HSI) is commonly used in the field of remote sensing. However, recent research has shown that categorizing HSI can be problematic due to the limited availability of labelled data. There is significant interest in applying this technique to hyperspectral data. Previous graph neural network (GNN)-based methodologies often used a graph filter to obtain HSI properties, but the potential advantages of various graph neural networks and graph filters have not been fully exploited. GNNs often operate under the assumption that a node’s neighbours are independent of each other, neglecting potential interactions among them. To overcome these limitations, graph attention neural network-based remote target classification (GANN-RTC) has been proposed. It has the ability to handle both the labelled and unlabelled datasets. To evaluate the performance of GANN-RTC, we compared it with existing methods using performance measures such as individual class accuracy, overall accuracy, and the Kappa coefficient. The findings indicate that the GANN-RTC yields enhancements in OA, ICA, and KC by 2.32, 7.89, and 2.47% for the Cuprite dataset and 4.79, 11.85, and 2.82% for the Pavia University dataset.

Research highlights

• The research focuses on remote target classification in hyperspectral imaging using a Graph Attention Neural Network.

• Previous methods in this field have not fully utilized the potential advantages of graph filters and graph neural networks.

• The proposed approach overcomes limitations by considering interactions between neighbouring nodes and can handle both labelled and unlabelled datasets.

• Performance evaluation shows significant improvements in overall accuracy, individual class accuracy, and the Kappa coefficient compared to existing state-of-the-art methods.

In this study, the focus is on predicting the properties of rocks beneath the Earth’s surface using global optimisation techniques such as genetic algorithms (GA), simulated annealing (SA) and particle swarm optimisation (PSO). The goal is to minimise the difference (error) between actual seismic data and synthetic (computed) seismic traces. Global optimisation is an approach that is independent of the initial model and aims to identify the global minimum of an objective function. In contrast, local optimisation relies on the accuracy of the initial model, and if an accurate initial model is not provided, it may become trapped in a local minimum, leading to an inaccurate representation of the subsurface model. What makes global optimisation powerful is that it does not get stuck in local minima (suboptimal solutions), but seeks the absolute best solution in the entire search space. This property is crucial in seismic inversion, where finding the most accurate representation of subsurface properties is of utmost importance for geophysical applications. The study includes one synthetic example and one real dataset, with a specific emphasis on evaluating acoustic impedance rock properties. While acoustic impedance is characteristic of rock layers, seismic data represents properties at the interfaces between these layers. Consequently, seismic data is highly valuable for gaining detailed insights into the subsurface. The results of the optimisation process provide exceptionally detailed views of the subsurface, aiding in the interpretation of seismic data. GA, SA and PSO algorithms perform well, both with synthetic data and real data. The inversion process identifies a zone with low acoustic impedance, corresponding to a prominent seismic anomaly. The evaluation of the inverted outcomes reveals that the impedance within the area ranges from 4300 to 4700 m/s*g/cc, situated within a specific time range of 900–950 ms in the seismic data of F3-block, Netherland.

Rock magnetic analyses of easternmost Indus Molasses of Nyoma–Rhongo section, Ladakh Himalaya have been performed. The thermomagnetic curve gives Curie point (Tc) information on the constituent magnetic minerals, which vary between 574° and 592°C. The hysteresis loops are harmonious and the resultant remanence ratio (Mrs/Ms) ranges between 0.10 and 0.19 and the coercivity ratio (Bcr/Bc) between 1.91 and 3.07. The domain states of magnetic grains majorly belong to the pseudo-single domain (PSD) state. The isothermal remanent magnetization (IRM) acquisition curves show saturation ranges between 250 and 300 mT, and the coercivity spectra show coercive force ranging between 24 and 41 mT. These investigations indicate that magnetic mineralogy in samples is predominantly controlled by fine to medium-sized PSD state magnetite and accessorily Ti-poor magnetite, pyrrhotite, and greigite. This magnetic mineralogy seemed homogenous and was not considerably affected by weathering, lithogenesis and geotectonic events, suggesting their deposition over well-developed palaeogeography with small-scale tectonic modulations. The best possible source for Indus Molasses, as identified in the current study is the Ladakh batholith having an affinity to the Eurasian Plate and these interpretations are in line with the literature.

Climate change can have adverse effects on various ecosystems on the globe, with the cryosphere being affected to a significant extent. Of the cryosphere, mountain or alpine glaciers are essential resources for freshwater and various ecosystem services. Glacial ablation is the process of removal of snow and ice from a glacier, which includes melting, evaporation, and erosion. The increase in temperature on the Earth due to climate changes is causing rapid glacial abrasion. The rapid global decline in alpine glaciers makes it necessary to identify the key drivers responsible for a glacial retreat to understand the eventual modifications to the surroundings and the Earth’s ecosystem. This study attempts to understand the influence of different driving factors leading to glacier retreat using Machine Learning (ML) and Remote Sensing (RS) techniques. Three models have been developed to estimate the glacial retreat: Feedforward Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM). The RNN performed the best with an average training and validation accuracy of 0.9. The overall shift of the area estimate has been identified over 10 years. The model thus generated can lead to a better understanding of the region and can provide a baseline for policy and mitigation strategies in the future.