Grain breakage under high stress conditions can significantly affect the hydro-mechanical behavior of granular materials by altering grain size distribution, pore structure, porosity, and permeability. This study investigates these coupled effects in two sands with contrasting mineralogical characteristics, namely Hostun silica sand and carbonate sand, subjected to high oedometric stresses up to 105 MPa. High-pressure oedometer tests were performed on dense specimens prepared at a relative density of approximately 0.9. Grain size distribution was determined by post-loading sieve analysis, while permeability was measured at different stress levels using a constant-head system connected to the oedometer cell. Pore-access size distribution was characterized using two complementary techniques depending on material response and stress level: the tensiometric method, based on drying-path water retention measurements and the Young–Laplace law, and mercury intrusion porosimetry for carbonate sand specimens that developed sufficient cohesion after high-stress loading. The results show that increasing stress induces progressive grain fragmentation, leading to an evolution of the grain size distribution toward an ultimate grading state, together with reductions in pore size and porosity. The crushing threshold was identified at approximately 12.5 MPa for Hostun sand and 3 MPa for carbonate sand, indicating the strong influence of mineralogical composition on breakage resistance. Permeability decreased markedly with stress, with a reduction of about 50 % for Hostun sand and more than 70 % for carbonate sand. This stronger reduction in carbonate sand is associated with more pronounced particle breakage, pore structure modification, fines production, and reduced pore connectivity. The experimental results were interpreted using the Continuum Breakage Mechanics framework, and a power-law relationship between permeability and porosity was calibrated for both sands. The analysis indicates that grain rearrangement and grain breakage jointly control porosity and permeability reduction, with the influence of grain breakage becoming increasingly significant at higher stress levels, particularly for carbonate sand.These findings provide an experimental and modeling basis for understanding the coupled effects of high stress, grain breakage, pore structure evolution, and permeability reduction in sands, with implications for geotechnical, geomechanical, and petroleum reservoir engineering applications..
GNSS vertical time series exhibit nonlinear and non-stationary characteristics, making it difficult for traditional time series forecasting methods to achieve high-precision predictions. To address this challenge, this study proposes ahybrid prediction model-CEEMDAN-VMD-LSTM-which integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), and Long Short-Term Memory (LSTM) networks. The model first applies the CEEMDAN algorithm to decompose the original GNSS time series into a set of Intrinsic Mode Functions (IMFs), and computes the Permutation Entropy (PE) and Sample Entropy (SE) of each IMF. A Composite Entropy (CE) is constructed by taking the equal-weighted average of PE and SE. Based on the composite entropy values, the K-Nearest Neighbors (KNN) algorithm is used to classify the IMFs into high-frequency and low-frequency components. These components are then linearly aggregated into high-frequency and low-frequency sequences, respectively. The high-frequency sequence undergoes a second decomposition using the VMD algorithm, resulting in k IMFs and a residual sequence. The low-frequency sequence (from the first decomposition), the IMFs, and the residual sequence (from the second decomposition) are separately modeled and predicted using the LSTM network. The final forecast is reconstructed by aggregating the predictions of the subcomponents. Experimental results on GNSS vertical time series from ten stations show that the proposed CEEMDAN-VMD-LSTM model achieves Root Mean Square Error (RMSE) values ranging from 2.20 mm to 3.69 mm, Mean Absolute Error (MAE) values between 1.75 mm and 2.95 mm, and coefficients of determination (R2) ranging from 0.80 to 0.89. Compared with baseline models including LSTM, VMD-LSTM, CEEMDAN-VMD-RNN, and CEEMDAN-VMD-GRU, the proposed model demonstrates varying degrees of improvement, validating its effectiveness for GNSS vertical time series prediction.
