Arabian Journal of Geosciences最新文献

This study investigates the spatial distribution of rainfall in Nigeria, utilizing ground-based rainfall data from 48 weather stations and two long-term satellite-based precipitation products spanning 39 years (1981–2019). Employing statistical techniques and kriging interpolation methods, this study analysed annual and seasonal rainfall patterns. Correlation coefficient was also used to compare areal averages of satellite-based rainfall estimates and ground-based rainfall data in Nigeria and for each of the six eco-climatic regions. Results indicate significant regional disparities, with the Tropical Wet (Mangrove and Swamp) region receiving the highest mean annual rainfall (> 2,300 mm) and the Sahel Savannah experiencing the lowest (< 450 mm). Eco-climatic regions exhibit varying contributions to total annual precipitation, with mangrove swamps and tropical rainforests dominating. Notably, 76.4% of annual rainfall occurs during the June–August and September–November periods, with August witnessing peak precipitation levels. Over Nigeria, there are strong correlations between satellite precipitation estimates (SPEs) and ground data on a monthly and seasonal basis, but the correlations are weaker on an annual scale, especially in Sahel and Montane regions. While SPEs provide reliable short-term rainfall estimates, caution is advised for annual precipitation estimates, particularly in regions with lower correlations. This study highlights the need for more efficient water use methods, with an emphasis on enhanced storage systems, distribution networks, sustainable irrigation practices, and judicious consumption to address rainfall variability. The findings highlight the importance of understanding rainfall distribution for agricultural planning and regional climate assessments. By integrating ground-based and satellite-derived data, this study enhances knowledge of Nigeria's climate dynamics, facilitating informed decision-making and resource management strategies.

In designing suitable support systems and ensuring safe excavation of a tunnel, deformation and block failure assessment around the opening is a crucial aspect of tunneling. In this study, a distinct element modeling approach was employed to evaluate the distribution of failed blocks, failure modes, and displacements of the tunnels to gain insight into support recommendations for the Wabe Hydropower Project in central Ethiopia. For this purpose, three representative numerical models were developed considering different rock mass along the tunnel alignment. Subsequently, the influence region classification technique was introduced, and the models were systematically classified into three distinct regions. This technique enabled the consideration of blocky rock mass as discontinuum through the direct inclusion of field-measured joints with average spacings of 0.2, 0.56, and 1.2 m into a region surrounding the tunnel opening. The simulation results indicated that tunnels in closely jointed rock mass behave anisotropic, with failed blocks following the joint inclinations of N253/72 and N035/79 and exhibiting a tensile failure mode. Tunneling in the fault zone induced a shear failure mode, with a significant distribution of failed blocks aligned in the maximum principal stress direction. However, under low horizontal in situ stress, both shear and tensile failure could exist, with tensile failure affecting the roof and floor. Furthermore, tunnels in closely jointed rock mass are primarily influenced by horizontal displacement, whereas tunneling in fault zones led to both greater horizontal and vertical convergences, with horizontal displacement being more significant. Finally, the obtained results were used to propose support recommendations.

Seismic site classification not only is crucial for seismic hazard assessment but also influences the reliability of ground motion data. The present study classifies 81 locations where Uttarakhand State Earthquake Early Warning System (UEEWS) seismic sensors are installed in the Kumaon region. The ground motion records of earthquakes occurring between 2019 and 2023 have been used as the dataset for this work. A winnowing approach has been applied to select good records from the dataset, and then, spectral acceleration (SA) and pseudo-spectral acceleration (PSA) have been derived for all the records. The horizontal-to-vertical spectral ratio (HVSR) curves have been created using SA and PSA. Four methods with the eight classification approaches have been applied to classify the sites. The first method uses the predominant period obtained from the average HVSR curve of the site and classifies it according to the standard schemes. In the second method, three approaches estimate the site classification index (SCI) by correlating the site’s HVSR curve with standard HVSR curves. In the third method, time-averaged shear wave velocity (V_s30) from the depth of 30 m to the surface of the earth, is estimated using two different empirical models, while in the fourth method, PSA is normalized by peak ground acceleration (PGA). The results from all the approaches have been thoroughly examined and the final classification has been made by comparing them with the standard curves. Out of 81 sites, 31, 23, 1, 1, 6, 2, and 17 have been classified as classes I, II, III, IV, V, VI, and VII, respectively. The description of site categories has been explained in the subsequent sections. It has also been illustrated that the earthquake’s magnitude, epicentral distance, and depth do not affect the predominant period of the sites. The classification of sites plays a crucial role in advancing seismic hazard investigations of the Uttarakhand region, as strong ground motion records are the primary input along with the site’s conditions. This study will be valuable in helping to mitigate potential earthquake damages in the future.

