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Mechanical activation and geopolymerisation of Taiwanese fly ash
The use of raw fly ash in the production of cement and geopolymer products is limited due to its low reactivity. However, the reactivity of fly ash can be increased through a mechanical activation process, allowing for its wider use. This study focused on the mechanical activation and geopolymerisation of coal fly ash originating from Taiwan. The mechanical activation was carried out by grinding in a batch-stirred media mill for 1, 3 and 5 minutes. The fineness and specific surface area significantly increased after 1 minute, and then only slightly decreased with further milling. The lime absorbance test showed significant improvement in pozzolanic activity (130-140% increase) after mechanical activation. For geopolymer synthesis, a 10M NaOH solution was used as an alkali activator with a liquid/solid ratio of 0.35. The geopolymer containing 3-minute fly ash grind produced the highest 28-day compressive strengths (36.1 MPa). The structural characterisation of fly ash and geopolymer was carried out by Fourier Transform Infrared (FTIR) spectroscopy. Changes in the FTIR spectra of fly ashes indicated the formation of amorphous aluminosilicate gel, a new reactive product as a result of geopolymerisation. Based on the strength development of geopolymers, mechanical activation enhanced the reactivity of fly ash, resulting in rapid setting and higher strength development during the early stages of geopolymerisation. The results clearly showed that, after mechanical activation, fly ash became a more reactive raw material, offering greater application potential than its current uses in cement production, road construction, and soil stabilisation.
Copyright (c) 2025 Fanni Dolgos, Roland Szabó, Wei-Ting Lin, Gábor Mucsi https://creativecommons.org/licenses/by/4.0
A Sustainable and Practical Machine Learning Approach Using Scikit-Learn for Predicting Stope Instability: Identification of Critical Geotechnical Factors
Stope instability remains a persistent and hazardous challenge in underground mining, impacting safety, efficiency, and sustainability. Traditional stability assessment methods, while valuable, are often limited by site-specific calibration, simplifications, and adaptability issues in dynamic underground conditions. While machine learning shows potential for improved accuracy, a critical gap persists in understanding how geotechnical factors interact in practice. This study introduces a novel, practical machine learning framework (Scikit-Learn) to predict stope instability, and crucially, to quantify the nuanced, non-linear influence and interaction of critical geotechnical factors in a shallow gold mine. Comprehensive geotechnical investigation (observations, lab tests, rock mass classifications, blast damage assessments) and advanced data analysis (Random Forest feature importance, RFE, decision boundary analysis) identified water ingress, blast-induced damage, and rock mass quality (RMR) as the most significant instability factors. Water ingress profoundly impacted stability, with moderate blast damage exacerbating instability under high water ingress. Rock strength showed comparatively lower significance. The developed model achieved robust predictive performance (accuracy: 0.83, precision: 0.88, recall: 0.83, F1-score: 0.83). Based on these insights, tailored support patterns (e.g. 22mm/16mm cone bolts, timber props) are proposed to mitigate specific risks. This research significantly advances targeted rock mechanics solutions by providing a deeper, quantifiable understanding of complex instability mechanisms, enhancing mine safety and operational efficiency in shallow gold mining.
Copyright (c) 2025 Amos Bemo, Deji Olatunji Shonuga, Tawanda Zvarivadza, Moshood Onifade, Manoj Khandelwal https://creativecommons.org/licenses/by/4.0
Integrating ANN Prediction with Honeybee Optimisation for Flyrock Minimisation in Open-Pit Mining
Flyrock is an undesirable phenomenon resulting from blasting in open-pit mines, posing significant risks to both environmental and human safety. Given these risks, a comprehensive study of flyrock is essential to mitigate its adverse effects. This study presents a novel hybrid intelligent model designed to predict and minimize flyrock distance by integrating an Artificial Neural Network (ANN) with a Honeybee Optimization Algorithm. Utilizing a dataset of 334 blast records collected from the Sungun copper mine, various ANN models were developed and evaluated. After assessing multiple models through a formal scoring system, the most effective one was selected for optimization. The chosen ANN model demonstrated strong predictive performance, achieving coefficients of determination (R²) of 0.8930 and 0.8874, as well as root mean square error (RMSE) values of 0.2486 and 0.2512 for the training and testing phases, respectively, outperforming conventional empirical models. To further refine the blast pattern for safety, the Honeybee Optimization Algorithm was employed to minimize the predicted flyrock distance. The optimal flyrock distance was determined to be 7.25 meters, reflecting a 27.5% reduction compared to the lowest observed value in the collected data. This demonstrates the superiority of the proposed hybrid approach in enhancing blasting safety and efficiency.