In view of the severe thermal hazards induced by high temperatures in deep underground engineering, a thermo-mechanical coupling model based on PFC3D is adopted to investigate the microstructural evolution and mechanical characteristics of sandstone subjected to high-temperature thermal damage. The results show that: (1) The expansion of thermally induced cracks has a threshold effect and heterogeneous characteristic. The cracking degree increased sharply in the temperature range of 800 degrees C-1000 degrees C. Thermally induced cracks presented a central concentration phenomenon and gradually weakened from the core region to both ends of the rock specimen; (2) The incubation stage of thermal damage was mainly dominated by local shear slip. As the temperature increased, thermal deformation triggered the widespread initiation of tensile fractures, leading to a transition of the failure mode to tensile-shear joint yield; (3) The crack network has undergone an evolutionary process from locally preferred orientation to overall randomness distribution. The comprehensive uniformity index indicates that thermal stress will drive the failure behavior of rock, prompting a transition from anisotropy to uniform disintegration; (4) Based on the three-dimensional contact microstructure diagram, it was found that heating below 400 degrees C would promote the polarization of the force chain. Local strengthening would lead to a slight increase in uniaxial compressive strength, and the thermal damage variable value was relatively low. However, after exceeding 600 degrees C, the main skeleton collapsed, causing the thermal damage variable approaching 0.8.
The selection and design of foundation and superstructure systems depend heavily on determining the soil parameters and bearing capacity. Currently, bearing capacity is commonly estimated using analytical equations that involve numerous variable parameters. However, employing multiple formulations and variables in calculating bearing capacity can introduce inaccuracies and computational difficulties. This study aims to reduce potential inaccuracies and computational complexities in calculating bearing capacity and to accelerate the process using a Visual Basic application. This application simultaneously calculates and compares bearing capacities by Terzaghi, Meyerhof, Hansen, and Vesic methods. For this purpose, an example problem involving the calculation of bearing capacity was solved using both the conventional methods and a Visual Basic application, and the results were compared. This study demonstrated that bearing capacity results can be obtained more rapidly and consistently using the Visual Basic application than with conventional methods.
Rocks may have different external appearances due to internal factors such as minerals and external factors such as pressure and temperature that are effective in the formation process. Rocks of the same type may be visually different from each other due to reasons in the formation process. It may be difficult to distinguish rocks with the human eye due to differences such as color, gap sizes, and stains. In this study, 3 different rock types (tuff, granite, basalt) that are visually dissimilar were classified with deep learning. As the first step for classification, a data set was prepared for training and test images. Training and test images were created by imaging rock samples with different sizes, colors, and shapes twice with different poses. In the study, images with different features were obtained by applying two different pre-processing methods. These are, instead of reducing the size of the full rock image, dividing the image into square sections of 224x224 pixels and cropping the full image from the edges so that only rocky regions are in the image. After the pre-processing, the rocks were detected by applying GoogleNet and ResNet deep network models. As a result of experimental studies conducted with a total of 504 rock images, approximately 94 % (GoogleNet) and 96 % (ResNet) correct classification rates were obtained. This study has shown that image processing-based automatic rock classification applications will make significant contributions to future studies in the field of geology and mining.
Accurate evaluation of ICESat-2 ATL08 terrain elevations in rugged mountainous areas is important for reliable geodetic applications. Inthis study, ATL08 Version 006 100 m terrain segments in the upper Yellow River Basin were evaluated against a 2 m GF-7 stereo DEM after converting ATL08 ellipsoidal heights to orthometric heights to ensure vertical-datum consistency. After quality screening, 15,904 segments were retained, and the mean absolute elevation difference was 14.08 m. Geodetector analysis identified terrain_slope and h_te_std as the dominant explanatory factors, whereas snr, n_ca_photons, h_te_uncertainty, and h_te_interp_minus_median played secondary but still significant roles. To reduce spatial leakage, the retained segments were partitioned into spatially disjoint blocks of approximately 10 km and systematically assigned to five folds. Six machine-learning models were then compared for predicting absolute elevation error under spatially blocked cross-validation. HistGradientBoosting achieved the best overall performance, with a mean test R2 of 0.452 +/- 0.016, RMSE of 10.65 +/- 0.64 m, and MAE of 7.24 +/- 0.40 m across the five spatial folds. The results indicate moderate but stable predictive skill in geographically unseen areas and support terrain-aware screening of ATL08 segments for DEM validation and related geodetic applications.