This study provides a detailed review of the open pit mine planning process, focusing on the critical parameters that influence the stability, safety, and efficiency of mining operations. Historically, the importance of integrated mine planning and geomechanical understanding in bench design has been underestimated, leading to operational challenges. The primary objective of this review is to emphasize the significance of effective mine planning and design, highlighting key factors such as rock mass properties, bench geometry, stability considerations, blast design, and other operational elements that directly impact efficiency and safety. Optimizing bench design requires a careful balance of economic, geomechanical, and operational factors, including bench height, slope angle, blasting design, and equipment considerations, to enhance safety and productivity in open pit mining. Numerical modelling is crucial for simulating interactions between rock behavior, bench design, and mining processes, providing insights into stress distribution, material displacement, and potential failure mechanisms. Incorporating machine learning techniques in open pit mine planning introduces innovative solutions for design optimization. In conclusion, the paper proposes strategies for improving stability and productivity through integrated blasting protocols, advanced monitoring technologies, and machine learning for design optimization. Future research should focus on enhancing safety and productivity by refining modelling techniques and deepening the understanding of mine planning and design for sustainable mining operations.

In the context of global warming, the East African region has experienced frequent droughts, with severe impacts on local society and livelihoods. Kenya, in particular, is one of the most drought-affected countries in the region. In May 2023, Kenya experienced an unprecedented extreme drought event that posed a serious threat to the lives and property of the local population. This study focuses on this event, and through quantitative diagnostic analysis, tentatively examines the main controlling factors and possible influencing mechanisms that affect rainfall in Kenya during this event. The analysis results indicate that the anomalous vertical atmospheric motion in 2023, which influences the transport process of the vertical gradient of water vapor, is the main controlling factor of the Kenyan drought event, with the anomalous descending airflow playing a dominant role. Further analysis shows that the anomalous warming of sea surface temperatures in the southwestern Indian Ocean in May 2023 triggered an anticyclonic circulation over the western Indian Ocean, which significantly influenced the anomalous vertical atmospheric motion. This research provides a preliminary explanation of the causes of the drought event from an air-sea interaction perspective.

The shear strength of soil is a significant engineering property. Recently, the utilization of nature-based elements, including roots and fibers, to enhance soil shear strength for surface applications like erosion control has received considerable attention. The experimental program outlined in this paper encompasses direct shear testing on bare soil, soil-fiber, and soil-root specimens with diverse compositions for parameters. The current study utilized four locally sourced grassroots from the Himalayan region, along with a combination of natural and synthetic fibers, to investigate the enhancement of shear strength in surface soils. A fractional factorial method of experimental design has been implemented for laboratory testing programs. Additionally, data analysis has been conducted to determine factor contributions and optimum parameter for the most favorable results. The findings demonstrate that the incorporation of plant roots and fibers significantly affects the shear strength of the soil matrix. The root area ratio serves as an equivalent for fiber content in soil-root interaction research aimed at improving shear strength at the soil surface.

This study investigated the performance of machine learning models in predicting the FS and slip surface. The models considered include support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) algorithms. The slope stability analysis data for training of machine learning algorithms were obtained through the limit equilibrium method. This includes various scenarios of dry and homogeneous slope cases, encompassing a range of slope geometries (height (H), slope ratio (v/h)), and soil shear strength parameters (soil unit weight (γ), cohesion (c), friction angle (ϕ)). According to the evaluation using Taylor’s chart metrics, including standard deviation, correlation determination (R²), and root-mean-square error (RMSE), the XGBoost algorithm demonstrated the best performance. Additionally, employing the SHapley Additive exPlanations (SHAP) methodology revealed the significance order of variables as v/h > H > c > ϕ > γ for the factor of safety (FS) and H > v/h > c > ϕ > γ for the slip surface.

