Upscaling dispersivity for conservative solute transport in naturally fractured media

Differences and implications of strontium distribution coefficient on various granite compositional materials

The distribution coefficient (Kd) of radionuclides is a crucial parameter in assessing the safety of high-level radioactive waste (HLW) geological repository. It is determined in the laboratory through batch and column experiments. However, differences in obtained Kd values from distinct experiments have not been thoroughly assessed and compared. This study evaluated strontium (Sr) sorption on different granite materials using static batch and dynamic experiments (column and core-flooding experiments). The results from batch sorption experiments showed higher Sr sorption on granite under acidic and strongly alkaline conditions, low solid-liquid ratios, and low ionic strength. In column experiments, a two-site sorption model was used to simulate Sr transport in crushed granite and mixed pure minerals. The sorption of Sr on crushed granite exhibited a higher affinity than that of mixed pure minerals. The dual-porosity transport model was employed to investigate Sr transport behavior in fractured granite in the core-flooding experiment. Kd obtained from batch sorption experiments are four to twenty times higher than those from column experiments, and two to three orders of magnitude higher than that from a core-flooding experiment. The results of this study provide valuable insights into safety assessment for the HLW geological repository.

Annual atrazine residue estimation in Chinese agricultural soils by integrated modeling of machine learning and mechanism-based models

This study presents a comprehensive approach to estimating annual atrazine residues in China's agricultural soils, integrating machine learning algorithms and mechanism-based models. First, machine learning was used to predict essential parameters influencing atrazine's adsorption, degradation, and dispersivity of solute transport. The results demonstrated that soil organic matter was the most important input variable for predicting adsorption and degradation; clay content was the primary variable for predicting dispersivity. The SHapley Additive exPlanations (SHAP) contribution of various soil properties on target variables were also analyzed to reveal whether each input variable has a positive, negative, or complex effect. Subsequently, these parameters inform the construction of a detailed model across 23,692 subregions of China, with a 20 km × 20 km resolution. The model considered regional variations and soil layer heterogeneity, including rainfall, soil depth-specific properties, and parameters for adsorption, degradation, and dispersivity. Utilizing the convection-dispersion equations and the Phydrus, the model simulated atrazine's transport and degradation patterns across diverse soil environments after applying 250 mL of atrazine (40%) per Chinese mu. The outcomes provided a spatially explicit distribution of atrazine residues, specifying that the arid areas have the highest residual risk, followed by the Northeast, Southwest, and Southeast. Atrazine levels may exceed national drinking water standards at 50 cm depth in Inner Mongolia, the Qinghai-Tibet Plateau, and the Jungar Basin. This study's integrative approach may also offer valuable insights and tools for evaluating residues of various pesticides and herbicides in agricultural soils.

Quantifying the impact of upscaled parameters on radionuclide transport in three-dimensional fracture-matrix systems

Assessing the long-term safety of geological repositories for high-level radioactive waste is critically dependent on understanding radionuclide transport in multi-scale fractured rocks. This study explores the influence of upscaled parameters on radionuclide movement within a three-dimensional fracture-matrix system using a discrete fracture-matrix (DFM) model. The developed numerical simulation workflow includes creating a random discrete fracture network, meshing of the fractures and matrix, assigning upscaled parameters, and conducting finite element simulations. We simulated the spatiotemporal evolution of radionuclide concentrations in the fractures and matrix over a century, revealing significant spatial heterogeneity driven by a heterogeneous seepage field. Employing geostatistics-based upscaling methods, we predicted the effective ranges of crucial solute transport parameters at the field scale. The matrix diffusion coefficient, matrix distribution coefficient, and longitudinal dispersivity were upscaled by factors of 2.0-3.0, 2.5-4.0, and 10-104, respectively, based on laboratory-scale measurements. Incorporating these upscaled parameters into the DFM model, we analyzed their impact on radionuclide transport. Our findings demonstrate that an upscaled matrix diffusion coefficient and matrix distribution coefficient result in a delayed transport of radionuclides in fractures by enhancing mass transfer between the fractures and rock matrix, while an upscaled longitudinal dispersivity accelerates transport by advancing the positions of concentration peaks. Sensitivity analysis revealed that the matrix distribution coefficient is the most impactful, followed by dispersivity and matrix diffusion coefficient. These insights are important for minimizing parameter uncertainties and enhancing the accuracy of predictions concerning radionuclide transport in multi-scale fractured rocks.