Beneficiation investigations targeting the low-grade manganese ores situated within the boundaries of the Mohmand district were executed with the aim of connecting their potential for integration into manganese-centric industrial applications. A meticulous delineation of these ores was undertaken through a comprehensive amalgamation of petrographic, geochemical and mineralogical analyses, followed by experimental assessments to determine paths for their value upgrading. Employing physical beneficiation methodologies, notably gravity and magnetic separation, led to discernible enhancements in the manganese-to-iron (Mn:Fe) ratio, thus substantiating the feasibility of upgrading these ores. Geochemical characterization classified the ores as siliceous manganese ores, while mineralogical scrutiny delineated braunite and pyrolusite as the principal manganese-bearing minerals. Quartz (SiO2) emerged as the predominant gangue mineral, accompanied by minor quantities of calcite (CaCO3) and hematite (Fe2O3). Noteworthy is the fractured nature of the manganese minerals, compounded by secondary quartz infillings. Beneficiation endeavors underscored the efficiency of coarser grain sizes in achieving optimal liberation degrees for these low-grade ores. The findings underscore the potential for substantial augmentation in manganese content, evidenced by an elevation from 24 wt. % to 43.6 wt. % through gravity separation, coupled with a recovery rate of 66.49%. Magnetic separation interventions yielded further improvements, elevating the concentration to 45.96 wt. %, while concurrently enhancing recovery rates to 89.17 %. The cumulative outcomes underscore the viability of integrating these beneficiation strategies into industrial processes, thereby fostering enhanced utilization of low-grade manganese ores within the Mohmand district.
This study develops an integrated machine learning (ML) framework for debris flow susceptibility and risk index mapping across Quang Nam Province, a region prone to high-intensity monsoonal rainfall and strongly weathered metamorphic bedrock. Four ML algorithms including Gradient Boosting (GB), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were trained on 228 stream-channel samples described by eight causative factors, under two class-imbalance correction strategies. Model performance was assessed through stratified 10-fold cross-validation, and feature importance was quantified using Shapley Additive Explanations (SHAP). Gradient Boosting with cost-sensitive learning (GB-CS) achieved the best overall performance (AUC = 0.9902, F1 = 0.9286, only 4 misclassifications in 228 instances), outperforming the Modified Analytic Hierarchy Process (M-AHP) expert baseline by 0.249 AUC units (33.5 % relative improvement). SHAP analysis identified channel gradient as the dominant predictor (mean normalized importance = 0.442) across GB, RF, and SVM, followed by elevation (0.142) and geology (0.108). The GB-CS susceptibility map correctly classified 92.9 % of inventory sites within the High or Very High susceptibility classes, covering 20 % of the stream network. Integration of susceptibility with a land-use and elevation-weighted vulnerability index revealed that the highest-risk reaches occur not in the most hazardous western highlands but in transitional mid-elevation agricultural corridors - a finding with direct consequences for early-warning system prioritization.
The areas along transportation routes constructed in mountainous terrain often harbor significant landslide hazards. Ensemble learning techniques have proven their effectiveness in improving landslide susceptibility prediction performance. In this study, novel ensemble models (Bagging (B), Cascade Generalization (CG), and Dagging (D)) based on the Dual Perturb and Combine for Tree-based (DPCT) approach were employed to predict landslide susceptibility along the Ha Long-Van Don highway. The dataset comprised 77 landslide locations (3263 points), non-landslide locations (1:1 ratio with landslide points), and 14 conditional factors, including topography characteristics, geology, rainfall, and land use/land cover (LULC), which were input parameters for the models (B-DPCT, CG-DPCT, D-DPCT, and DPCT). Evaluation criteria for model prediction outcomes included the area under the receiver operating characteristic curve (AUC), parameters derived from the confusion matrix, the Kappa statistic, and the root mean square error (RMSE). The results demonstrate that the integration of higher-resolution datasets with hybrid machine-learning models leads to a significant improvement in predictive performance and accuracy for landslide susceptibility mapping compared to previous studies. Accordingly, landslide susceptibility maps predicted based on the B-DPCT model exhibited optimal evaluation results on the validation dataset (AUC = 0.948, accuracy ACC = 83.6, Kappa statistic = 0.67, and RMSE = 0.37), suggesting their recommended use for construction planning and mitigation efforts along the Ha Long-Van Don highway to minimize landslide-induced damages.