Copyright (c) 2025 Mojtaba Rezakhah, Erfan Nemati, Amir Batarbiat, Manoj Khandelwal https://creativecommons.org/licenses/by/4.0
Seed-Guided, Fault-Aware Horizon Tracking via Greedy Propagation for Geological Modelling
The Gola Gas field in the northwestern Drava Basin lies within the structurally complex petroleum system. Its sandstone reservoirs exhibit spatial heterogeneity due to deltaic fan sedimentation, necessitating robust interpretation tools. We present a fully Python-based seismic horizon extraction and 3D geological modelling workflow tailored to this challenge. The method leverages the greedy propagation method to track horizons with optimal reflector continuity, overcoming limitations of manual interpretation and traditional auto-pickers in faulted or noisy zones. An initial approximation is provided via seed-guided interpolation. Around this surface, a seismic sub-volume is extracted and flattened to align reflections. Within this volume, the greedy algorithm identifies a locally optimal path, maximizing continuity and amplitude strength. Our implementation extends the cost-based framework with slope guidance, local correlation, and robust outlier filtering to ensure geologic realism and repeatability. Surfaces are converted to 3D meshes using interpolation and smoothing, and the results are integrated into an interactive 3D geological model of the Gola field. For the first time in the research area, the model was built entirely in Python, enabling automated horizon extraction, geobody visualization, and seismic interpretation within a flexible, open-source environment.
Copyright (c) 2025 Ana Brcković, Marko Cvetkovic, Josipa Kapuralić, Ivan Medved https://creativecommons.org/licenses/by/4.0
Concentrating the Zn-Pb sample from Nignan ore deposit
The aim of the present study is to increase the grade of zinc from lead and zinc ore from the Noahang (Chah-Ahan) area of the Nignan deposit to more than 40% Zn, using the flotation method. The experiments were conducted on a low-grade sample with average grades of 3% zinc and 0.05% lead. The main zinc minerals in this sample were sphalerite and smithsonite in the carbonate field, and galena was rarely observed. The results of mineralogical studies and polished thin sections showed that the degree of liberation of zinc ores in the size fraction -106+75 μm was 90-95%. Different parameters such as size, solid mass percentage, type and dosage of collector were investigated, and under optimal conditions, a concentrate with a grade of 5% Zn and 70.4% recovery was obtained. In addition, kinetic experiments were conducted to achieve the highest Zn recovery in the rougher stage, which reached up to 58.6% of recovery. The results indicated that the best grade and recovery of zinc were achieved when the sample was crushed with a wet rod mill for 8 minutes to reach to the desired degree of liberation. The final grade and recovery of flotation method were obtained as 42.19 and 40.08% for zinc concentrate, respectively.
Copyright (c) 2025 Maryam Abedi Moghadam, Marzieh Hosseini Nasab, Mohammad Noaparast https://creativecommons.org/licenses/by/4.0
Anthropogenic Hazards Assessment of Mining Activities in Surghar Range, Punjab, Pakistan
This study investigates the environmental impacts of coal mining in the Surghar Range, Punjab, Pakistan, with a focus on the contamination of soil and groundwater by potentially toxic elements (PTEs). To evaluate the extent and sources of contamination, 22 soil and 134 water samples were collected and analyzed using standard physicochemical and geochemical methods. This study identified iron (Fe), zinc (Zn), and lead (Pb) as the primary anthropogenic PTEs, while chromium (Cr) and arsenic (As) also appear at elevated levels in some locations. Soil PTE concentrations followed the order: Fe > Zn > Pb > Cr > As. Pollution indices revealed moderate to high levels of contamination, with a pollution load index (PLI) of 1.20 indicating overall moderate pollution. Although the potential ecological risk index (PERI) was relatively low at 18, the concentration levels of certain PTEs exceed internationally recognized thresholds, necessitating environmental remediation. Principal Component Analysis (PCA) identified Fe and Zn as dominant contributors to pollution. Groundwater analysis showed a spatial gradient in water quality: samples near the Indus River were of freshwater quality, while salinity levels increased westward toward the mining zone, with brine-level salinity detected in Tehsil Isa Khel. Water near mining areas exhibited lower pH and higher electrical conductivity (EC), indicative of acid mine drainage and sulfur-rich coal influence. Comparative analysis with global mining regions revealed that mean PTE concentrations in the Surghar Range are significantly higher, emphasizing the need for sustainable mining practices and targeted environmental management strategies to protect soil and water resources.