Appraisal of reservoir quality for hydrocarbon-bearing deep-water Miocene sandstones incised valley, south-east Asian offshore Indus: An application of seismic attributes and instantaneous spectral porosity quantitative reservoir simulations

Incised marine valleys (IVS) are hot topics in exploring the stratigraphic oil and gas-bearing plays. Multiple channelized sandstone lenses at varying depths [m], thicknesses [m], and porosities [%] constrain seismic impedance. The presence of hydrocarbon-bearing resources affects the seismic impedance (density (g/cc) and velocity (m/s)). Therefore, a quantitative prediction has been carried out for determining the thickness [m], porosity [%], and depths [m] of laterally distributed channelized sandstone lenses (SLS) for IVS, Indus offshore Basin (IOB), Pakistan, using 2-D instantaneous spectral porosity quantitative modelling (2DSSM), continuous wavelet transforms-based (CWT) 2-D instantaneous spectral density modelling (2DSSDM), and spectral decomposition tools. The 2DSSM remained limited in predicting the number of channelized sandstone lenses and their quantitative stratigraphic attributes. The 45-Hz-based processing of conventional 2DSSM has resolved the two channelized sandstone lenses of the stratigraphic trap. The deepest channelized sandstone lens has attained 1-6 m thickness with a lateral extent of 3 km, within the porosity range of 18-33 %. The highest confidence level for predicted petrophysical attributes such as 13 m-thick pay zones, -0.08, -0.067, and -0.07 acoustic impedances [g/c.c.*m/s], and 28 % porosities with R2 > 0.85 have validated interpretations. The response of 45-Hz CWT waveform-based inverted density and thickness simulations has predicted the highest thicknesses and lowest densities of reservoir sandstones within the meandering channel belt of the deepwater depositional system. The predicted densities and thicknesses for the coarse-grained sandstone lenses of point bars were 1.8-1.9 g/cc and 15 m, respectively. In the same way, the quantitative estimates of predicted density and simulated thickness have shown a strong coefficient correlation (R2 > 0.80), which confirms the presence of gas-bearing prospects within the IVS. The facies-controlled migration is thought to be the movement of the reservoir facies of the point bars and channelled sandstone-filled lenses to the side.

Integrated machine learning for modeling bearing capacity of shallow foundations

Analyzing the stability of footings is a significant step in civil/geotechnical engineering projects. In this work, two novel predictive tools are suggested based on an artificial neural network (ANN) to analyze the bearing capacity of a footing installed on a two-layered soil mass. To this end, backtracking search algorithm (BSA) and equilibrium optimizer (EO) are employed to train the ANN for approximating the stability value (SV) of the system. After executing a set of finite element analyses, the settlement values lower/higher than 5 cm are considered to indicate the stability/failure of the system. The results demonstrated the efficiency of these algorithms in fulfilling the assigned task. In detail, the training error of the ANN (in terms of root mean square error-RMSE)) dropped from 0.3585 to 0.3165 (11.72%) and 0.2959 (17.46%) by applying the BSA and EO, respectively. Moreover, the prediction accuracy of the ANN climbed from 93.7 to 94.3% and 94.1% (in terms of area under the receiving operating characteristics curve-AUROC). A comparison between the elite complexities of these algorithms showed that the EO enjoys a larger accuracy, while BSA is a more time-effective optimizer. Lastly, an explicit mathematical formula is derived from the EO-ANN model to be conveniently used in predicting the SV.

Landsat-8 OLI/SRTM and gravity characteristics of the Pan-African fracture aquifers of the north central Cameroon region (central Africa)

Finding drinking water in a Precambrian (Pan-African) basement context is a major concern for many Central African countries. For example, most groundwater is in fissured or fractured basement aquifers. To enhance the success rate of boreholes, this study aims to identify the features and the geographic reach of the lineament directions likely to drain groundwater. The research area has a size of 23 045 km2, is 120 km from Yaoundé, Cameroon, and is located there. The methodological approach integrates current borehole and outcrop data with spatial data (Landsat-8 OLI/SRTM and gravity). The main NE-SW (N20-70°E), E-W (N80-100°E), and secondary N-S (N0-20°E) and NW-SE (N120-140°E) fracture directions display on the generated structural lineament map. The Pan-African orogeny is linked to the E-W direction, and the Sanaga Fault is related to the NE-SW direction. The other directions (N-S and NW-SE) correspond to the post-collisional decompression phase. This study locates medium-flow boreholes on or near the NE-SW, E-W, and NW-SE trending fractures, hence enhancing the knowledge of fissured and fractured basement aquifers in the Central region of Cameroon. The corresponding water flow rates range from 1 to 3 m3/h as well. The extensive exploration of fractured bedrock aquifers can benefit from these findings.

A novel evolutionary combination of artificial intelligence algorithm and machine learning for landslide susceptibility mapping in the west of Iran

Detecting and mapping landslides are crucial for effective risk management and planning. With the great progress achieved in applying optimized and hybrid methods, it is necessary to use them to increase the accuracy of landslide susceptibility maps. Therefore, this research aims to compare the accuracy of the novel evolutionary methods of landslide susceptibility mapping. To achieve this, a unique method that integrates two techniques from Machine Learning and Neural Networks with novel geomorphological indices is used to calculate the landslide susceptibility index (LSI). The study was conducted in western Azerbaijan, Iran, where landslides are frequent. Sixteen geology, environment, and geomorphology factors were evaluated, and 160 landslide events were analyzed, with a 30:70 ratio of testing to training data. Four Support Vector Machine (SVM) algorithms and Artificial Neural Network (ANN)-MLP were tested. The study outcomes reveal that utilizing the algorithms mentioned above results in over 80% of the study area being highly sensitive to large-scale movement events. Our analysis shows that the geological parameters, slope, elevation, and rainfall all play a significant role in the occurrence of landslides in this study area. These factors obtained 100%, 75.7%, 68%, and 66.3%, respectively. The predictive performance accuracy of the models, including SVM, ANN, and ROC algorithms, was evaluated using the test and train data. The AUC for ANN and each machine learning algorithm (Simple, Kernel, Kernel Gaussian, and Kernel Sigmoid) was 0.87% and 1, respectively. The Classification Matrix algorithm and Sensitivity, Accuracy, and Specificity variables were used to assess the models' efficacy for prediction purposes. Results indicate that machine learning algorithms are more effective than other methods for evaluating areas' sensitivity to landslide hazards. The Simple SVM and Kernel Sigmoid algorithms performed well, with a performance score of one, indicating high accuracy in predicting landslide-prone areas.