The presence of colored noise in GNSS coordinate time series can affect the accurate estimation of station velocity and velocity uncertainty. Accurate noise model identification is crucial for obtaining reliable and high-precision velocity estimates and their true confidence intervals. To address this, accurate noise model identification is critical for high-precision velocity estimates. The study selected 37 GNSS coordinate time series on the West Coast of the United States from 1999 to 2023, and we adopt a novel Bayesian Information Criterion with temporal prioritization (BIC_tp), extending the standard BIC by incorporating temporal factors to detect colored noise in GNSS coordinate time series, and compares it with BIC and the Akaike Information Criterion (AIC). The results show that BIC_tp effectively identifies distinct noise profiles, with the north (N) and east (E) components dominated by flicker noise plus random walk plus white noise (FNRWWN) and the up (U) component by power-law plus white noise (PLWN). Subsequently, the Mann-Whitney U-test and error propagation analysis assess the impact of these noise models on station velocity and annual amplitudes. Mean station velocity is significantly positive (0.07-0.20 mm/yr, 95 % confidence interval) with no outliers in velocity uncertainty. The U-test indicates that 70.3 % and 86.5 % of annual displacement a mplitudes in the N and E components, respectively, are below 1.0 mm, suggesting stable horizontal motion, while vertical (U) displacements show greater annual variations. Non-parametric testing enhances the detection of anomalous velocity and periodic terms, improving velocity and amplitude estimate accuracy by identifying data processing errors.
The & Zcaron;elezn & eacute; hory Mountains, a highland region within the central Bohemian Massif in Czechia, are bordered to the southwest by a prominent 70 km-long fault formed through Cenozoic thrust tectonics. Along this fault-known as the & Zcaron;elezn & eacute; hory Fault-the crystalline bedrock has been thrust over Cretaceous sediments, forming a sharp and distinct geological boundary. Despite its pronounced geomorphological expression, the fault has traditionally been considered inactive, largely due to a lack of detailed observations and focused research. To evaluate its potential neotectonic activity during the Late Cenozoic, we applied a range of geomorphometric indices, including the Strahler hypsometric integral, asymmetry factor (AI), mountain-front sinuosity (Smf), and stream-gradient index (SL). High-resolution digital elevation models and topographic maps were used to analyze fault scarp morphology and to assess the drainage network, a commonly sensitive indicator of tectonic deformation. The results suggest spatial variability in tectonic influence, with different indices highlighting distinct areas of potential activity. Through cross-comparison, we identified several locations as promising candidates for further field investigation. Notably, the Po & ccaron;& aacute;teck & yacute; Valley exhibits consistently high values across multiple indices, while the Bumb & aacute;lka Valley features a pronounced knickpoint along its longitudinal profile within the fault zone.
This study investigates the potential of multispectral remote sensing data, specifically Landsat 9 imagery, to map hydrothermal alteration zones in the Tata region, located in the southeastern AntiAtlas of Morocco. The research focuses on identifying iron oxide and hydrothermal alteration zones using advanced image processing techniques, including Principal Component Analysis (PCA), band ratios, and spectral indices. The methodology also incorporates directional filters for lineament extraction and field validation to ensure accuracy. The PCA approach effectively reduced data dimensionality while enhancing relevant spectral features, particularly those indicative of iron oxide and hydrothermal mineralization. Band ratios and spectral indices further delineated iron oxide and hydrothermal alteration areas, which were corroborated by field investigations revealing polymetallic mineralization, including copper, lead, and iron oxides. The results highlight the strong structural control on mineralization and demonstrate the usefulness of multispectral remote sensing for regional mineral exploration. Field validation based on approximately 38 mineralized observation points shows a strong spatial coincidence between mapped alteration zones and known surface mineralization.
