Arabian Journal of Geosciences最新文献

Seismic deconvolution is a critical process in geophysical signal processing, aimed at enhancing the resolution of subsurface imaging by recovering sparse reflectivity sequences from convolved seismic data. This study introduces an advanced deconvolution approach utilizing the Proximal Dykstra (PD) algorithm, a convex optimization technique that leverages proximal operators to address the ill-posed nature of seismic inverse problems. The proposed method integrates a quadratic data fidelity term with a sparsity-promoting ℓ₁-norm regularization, enabling robust reconstruction of reflectivity series in the presence of additive noise. The PD algorithm iteratively alternates between projections that enforce data consistency and sparsity, achieving superior resolution and noise suppression compared to the benchmark Iterative Shrinkage-Thresholding Algorithm (ISTA). Performance evaluations on synthetic seismic datasets, with signal-to-noise ratios ranging from 1.92 dB to noise-free conditions, demonstrate the PD algorithm’s ability to sharpen reflectors, enhance lateral continuity, and broaden spectral bandwidth. Application to real seismic data from southern Iran further validates its effectiveness, revealing improved delineation of geological features and fault structures. Despite its computational complexity and sensitivity to parameter tuning, the PD algorithm offers a flexible and scalable framework for seismic deconvolution, making it a valuable tool for high-resolution subsurface characterization in challenging geophysical environments.

Groundwater Quality assessment is a cornerstone of sustainable agriculture, especially in semi-arid regions where irrigation demands are high and soil health depends on water chemistry. This study presents an integrated hydrochemical, geospatial, and statistical analysis to evaluate the irrigation suitability of groundwater in the semi-arid alluvial tract of Uttar Pradesh, India. A total of 39 groundwater samples were collected and analysed for major cations (Ca²⁺, Mg²⁺, Na⁺, K⁺) and major anions (Cl⁻, SO₄²⁻, HCO₃⁻, CO₃²⁻), and physicochemical parameters. Irrigation water quality indices EC, SAR, Na%, MAR, SSP, KR, RSC, TH, and PI were computed. Spatial variability was mapped using GIS-based Inverse distance weighting and indicator kriging, while principal component analysis (PCA) was applied to identify controlling hydrochemical factors. Results showed EC ranged from 53 to 5913 µS/cm, with 39.5% of samples permissible, 15.8% doubtful, and 2.6% unsuitable for irrigation. SAR placed all samples in the S1(low sodium hazard) class, but Na% classified 46% as doubtful to unsuitable, and MAR exceeded safe limits in 41% of samples. SSP was excellent to good in 97.4% of cases, while negative RSC values confirmed irrigation safety. Hydrochemical facies were dominated by the Ca–Mg–HCO₃ type. Wilcox and USSL plots confirmed that most samples were good-permissible, though localized salinity hazards were identified in the northeast and southeast. PCA highlighted SSP, PI, and KR as the most influential parameters. Overall, the study concludes that the groundwater is largely suitable for irrigation, though localized salinity and sodium risks require targeted management to protect soil permeability and crop productivity.

Accurate and geospatial estimation of fruit yield in densely structured orchards remains a major challenge for precision horticulture, primarily due to canopy heterogeneity, spectral saturation, and the limited physiological interpretability of conventional vegetation indices. This study proposes a novel, biophysically grounded framework for pixel-level yield estimation in kinnow mandarin (Citrus reticulata) orchards by integrating Sentinel-2–derived Red-Edge Position (S2REP) with key canopy traits, namely Leaf Area Index (LAI) and chlorophyll content. Field measurements were conducted across 550 individual tree canopies aggregated into 55 independent orchard pixels (20 × 20 m), enabling direct linkage between satellite observations and in situ biophysical and yield data. Strong and statistically significant relationships were established between S2REP and both LAI (adjusted R² = 0.86, p < 0.001) and chlorophyll content (adjusted R² = 0.80, p < 0.001), confirming the sensitivity of red-edge signals to canopy structural and biochemical variations. These S2REP-derived traits were subsequently integrated into a multiple linear regression model for yield prediction, achieving high explanatory power (adjusted R² = 0.85) and robust generalization performance on independent validation data (validation R² = 0.82; RMSE = 46 kg pixel⁻¹; MAE = 42 kg pixel⁻¹). Predicted yields ranged from approximately 120 to 660 kg per 20 × 20 m pixel, with the majority of values predominantly concentrated between 300 and 500 kg. Geospatial maps of LAI, chlorophyll content, and yield revealed pronounced intra-orchard variability, reflecting differences in canopy vigor, physiological status, and management conditions. By explicitly linking satellite-derived spectral features to physiologically meaningful canopy traits, the proposed framework enhances transparency, robustness, and scalability compared to purely empirical approaches. This methodology offers a cost-effective and operational solution for orchard-scale monitoring, early yield forecasting, and site-specific management, with direct implications for growers, policymakers, insurers, and agri-business stakeholders.