The Adigrat Sandstone Formation, representing the siliciclastic assemblage of the Lower Mesozoic succession, underlies the Gohatsion Formation in the Blue Nile Basin. Despite its stratigraphic and geological significance, the impact of diagenesis on the porosity value of the Adigrat sandstone remains poorly understood. By analyzing a detailed field description of two stratigraphic logs, along with the associated thin sections and additional correlated well data, the environment of deposition has been interpreted. The petrographic analysis was carried out to 30 sandstone samples collected from five selected outcrops, and complemented by the two stratigraphic columns. The main diagenetic processes affecting the Adigrat sandstone porosity were compaction, cementation, mineral dissolution, replacement, authigenesis, and recrystallization. The framework grain and cement relationship suggests an early quartz cement precipitation, followed by partial or intense calcite and hematite development in some samples as the second cementation phase. Feldspar alteration to lath-shaped kaolinite clusters causes kaolinite to act as a pore-lining and pore-filling cement, thereby reducing porosity. Conversely, the fracture and dissolution of some samples enhanced the fluid storage capacity. The estimated existing optical porosity (EOP) varies between 1 and 8%, with a mean value of 5%, of which 70% of the samples possess catenary and cul-de-sac porosities. Based on petrographic analysis, the sandstone is mineralogically categorized as sub-mature to mature. These findings significantly contribute to understanding the diagenetic evolution of the Adigrat Sandstone Formation, providing valuable insights for reservoir characterization and exploration strategies in the Blue Nile Basin (BNB).

Drought is one of the most destructive environmental hazards posing negative economic and social consequences. The country of Iran, which is located in the dry and semi-arid belt, is involved in much damage caused by drought every year, which makes it necessary to investigate. In this study, an attempt was made to investigate the frequency (number of occurrences) of severe and extreme droughts in the future. We considered of monthly averaged precipitation of 10 climate models of the fifth report of the Intergovernmental Panel on Climate Change (IPCC) for the basic period (1976–2005) and future periods (2020–2049, 2070–2099) under two scenarios (RCP4.5, 8.5). Using a new method, the difference between the average monthly precipitation of the models in the base period with the observed data and the inverse of the difference of each model was divided by the sum of the inverse of all models in each month (WP). Next, the average monthly precipitation of each model in the future period and the corresponding scenario was divided into the base period of that model (PCF), and at the end, for each month, the amount of WP was multiplied by the PCF of each model and their sum was obtained ((Delta P)). The value (Delta P), which is a 12-month time series, is introduced to the lars-wg model as a scenario file, and this model builds precipitation data based on this file. In the following, using the 12-month SPI index, according to the SPI index classification (values between − 1.5 and − 2 as severe drought and greater than − 2 as extreme drought), the total number of events in which the 12-month SPI (during the examined period in each station) being placed in the severe and extreme category was calculated. The estimation of error indices, especially R_Sqr (0.95), on average shows the accuracy of the combined weighted method and the Lars-Wg model in simulating precipitation. Also, the result presented in box plots shows an increase in the frequency of severe and extreme droughts in most of the country’s stations. Except group 3 (Southwestern and Western regions of the country), where the frequency of severe drought has decreased, in other groups, especially group 4 (60% on average), there is an obvious increase. The frequency of extreme drought in areas with good rainfall in the western and northern half of the country (especially groups 3 and 4) has declined (86% on average), while extreme events has decreased in group 1 with low rainfall. Considering that these areas are the main agricultural poles in the country, increasing the frequency of extreme drought can create harmful economic, social, and environmental consequences.

The primary intention of this study is to explore the ability of convolutional neural networks (CNNs) for forest classification using Compact Polarimetric (CP) data. Due to the phenomenal performance of the CNNs, more and more studies have tended to apply CNN-based methods to classify polarimetric synthetic aperture radar (PolSAR) images. In this study, three strategies were applied for this purpose. The first strategy involved designing and applying a CNN-based network to the Full Polarimetry (FP) mode of RADARSAT-2 C band, the simulated CP modes, and the reconstructed Pseudo Quad (PQ) modes. The results of these different modes were then compared with each other. In the second strategy, we compared the outcomes obtained from the first strategy with those from the Wishart classifier and the support vector machine (SVM) used in previous studies. Finally, the last strategy combined the CP modes to improve the classification outcomes further. Results showed that the CNN network outperformed other methods by using the CP modes for forest classification, and combining π/4 and DCP_L modes provided higher overall accuracy.