Arabian Journal of Geosciences最新文献

{"title":"Comprehensive tsunami hazard assessment for Wudam As-Sahil, Northern Oman: Integrating deterministic and probabilistic approaches","authors":"Issa El-Hussain,&nbsp;Zaid Al-Habsi,&nbsp;Rachid Omira,&nbsp;Ahmed Deif,&nbsp;Adel Mohamed,&nbsp;Maria Ana Baptista,&nbsp;Yousuf Al-Shijbi","doi":"10.1007/s12517-025-12179-4","DOIUrl":"10.1007/s12517-025-12179-4","url":null,"abstract":"<div><p>Tsunamis pose serious threats to coastal regions, particularly regions with critical infrastructure. Recent events in the Indian Ocean and Japan have demonstrated the necessity of conducting comprehensive tsunami hazard analyses across regions including Oman which has experienced historical tsunamis generated from Makran Subduction Zone (MSZ). This study seeks to assess the tsunami hazard for Wudam As-Sahil coast in northern Oman using both deterministic and probabilistic approaches, focusing on earthquake-generated tsunamis from the MSZ. The research employs Deterministic Tsunami Hazard Assessment (DTHA) to model worst-case tsunami scenarios and Probabilistic Tsunami Hazard Assessment (PTHA) to estimate wave height probabilities over various exposure times. Numerical models simulate tsunami generation, propagation, and inundation based on historical and hypothetical earthquake events. The DTHA results indicate that maximum tsunami wave heights could reach 3 m. In contrast, PTHA findings suggest a low probability of waves exceeding 1 m. Furthermore, this study identified Mw 7.2 western MSZ scenario as the most hazardous scenario for Wudam As-Sahil coast with potential run-up heights reaching up to 2.7 m. The findings underscore the moderate tsunami risk facing the Wudam As-Sahil coast. The hazard assessments provide valuable insights for disaster preparedness, indicating areas in need of mitigation measures and emergency planning efforts.\u0000</p></div>","PeriodicalId":476,"journal":{"name":"Arabian Journal of Geosciences","volume":"18 1","pages":""},"PeriodicalIF":1.827,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crop type discrimination through low cost proximal RGB imaging and multivariate analysis","authors":"Koushik Banerjee,&nbsp;Suman Dutta,&nbsp;Bappa Das,&nbsp;Debasish Roy,&nbsp;Suman Sen,&nbsp;Bhabani Prasad Mandal,&nbsp;Arghya Chatterjee","doi":"10.1007/s12517-024-12165-2","DOIUrl":"10.1007/s12517-024-12165-2","url":null,"abstract":"<div><p>The current study is an attempt to use low cost red green blue (RGB) image–based vegetation indices (VIs), obtained from simple RGB camera, in separating six different field crops. To achieve this, sixteen VIs were calculated and used as inputs in different multivariate analysis for separating wheat (<i>Triticum</i> spp), mustard (<i>Brassica</i> spp), cabbage (<i>Brassica oleracea</i>), pigeon pea (<i>Cajanus cajan</i>), brinjal (<i>Solanum</i> app) and chickpea (<i>Cicer arietinum</i>). Based on the classification and regression tree (CART) analysis, the study identified Green Red Ratio Index (GRRI), Color Intensity Index (INT), Color Index Of Vegetation (CIVE) and Woebbecke Index (WI) were statistically significant (<i>p</i> &lt; 0.05) in discriminating six different crops. The results obtained from CART analysis were subsequently compared with discriminant analysis, which showed an accuracy of 96.3% of classifying different crops. Hence, out of 16 indices, the study meaningfully identified four most sensitive VIs that can be used to classify different field crops. The information achieved in this study can help in commercial and scientific decision-making, planning in agribusinesses, and can be an important tool for conducting crop survey at regional scale.</p></div>","PeriodicalId":476,"journal":{"name":"Arabian Journal of Geosciences","volume":"18 1","pages":""},"PeriodicalIF":1.827,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142963072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The study of water cut in the AB reservoir unit of Zubair formation at South Rumaila oilfield, Southern Iraq using petrophysics, geostatistics, and machine learning techniques","authors":"Alaa M. Al-Abadi,&nbsp;Amna M. Handhal,&nbsp;Esra Q. Saleh,&nbsp;Mustafa Kamil Shamkhi Aljasim,&nbsp;Amjad A. Hussein","doi":"10.1007/s12517-024-12173-2","DOIUrl":"10.1007/s12517-024-12173-2","url":null,"abstract":"<div><p>This study investigated the spatiotemporal variation of water cut in the AB reservoir unit of the Zubair Formation at the South Rumaila oilfield in Iraq using petrophysics, geostatistics, and machine learning techniques. The study found that the spatial distribution of petrophysical properties such as porosity, permeability, volume of shale, and unit thickness had little impact on the distribution of water cut. The most important factor was the rates of water injection and oil production. The study also found that the AB unit is homogeneous rather than heterogeneous, and this heterogeneity does not play a crucial role in the evolving water cut across the oilfield. The study of historical water cut data showed that the northern part of the oilfield had a higher water cut than the central and southern areas in 2012. However, as production and injection rates increased, the entire oilfield saw significant increases in water cut. Modeling of water cut using four machine learning algorithms (random forest, cubist, support vector machine, and linear regression) and a multi-layer perceptron deep learning technique showed that the random forest and cubist algorithms were the best in both training and testing stages. The stand-alone models of these algorithms for each well location can be used to quickly and easily predict water cut values throughout the oilfield, providing a way to efficiently manage the AB reservoir unit.