Prediction of the Bond Work Index (BWI) and Hardgrove Grindability Index (HGI) from routinely measured rock mechanical and index parameters is critical for energy-efficient comminution design, yet remains challenging due to complex, nonlinear relationships among strength, abrasivity and energy consumption. This study proposes a deep neural network (DNN)-based machine-learning framework to estimate BWI and HGI using mechanical and index test results obtained from 24 coal surrounding rock samples (sandstone and siltstone) from the Zonguldak Basin. The input space is constructed from seven routinely performed laboratory tests describing rock strength, drillability, abrasivity and hardness. Correlation analysis and Variance Inflation Factor (VIF)-based screening are employed to identify and remove redundant or weakly contributing parameters, leading to more compact DNN architectures. The resulting models achieve high predictive performance, with R2 values of about 0.8-0.86 for BWI and up to about 0.95-0.96 for HGI on the available dataset and additional stratified train-test splits and cross-validation analyses confirm the potential of the approach, particularly for HGI prediction. Overall, the proposed methodology demonstrates that DNNs can capture multi-parameter nonlinear interactions more effectively than traditional empirical formulations and offers a practical tool for rapid grindability assessment, mining energy optimization and the development of data-driven rock characterization workflows.
This study presents a comprehensive laboratory analysis of the elastic and mechanical properties of Mrakotin Granite (MG), a high-quality granitic rock from the Czech Republic known for its strength, frost resistance, and low porosity. A range of experiments was conducted, including porosity and permeability tests, tensile and compressive strength measurements, ultrasonic velocity analysis, and evaluations of both static and dynamic elastic moduli. Results confirm that MG is a highly low-permeability material (Phi < 0.9 %) with high uniaxial compressive strength (UCS approximate to 180 MPa), making it highly suitable for geotechnical and geophysical applications such as hydraulic fracturing studies. Ultrasonic measurements under confining pressure revealed moderate elastic anisotropy at low pressure (k(a) approximate to 9 %), which decreases with increasing pressure (k(a) approximate to 3 % at 120 MPa), indicating microcrack closure and a transition from crack-dominated to matrix-dominated behaviour. Young's modulus increased by up to 80 % under pressure, and the measured static and dynamic elastic moduli are consistent with reported values for other granitic rocks. In conclusion, Mrakotin Granite demonstrates a highly homogeneous structure, mechanical stability, and excellent elastic properties, making it a promising material for demanding engineering purposes and advanced geophysical research.
The growing demand for durable high-performance concrete has highlighted the need for high quality coarse aggregates with strong mechanical and suitable mineralogical properties. In this study, the engineering and petrographic properties of Mansehra Dolerite (MD), a mafic intrusive rock from northern Pakistan, are investigated to evaluate its suitability as a high-strength aggregate for durable infrastructures. A total of 40 representative rock samples were collected from three key regions: Tanawal Area (Group A), Plura Area (Group B) and Oghi Area (Group C). The petrographic analysis of 25 samples from these three groups confirmed the doleritic nature of the rock, characterized by a dominant plagioclase (30-60 %) and clinopyroxene (20-40 %), with accessory orthopyroxene, hornblende, opaque minerals, and minor biotite, quartz, and chlorite. The rocks exhibit typical ophitic to sub ophitic textures with occasional porphyritic features, indicating slow crystallization in an intrusive environment. Petrographic screening indicates low Alkali Silica Reaction (ASR) potential; however, definitive confirmation requires performance based expansion testing standardized by American Society for Testing and Materials (ASTM C1260 and/or ASTM C1293/C1293M), which was not conducted in this study. The engineering tests revealed that the MD aggregates have excellent physical and mechanical properties, including low porosity (0.51-2.80 %), minimal water absorption (0.16-0.96 %), high specific gravity (up to 3.25), and high resistance to impact, crushing, abrasion, and weathering. Group A (Tanawal) samples consistently showed the highest quality, with the lowest impact strength value (4.1-10.5 %) and the lowest abrasion loss (8.5-13.4 %), making them particularly suitable for construction and heavy concrete applications. These results indicate that Mansehra dolerite is a mechanically competent aggregate source, and petrographic observations suggest low ASR potential; Group A material shows the most favorable overall indices for high-strength concrete applications.