Sedimentation-driven reservoir degradation and volumetric decline represent critical constraints for water supply systems in rapidly urbanising environments. This study applied GNSS-referenced Single Beam EchoSounder (SBES) bathymetry to quantify depth change, storage capacity, and sedimentation patterns in the Erelu Reservoir, southwestern Nigeria. High-resolution surveys conducted in 2022, 2023, and 2025 indicate a progressive reduction in reservoir capacity, with average depth decreasing from 2.52 m to 2.03 m and total volume declining from 2.30 million m³ to 2.17 million m³. Population projections and water-demand modelling for 2024–2030 demonstrate that the current storage can support municipal needs for only 50–56 days under moderate consumption assumptions, underscoring increasing supply vulnerability. Recommended actions include targeted dredging, removal of aquatic weeds, expansion of rainfall-harvesting systems, and increasing treated-water storage from 4,800 m³ to approximately 600,000 m³. By integrating geodetically controlled bathymetry with volumetric modelling, this study addresses a methodological gap and provides quantitative evidence required for long-term reservoir restoration and operational planning.

Oceanic Anoxic Events (OAEs) are crucial events in Earth’s geological history. Due to the tough terrain, less information is available for the Himalayan foreland basin (HFB) sequence in India. This study focuses on the Kalakot shale of the Jammu region and the Bhuj shale of the Kutch region. We compare the HFB shale (Jammu region) with the Bhuj shale (Kutch region) using sediment geochemistry to glean the prevalence of anoxic conditions. Various shale litho-facies in the Kutch region indicate a depositional environment, whereas the Jammu region shows a low-energy lagoonal to sub-tidal environment. Among the major elements, Al₂O₃ and Fe₂O₃ in the Kalakot shale of Jammu and the Bhuj shale of Kutch were analyzed. The Bhuj shale shows Ni/Co ratios varying between 24.25 and 1.65 (average 8.5), indicating the prevalence of anoxic conditions. Further, the values of V/ (V + Ni) lie between 0.7 and 0.8, corroborating with PAAS values and exposure to anoxic to dysoxic conditions. This aligns well with V/Cr values in the range of 1 to 3, indicating dysoxic to sub-oxic conditions. In the Kalakot samples, the Ni/Co ratio (average = 2.5) and V/ (V + Ni) ratio (average ~ 0.81) corroborate the prevalence of sub-oxic to anoxic depositional settings. In Kalakot, the Ni/Co ratio varies between 0.2 and 24 (average 2.5), and the V/ (V + Ni) ratio lies between 1.0 and 0.8, indicating sub-oxic to anoxic conditions. The intercomparison of geochemical data between Kutch and HFB revealed that the understanding of regional and global OAEs are crucial for deciphering the paleo depositional conditions.

The analysis of mine pressure law is very important for the safe mining of deep super-long gangue backfilling face. In this study, combined with on-site monitoring, numerical simulation and other means, the law of mining pressure appearance in deep super-long gangue backfilling face was analyzed, and a multivariate nonlinear regression model between the main control factors and the characterization index of mine pressure appearance was established. The sensitivity ranking of the main control factors was determined. The results show that the advanced influence range of super long face gangue backfilling mining is much larger than that of shallow short face backfilling mining. The difference of support resistance in the upper, middle and lower areas of backfilling face is not obvious, which is significantly different from that of caving mining. The correlation coefficient square ( R² ) of multivariate nonlinear regression equation was greater than 0.99, and the fitting effect was good. The sensitivity of the maximum subsidence of the immediate roof is most influenced by backfilling rate, followed by advancing distance, face length, and overburden depth. The research results provide theoretical support for breaking through the 300 m barrier in subsequent deep in-situ gangue backfilling mining face lengths.