</p></div>","PeriodicalId":476,"journal":{"name":"Arabian Journal of Geosciences","volume":"18 1","pages":""},"PeriodicalIF":1.827,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142963071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial distribution of rainfall in Nigeria","authors":"Afeez Alabi Salami,&nbsp;Rhoda Moji Olanrewaju,&nbsp;Katherine Olayinka Bakare,&nbsp;Olushola Razak Babatunde","doi":"10.1007/s12517-024-12168-z","DOIUrl":"10.1007/s12517-024-12168-z","url":null,"abstract":"<div><p>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 (&gt; 2,300 mm) and the Sahel Savannah experiencing the lowest (&lt; 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.</p></div>","PeriodicalId":476,"journal":{"name":"Arabian Journal of Geosciences","volume":"18 1","pages":""},"PeriodicalIF":1.827,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical analysis for failure and deformation assessment of the waterway tunnel, Wabe Hydropower Project, Central Ethiopia","authors":"Mesay Tefera Kassaw,&nbsp;Bayisa Regassa Feyisa,&nbsp;Tarun Kumar Raghuvanshi,&nbsp;Mamo Methe","doi":"10.1007/s12517-024-12166-1","DOIUrl":"10.1007/s12517-024-12166-1","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":476,"journal":{"name":"Arabian Journal of Geosciences","volume":"18 1","pages":""},"PeriodicalIF":1.827,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142940964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Site classification of locations of installed sensors in the Kumaon Region of the Himalayas using empirical approaches","authors":"Pankaj Kumar,&nbsp; Kamal,&nbsp;Ashok Kumar","doi":"10.1007/s12517-024-12154-5","DOIUrl":"10.1007/s12517-024-12154-5","url":null,"abstract":"<div><p>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 (<i>V</i>_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.</p></div>","PeriodicalId":476,"journal":{"name":"Arabian Journal of Geosciences","volume":"18 1","pages":""},"PeriodicalIF":1.827,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced bench design and technical challenges in open pit mining: a comprehensive review of stability and productivity","authors":"Geleta Warkisa Deressa,&nbsp;Bhanwar Singh Choudhary,&nbsp;Nagessa Zerihun Jilo","doi":"10.1007/s12517-024-12157-2","DOIUrl":"10.1007/s12517-024-12157-2","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":476,"journal":{"name":"Arabian Journal of Geosciences","volume":"18 1","pages":""},"PeriodicalIF":1.827,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An analysis of the causes of Kenya’s extreme drought event in May 2023","authors":"Ai Zeng,&nbsp;Lin Liu,&nbsp;Paul Oloo,&nbsp;Qiuchi Li,&nbsp;Yawen Wang","doi":"10.1007/s12517-024-12169-y","DOIUrl":"10.1007/s12517-024-12169-y","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":476,"journal":{"name":"Arabian Journal of Geosciences","volume":"18 1","pages":""},"PeriodicalIF":1.827,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiparameter study of shear strength improvement near-surface by vegetation roots and fibers","authors":"Charu Chauhan,&nbsp;Kala Venkata Uday","doi":"10.1007/s12517-024-12170-5","DOIUrl":"10.1007/s12517-024-12170-5","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":476,"journal":{"name":"Arabian Journal of Geosciences","volume":"18 1","pages":""},"PeriodicalIF":1.827,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting slope stability potential failure surface using machine learning algorithms","authors":"MyoungSoo Won,&nbsp;Shamsher Sadiq,&nbsp;JianBin Wang,&nbsp;YuCong Gao","doi":"10.1007/s12517-024-12146-5","DOIUrl":"10.1007/s12517-024-12146-5","url":null,"abstract":"<div><p>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 (<i>H</i>), slope ratio (<i>v</i>/<i>h</i>)), and soil shear strength parameters (soil unit weight (γ), cohesion (<i>c</i>), friction angle (ϕ)). According to the evaluation using Taylor’s chart metrics, including standard deviation, correlation determination (<i>R</i>²), 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 <i>v</i>/<i>h</i> &gt; <i>H</i> &gt; <i>c</i> &gt; ϕ &gt; γ for the factor of safety (FS) and <i>H</i> &gt; <i>v</i>/<i>h</i> &gt; <i>c</i> &gt; ϕ &gt; γ for the slip surface.</p></div>","PeriodicalId":476,"journal":{"name":"Arabian Journal of Geosciences","volume":"18 1","pages":""},"PeriodicalIF":1.827,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}