Arabian Journal of Geosciences最新文献

This study investigates hybrid deep learning approaches for predicting human thermal comfort indices in urban environments, based on feels-like temperature—a metric that captures physiological responses to weather conditions by human beings more effectively than conventional temperature readings. This parameter is determined based on the 'BiLSTM-GRU-Attention’ model, developed to forecast heat index and wind chill and serve as critical indicators of perceived thermal stress. The model was trained on meteorological data from Patna City, using air temperature, specific humidity, and wind speed as input features. For enhancing performance evaluation, five statistical metrics were employed: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R²). Results demonstrated high predictive accuracies. Heat index forecasts achieved MAE between 55.55 and 51.5, MSE of 4.809, RMSE between 7.453 and 7.17, MAPE of 0.044, and R² of 0.9649. Wind chill predictions performed even better, with MAE of 1.491, MSE of 3.88, RMSE of 1.97, MAPE of 0.0195, and R² of 0.9739. Individual parameter forecasts showed excellent correlation for air temperature (R² = 0.9748) and specific humidity (R² = 0.9424), while wind speed predictions were less accurate (R² = 0.2396), likely due to urban atmospheric variability. The study of GRU-Attention, BiLSTM-GRU-Attention, and CNN-BiLSTM-Transformer models using three parameters: temperature, wind speed, and humidity and extracting two derived parameters, heat index and wind chill, with five statistical matrices: R², MSE, RMSE, MAE, and MAPE establishes a reliable framework for predicting human-centered thermal indices, offering valuable insights for urban weather forecasting systems. These findings support public health strategies and urban climate resilience planning by emphasising thermal comfort over traditional meteorological metrics in the context of increasing climate variability.

This research focuses on the evaluation of physiochemical characteristics and technological properties of illitic-rich clays endowed in the Okposi-Uburu region, to assess their suitability as raw materials for structural ceramic and brick production. To achieve this, mineralogical compositions and elemental analyses were investigated via X-ray diffraction (XRD) and X-ray fluorescence spectroscopy, respectively. Evaluations of physical characteristics were performed by particle-size analysis, Atterberg limits, and total organic content tests. As part of the ceramic evaluation process, technological tests were performed on clays fired at a conventional temperature range of 850–1050 °C. In terms of mechanical strength, the compressive resistances were investigated via three-point loading strength tests. From the results, XRD revealed the predominance of illite, quartz, flux-inducing minerals (K-feldspar, carbonate), and accessory phases. The illite, which promotes glassy phase formation, accompanied by significant percentages of SiO₂ (42.6–56.4%), Al₂O₃ (21.9–30.3%), and K₂O (2.3–6.9%), signifies good materials for the production of ceramic and terracotta building materials. Low amounts of Fe₂O₃ with high alkali contents positively influenced the ceramic properties. Using ternary diagrams, particle-size analysis revealed adequate characteristics with good plasticity. The extruded specimen revealed red coloration attributable to hematite present in mineral compositions. At sintering temperatures above 850 °C, mineral transformations occurred with the crystallization of new phases. The specimen revealed the development of earlier densification, good linear shrinkage, reduced water absorption, and enhanced mechanical performance, with compressive strength ranges of 8.5–9.9 MPa, attributable to the sintering process. The silicon–aluminum compositions, based on technological characteristics, indicate that the clays satisfy favorably as raw materials according to ASTM standards and specifications for structural ceramic and building brick production.

Remote sensing (RS) technologies play a pivotal role in acquiring massive volumes of spatial data across various domains. To augment the capabilities of RS systems such as scene classification and change detection, machine learning (ML), especially deep learning (DL) networks, has achieved great success, but many of them often rely on a large number of labeled samples for supervision. As sufficient labeled training data are not always ready due to, e.g., continuously emerging RS datasets and costly sample annotation in real-world applications. In order to address the issue of sample scarcity, many studies prefer to utilize auxiliary information including those in the form of knowledge graph (KG) and zero-shot learning (ZSL) to reduce the reliance on labeled samples. To exclusively focus on discussing related technologies, this paper thoroughly reviews zero-shot learning (ZSL) and knowledge graphs (KG) with remote sensing (RS), highlighting their individual benefits and combined potential. Additionally, it discussed constructing methods of specific KGs for RS applications, emphasizing semantic relationships for contextualized information retrieval. The paper discusses the advantages of integrating ZSL & KG methods in RS applications, particularly in scene classification, and then presents emerging approaches based on ZSLKG across various domains which quotes the potentiality of ZSLKG and finally, suggests potential RS applications complying ZSL & KG. Overall, it highlights the transformative impact of ZSLKG convergence on enhancing the intelligence and efficiency of RS applications.

The excavation of deep rock masses by blasting is influenced by both the static and dynamic stresses produced by the detonation of explosives. Based on the analysis of explosive effects, formulas for explosive stress and energy have been derived. A computational model for blasting in a large cavity with four boreholes has been established. Numerical simulations were performed using the finite element analysis software ANSYS-LS/DYNA, exploring eight distinct in situ stress scenarios, which encompassed both bidirectional equal pressure and bidirectional unequal pressure conditions. The results show that explosive cracks in the four boreholes facing the cavity are denser, and the rocks in the excavation area are more fragmented. This confirms that the presence of a large cavity enhances the fragmentation effect of the blasting excavation. Given that the in situ stress is significantly lower than the pressure produced by the shock wave during detonation, its influence on the development of the fractured zone is minimal. However, it significantly influences the length and morphology of the cracks. In the blasting of rock masses with high in situ stress, the distance of crack propagation between boreholes diminishes with increasing levels of in situ stress. The cracks predominantly extend in the direction of the maximum principal stress. Therefore, arranging the boreholes along the direction of the maximum principal stress and reducing the spacing between boreholes is beneficial for connecting and penetrating the cracks between boreholes, resulting in a better blasting excavation surface.