Erosion rates derived from in situ-produced cosmogenic nuclides (CN) have made a significant contribution to quantitative geomorphology. In Europe, extensive CN-based erosion rate datasets mainly cover the Alps and western Europe. However, there is hardly any data from Central and Eastern Europe. This pilot study employs the analysis of in-situ produced cosmogenic 10Be and 26Al to reveal the erosion rate of alluvial sediments of the Jizera River, in the northern part of the Bohemian Massif. Samples of recently deposited alluvial quartz-rich sands were collected from two sites located on the upper and middle river drainage and CN concentrations were analyzed using the multi-isotope accelerated mass spectrometry (AMS) facility newly operating at the Nuclear Physics Institute in & Rcaron;e & zcaron;. Multi-grain size analysis was applied to identify complex sedimentary histories of the sediment. The calculated erosion rates 42 to 52 m Myr-1 are generally consistent between nuclides and align well with similar 10Be-derived erosion rates of 22 to 51 m Myr-1, which have been recently established for the southern part of the Bohemian massif by Robl et al. (2025). At one locality studied, the multi-nuclide technique revealed the coexistence and mixing of non-buried and buried sediments within the alluvial deposits. As an independent check of the erosion rate measurements, the cosmogenic dataset of Jizera River sediments was supplemented with the estimates of the uplift rate of respective bedrocks (45 to 100 m Myr-1), based on available apatite fission-track analysis data. Taking together, these data reveal the general predominance of uplift over erosion over the region, which is a widespread pattern characteristic of the Neotectonic period of accelerated uplift in compressional tectonic zones. Our case study demonstrates the potential of a paired CN technique as a modern tool for quantitative geomorphologic analyses applicable to various other terrains in Central and Eastern Europe.
This work presents new results of the pre-processing study of the Ni and Co-poor lateritic raw material from the reserved deposit K & rcaron;em & zcaron;e in South Bohemia. The proposed technological treatment of the raw material aims to obtaining a concentrate of elements of interest. This step is missing in the present Ni and Co extraction from laterites as the entire amount of the mined rock is being leached. This work focuses on a possible pre-concentrating of elements of interest and facilitating the final metallurgical processes. We carried out experiments involving interconnected pre-processing modes, such as magnetic separation, heating of the raw material, oxidation by ozone, or use of a low frequency supersound. The experiments showed that an appropriate combination of these pre-processing modes can result in a substantial concentration of Ni and Co (from 0.4 to 1.05 wt. % Ni and 0.01 to 0.027 wt. % Co). The incorporation of these pre-processing modes into the technological method of laterite ore processing can reduce total costs of the raw mineral treatment. In addition, it makes it possible to process poorer raw materials whose treatment was previously uneconomic.
Protective layer mining represents one of the most effective and economical regional strategies for rock burst prevention. Studying its anti-impact mechanisms carries significant scientific implications and provides substantial guidance for engineering applications. This study systematically investigates the pressure relief, vibration damping, and energy absorption effects of protective layer mining by integrating theoretical analysis, numerical simulation, and engineering practice. The results demonstrate that, within a certain temporal and spatial range after protective layer extraction, both the stress and abutment pressure in the protected layer are markedly reduced, with a stress reduction rate reaching up to 25 %. Additionally, the frequency and intensity of dynamic loads induced by the fracture and slip of overlying strata are substantially attenuated. The loose and fragmented structures formed by protective layer mining effectively dampen far-field mining seismicity. Furthermore, following the prior extraction of the protective layer, the incremental strain energy near the working face during the mining of the protected layer is significantly decreased. The effectiveness of protective layer mining in mitigating rock burst hazards is closely related to factors such as interburden spacing, lithology of intervening strata, and coal seam thickness. Building upon these findings and contextualized within a developing mine, a design scheme for protective layer mining is proposed, laying a solid stress foundation for reducing the rock burst risk in the protected layer.