Investigating the influence of bentonite colloids on strontium sorption in granite under various hydrogeochemical conditions

The disposal of high-level radioactive waste in deep geological repositories is a critical environmental issue. The presence of bentonite colloids generated in the engineering barrier can significantly impact the transport of radionuclides, but their effect on radionuclide sorption in granite remains poorly understood. This study aimed to investigate the sorption characteristics of strontium (Sr) on granite as well as on the coexistence system of granite and colloids under various hydrogeochemical conditions, through batch experiments. Fourier transform infrared spectroscopy was employed to analyze the sorption forms of Sr on granite before and after sorption. Several hydrogeochemical factors were examined, including contact time, pH, ionic strength, coexisting ions, and bentonite and humic acid colloid concentration. Among these factors, the concentration of bentonite colloids exhibited a significant effect on Sr sorption. Within a specific range of colloid concentration, the sorption of Sr on the solid system increased linearly with the bentonite colloid concentration. pH and ionic strength were also found to play crucial roles in the sorption process. At low pH, Sr sorption primarily occurred through the outer sphere's surface complexation and Na+/H+ ion exchange. However, at high pH, inner sphere surface complexation dominated the process. As the ionic strength increased, electrostatic repulsion gradually increased, resulting in fewer binding sites for particle aggregation and Sr sorption on bentonite colloids. The results also indicate that with increasing pH, the predominant forms of Sr in the solution transitioned from SrHCO3+ and SrCl+ to SrCO3 and SrCl+. This was mainly due to the ion exchange of Ca2+/Mg2+ in plagioclase and biotite, forming SrCO3 precipitation. These findings provide valuable insights into the transport behavior of radionuclides in the subsurface environment of the repository and highlight the importance of considering bentonite colloids and other hydrogeochemical factors when assessing the environmental impact of high-level radioactive waste disposal.

Experimental and modeling investigation of dual-source iron release in water-solid-gas interaction of abandoned coal mine drainage

After mine closure and flooding, abandoned iron-prone devices and equipment (e.g., steel bolts and ground support meshes) and iron-bearing minerals (e.g., pyrite) form a dual-source iron pollution system in mine groundwater. Dual-source iron contributes to the water-solid-gas interaction in abandoned coal mines and the release of iron at different periods after mine closure, posing environmental risks in groundwater and discharging acid mine drainage, which contains large amounts of iron. In this study, a series of hydrochemical experiments were conducted to simulate the iron release process of the dual-source system, and electrochemical experiments were carried out to reveal the reaction mechanism, characterize the dual-source iron pollution release mode and quantify the release rate ratio. PHREEQC package was used to simulate the long-term hydrogeochemistry reactions of the water-solid-gas interaction to determine the key factors and suitable conditions that inhibit dual-source iron release. The results show that the dual-source system of iron-bearing minerals (pyrite) and steel bolts promote iron release from each other. The resulting calculated annual iron release indicated that the overall iron release rate ratio is: dual-source > bolt > pyrite, indicating that mine water would remain acidic for a long time due to the continuous release of iron from the system. Numerical modeling results show that maintaining the environment temperature below 25 °C and the pH above 3.5 is an effective way to reduce the iron release rate.

Land subsidence susceptibility mapping: comparative assessment of the efficacy of the five models

Land subsidence (LS) as a major geological and hydrological hazard poses a major threat to safety and security. The various triggers of LS include intense extraction of aquifer bodies. In this study, we present an LS inventory map of the Daumeghan plain of Iran using 123 LS and 123 non-LS locations which were identified through field survey. Fourteen LS causative factors related to topography, geology, hydrology, and anthropogenic characteristics were selected based on multi-collinearity test. Based on the results, five susceptibility maps were generated employing models and input data. The LS susceptibility models were evaluated and validated using the receiver operating characteristic (ROC) curve and statistical indices. The results indicate that the LS susceptibility maps produced have good accuracy in predicting the spatial distribution of LS in the study area. The result showed that the optimization models BA and GWO were better than the other machine learning algorithm (MLA). In addition, The BA model has 96.6% area under of ROC (AUROC) followed by GWO (95.8%), BART (94.5%), BRT (93.1%), and SVR (92.7%). The LS susceptibility maps formulated in our study can serve as a useful tool for formulating mitigation strategies and for better land-use planning.