To investigate the influence of thermal effects on the formation and distribution of sterane compounds in crude oil, we conducted a systematic geochemical analysis of 26 typical marine crude oil samples and one pyrolytic simulation sample from the Tarim Basin. The results reveal significant variations in sterane composition and distribution across maturity stages (Ro: 1.0%–2.5%). During the highly mature stage, thermal cracking significantly reduces sterane content. In the over-mature stage, a transient increase in sterane abundance correlates with the cracking of high-molecular-weight hydrocarbons. As maturity increases, differential generation and cracking rates of C₂₇ and C₂₉ regular steranes drive a characteristic trend: the C₂₇/C₂₉ regular sterane ratio initially decreases then increases. Consequently, this ratio cannot serve as a reliable source indicator in mature to highly mature stages. Similarly, divergent thermal stabilities cause a “reversal” in the C₂₉ sterane isomerization parameter at high maturity. Hence, at high thermal maturity levels, steranes become less applicable due to the equilibrium. Detected sterane isomerization parameters cannot be directly applied to constrain other geochemical effects. Furthermore, the ratio of rearranged to regular steranes exhibits a strong positive correlation with maturity, establishing its utility as an effective maturity parameter for crude oils in mature to highly mature stages.

Seismic-sequence stratigraphy and hydrocarbon potential of the “UYI” Field, located on the onshore Coastal Swamp depobelt of the Niger Delta, were carried out to ascertain the gross stratigraphic framework, predict lithology, depositional environment, and the implications for hydrocarbon exploration within the field. A combination of composite wireline logs and 3D seismic data was utilized to delineate nine stratigraphic surfaces (which comprise four sequence boundaries, SBs, eight maximum flooding surfaces, MFSs, and two transgressive surfaces, TSs) through the integration of log pattern and motifs, and information from biostratigraphic report. Also, grey level co-occurrence matrix (GLCM) seismic attribute was used to identify few subtle reflection terminations and an erosional channel incision that characterizes stratigraphic surfaces on the seismic. The four sequence boundaries (SB1 to SB4) subdivide the interval of study, within the Agbada Formation, into three Upper Miocene 3rd-order depositional sequences (SQ1 to SQ3) and their associated progradational to retrogradational cycles of system tracts (highstand, HST, lowstand, LST, and transgressive, TST). The thicknesses of these sequences, which are affected by the faults identified in the field, tend to increase towards the south and south-westerly directions (the general depositional trend of the Niger delta). Seismic facies analysis, integrated with lithologic interpretations from gamma ray log from the wells, shows sand and shale lithologies deposited on a broad shelf (upper to lower shoreface) to slope environment in a mixed high and low energy settings with good exploration prospects. Good sand distribution in the LSTs and HSTs may form favorable reservoirs while the shales of the HSTs and TSTs may constitute good source and seal rocks. The ubiquitous growth faults, with their associated rollover anticlines, within the field form the trapping mechanism.

Determining the productivity index (PI) of horizontal wells in conventional/unconventional reservoirs is crucial for efficient hydrocarbon recovery. Existing models tend to overestimate the PI of longer horizontal wells due to simplified assumptions related to drainage geometry and pressure loss. Thus, this study introduces a novel model that accurately integrates Darcy’s law, pseudo-steady state flow, flow convergence, and multiple pressure loss mechanisms, including pipe bends, kinetic energy, accumulation, and friction. The results showed that while longer wells generally improve PI, additional pressure losses reduce this advantage. Increased horizontal permeability and formation thickness enhance PI, with the doubling of permeability causing up to an 80% increase. Sensitivity analysis demonstrated that the most significant factors affecting PI are formation volume factor, oil viscosity, horizontal permeability, formation thickness, and well length. These findings underscore the importance of optimized well design, flow line maintenance, and data monitoring in optimizing hydrocarbon recovery from horizontal wells.

Lakes and their catchments in dryland regions are increasingly vulnerable to climate variability and intensified drought conditions worldwide. In the Lake Singida catchment, these challenges remain largely unexplored, resulting in significant impacts on both human populations and natural ecosystems. This paper analyzes temporal rainfall and temperature patterns while assessing drought conditions from 1991 to 2020. Our findings reveal an overall increase in rainfall by 415 mm, along with rising temperatures by 1.7 °C and 0.45 °C for minimum and maximum temperatures, respectively. The Rainfall Anomaly Index (RAI) indicates that the catchment has experienced below-average rainfall by 60% of the time, with extreme dryness recorded in 2003 and 2005 and extreme wetness in 1997 and 2020. Furthermore, the Standardized Precipitation and Evapotranspiration Index (SPEI) shows a significant rise in drought conditions over longer periods (18–24 months), with increasing trends identified through the Kendall test. These results highlight the complex dynamics between climate variability and drought, emphasizing the need for effective adaptation strategies to enhance the conservation and management of lake resources in the catchment.