In geotechnical engineering and underground mining engineering, coal and rock are often in the water-immersed environment, and the mechanical properties of coal and rock under the water-rock interaction show different degrees of deterioration. For this reason, coal and sandstone samples were prepared under six water immersion times of 10 d, 20 d, 30 d, 40 d, 50 d and 60 d, and uniaxial compression tests were carried out, and the results showed that: 1) With the increase of immersion time, the water content of coal and sandstone samples showed different degrees of increase, and finally gradually stabilized. The increase in water content of coal samples is more obvious than that of sandstone samples. 2) The compressive strength of coal and sandstone under water-rock interaction has obvious deterioration effect, and the deterioration effect of coal samples is more obvious than that of sandstone samples. The compressive strength of coal sample decreased by 50.55%, and the compressive strength of sandstone samples decreased by 10.92 % after being immersed in water for 60 d. 3) With the increase of water immersion time, the bursting energy index of coal and sandstone samples decreased to varying degrees, and the bursting liability was gradually weakened. 4) The water immersion time and coal rock material have a great influence on the failure characteristics of the sample, mainly in five aspects of crack length, crack initiation position, the angle between the crack and the axial direction of the sample, the number of cracks, and the failure type. 5) The damage model based on immersion time was constructed by using the damage theory. With the increase of immersion time, the damage degree of the sample increased gradually. 6) The calculation formula of void ratio is derived. The larger the pseudo void ratio is, the smaller the compressive strength will be. 7) The deterioration effect of the mechanical properties of coal rock under the water-rock interaction is the result of the gradual accumulation of the internal damage of coal and rock. Under the water-rock interaction, the physical and chemical effects inside the coal rock sample cause microscopic damage. 8) The multi-scale system of coal-rock structure under the water-rock interaction is constructed, and the water-rock interaction is analyzed from micro-scale, meso-scale and macro-scale. The research results can provide reference for the predicting the mechanical properties and studying the stability of rock mass in water-rich environments.
The present research combined aeromagnetic, aero-radiometric, induced polarisation (IP) and 2D electrical resistivity tomography (ERT) methods to reveal potential lithium-bearing pegmatite minerals of the study area. The datasets of combined airborne magnetic and radiometric data were processed and analysed using improved edge detections and hydrothermal techniques. The results of improved FVD-CET, AS-CET, %K_ratio_eTh, and Ternary grid anomalies reveal regions of major magnetic structures (lineaments), high amplitude magnetic anomalies, and hydrothermal alteration zones associated with the Pan-African Older Granitoid of the basement complex in comparison with the geological setting of the area. Several hydrothermally altered regions with high amplitude magnetic anomaly zones coupled with lineaments were identified to be favourable for lithium-bearing pegmatite minerals. These findings were consistent with previous aeromagnetic studies of the area, which focused solely on magnetic structures rather than alteration zones. Further geoelectric investigation along profiles 1 and 2 revealed three significant zones of lithium-bearing pegmatite mineralisation potential, marked as zones G, L, and L1. These zones are low/high resistivity and chargeability signature regions that could be viewed as probable target areas for lithium mineral exploration. Zones are located in Bajida and Gonan Goli of Kebbi state. The geoelectric approaches yielded a database of accurate coordinates, lateral lengths, and thickness/depths for possible lithium-bearing pegmatite zones. The innovative aspect of this research is the integration of datasets, the use of an improved targeting of hydrothermal alteration techniques, and the creation of a geophysical database for exact locations. These could guide the future exploration programs, supporting sustainable lithium resource development in Nigeria.