This study introduces a modified vulnerability assessment approach for inland aquifers susceptible to saline water intrusion, utilizing a revised GALDIT model named GALDITY. The case study focused on the Shahrood aquifer, selected due to its unique hydrogeological and hydrochemical properties. To assess saline water intrusion potential in the Shahrood aquifer, water levels and chemistry parameters were studied through sampling from 115 wells. The modified GALDITY model enhances the GALDIT framework by focusing on the saline-fresh water level difference and including well yield as a parameter, reflecting overexploitation impacts, assessing seven hydrogeological parameters. Each parameter is rated and weighted, producing vulnerability maps that help identify risk zones for effective management. The validation of GALDITY vulnerability map of Shahrood aquifer was performed using TDS concentration as a salinity indicator. The excellent adaptation of points with high TDS and areas with a high vulnerability index, suggests that this model is suitable for investigating the vulnerability of the aquifer to the intrusion of saline groundwater. Comparing the results of the GALDITY model with the original GALDIT model revealed that the improved model exhibited higher accuracy for inland aquifers. According to the vulnerability assessment, approximately 2% of the aquifer is classified as very low vulnerability, 14% as low vulnerability, while 47%, 26%, and 9% of the area fall under moderate, high, and very high vulnerability classes, respectively. The results show the GALDITY model provides valuable insights into the spatial distribution of vulnerability, aiding in the management and protection of internal aquifers against salinization.

A quantitative assessment of river streamflow variations due to natural and anthropogenic factors is important for developing effective climate change adaptation strategies and ensuring sustainable management of water resources. In the context of the Chenab basin in the western Himalayas, understanding these variations is particularly critical due to the region’s diverse topography and sensitivity to climate change. In this study, we project future streamflow in the Chenab basin, using the Variable Infiltration Capacity (VIC) model, and the Soil and Water Assessment Tool (SWAT) under RCPs 4.5 and 8.5. Climate projections indicate rising temperatures, with significant increases in Akhnoor, Batote, Reasi, and Tandi, particularly under RCP 8.5. Precipitation trends vary, with Akhnoor and Reasi showing annual declines and Tandi exhibiting consistent reductions. Snow cover analysis reveals a negative correlation with streamflow, delaying runoff. Land use projections show agricultural and urban expansion at the expense of water bodies and forests. Both models perform well, projecting streamflow declines, with VIC estimating reductions of 19,186.31 cusecs (2020–2040) and 32,908.92 cusecs (2040–2060) under RCP 4.5. The sharper decline under RCP 4.5 is due to slower warming delaying snowmelt, while RCP 8.5 accelerates snowmelt, partially offsetting reductions. Findings highlight climate change impacts on water resources and the need for adaptive management strategies.

This paper aims to develop a robust mathematical model for real-time skin factor evaluation during acid stimulation treatments in naturally fractured reservoirs (NFRs), addressing the limitations of existing methods that are designed for conventional reservoirs. A novel model is formulated by integrating the pressure transient response of double-porosity systems (based on Kazemi’s approach) into the real-time skin factor evaluation framework. The model modifies Prouvost and Economides’ method to account for NFR characteristics, including storativity ratio and interporosity flow coefficient. Its effectiveness is validated through a case study of a matrix acidizing treatment in an Iranian oil well from an NFR, with comparisons to established methods by Paccaloni, Prouvost and Economides, and Zhu and Hill. The proposed model accurately predicts the ultimate skin factor with only a 1.2% error relative to well test data, significantly outperforming other methods (errors up to 71.1%). This study introduces the first model to incorporate double-porosity pressure transient behavior into real-time skin factor evaluation for NFRs, offering a tailored approach that enhances the accuracy of stimulation treatment assessment and decision-making in complex reservoirs.

This study aims to quantify soil erosion and identify vulnerable areas in the Ifni Massif and Lakhsas limestone plateau, located in Morocco, by integrating the five factors of the Revised Universal Soil Loss Equation (RUSLE): Rainfall-Runoff Erosivity (R), Soil Erodibility (K), slope Length & Steepness (LS), vegetation cover (C), and supporting practices (P) performed using the Google Earth Engine (GEE) platform. Results show significant spatial variability in soil loss: with the highest values occurring in hydrographic networks, and the lowest in areas with flat topography. The model estimates an average annual soil loss of 18.3 t/ha/yr, with 59% of the study area experiencing rates exceeding 10 t/ha/yr. These GEE spatial modeling results have demonstrated the high fragility of soils and the importance of targeted conservation measures to mitigate the impacts of erosion in these vulnerable environments, and provide valuable information to decision-makers for land-use planning and the implementation of sustainable soil conservation strategies, particularly in the context of quarry siting. This could also have a positive impact on future projects in areas exposed to soil degradation risk.