Copyright (c) 2025 Syed Sajjad Ahmad, Hafiz Muhammad Zaheer Afzal, Hafiz Zeeshan Akram, Sajjad Khan, Ihtisham Islam, Salman Ahmed Khattak https://creativecommons.org/licenses/by/4.0
Combining the 3D geological and geostatistical modelling of gypsum concentration, using historical borehole and blasthole information
Geostatistics is used for estimating grade content in an ore deposit, thus enhancing the 3 Dimensional (3D) geological model precision, lowering the geological risk and boosting the performances of the mining industry. Despite the advantages, for decades the commercial application of the geostatistical tools has been mainly restricted to large metal mines. Except for a few notable cases, the majority of small-scale operations (quarries and industrial minerals) had hardly adopted geostatistical methods, due to the perceived complications compared to the advantages. Nowadays, things are changing, thanks to: the reduction of artisanal mining, the spread of informatization in all sectors, the necessity to improve the quarrying efficiency, the path towards sustainable mining and the challenges to obtain the social license to operate. This paper presents the recent digitalization and modelling work conducted at the “Monte Tondo” gypsum quarry (Italy), located in Northern Italy, besides the UNESCO natural heritage site named “karst and evaporite caves of the northern Apennines“. The geostatistical techniques were used to estimate the concentration block model using blastholes while a 3D geological model was realised using historical boreholes. Combining two models could help for a better understanding and interpreting of the extracted gypsum and could make an important improvement to detect the future areas of interventions, subjected to strict authorization rules by the local environmental authority.
Copyright (c) 2025 Stefano Merli, Sara Kasmaee, Cristina Barbalucca, Vanessa Cellini, Francesco Tinti https://creativecommons.org/licenses/by/4.0
Application of Artificial Intelligence in Reservoir Simulation for Characterizing Multi-Phase Fluid Flow through Petroleum Reservoirs
Artificial intelligence (AI) has rapidly advanced and influenced all scientific fields, including the petroleum industry, where it is no longer a novel concept. This article explores the evolving role of AI and its integration with traditional reservoir simulation approaches. The first section highlights the diminishing emphasis on conceptual and mathematical modelling in reservoir simulation, with a growing focus on sophisticated numerical solution techniques. This shift often neglects fundamental reservoir physics and mathematics, leading to the superficial characterization of multi-phase fluid flow in petroleum reservoirs. The second section examines the prevalent use of petroleum software packages, which heavily rely on input data without accounting for variations in data scales. These tools treat underlying programming as a black box, often bypassing critical basic sciences, such as reservoir conceptualization, applied mathematics, and numerical techniques, resulting in incomplete reservoir characterization. The third section discusses the role of machine learning (ML) and AI in reservoir applications. While data science plays a pivotal role, the lack of integration with fundamental reservoir physics reduces fluid flow analysis to an art devoid of scientific rigour. The final section proposes a hybrid approach that couples AI/ML with traditional reservoir simulation. This integration bridges the gap between science-based reservoir simulation and AI-driven fluid flow characterization, enabling the petroleum industry to achieve a new paradigm for multi-phase fluid flow analysis. By combining fundamental science with advanced AI techniques, this approach offers a comprehensive framework for accurate reservoir characterization and improved hydrocarbon production.
Copyright (c) 2025 Viswakanth Kandala, Suresh Kumar Govindarajan, Tummuri Naga Venkata Pavan, Swaminathan Ponmani, Srinivasa Reddy Devarapu https://creativecommons.org/licenses/by/4.0
MAETEC: An Optimized MATLAB-Based Approach for Accurate and Efficient Terrain Correction in Complete Bouguer Anomaly
Terrain correction is important in generating accurate Complete Bouguer Anomaly (CBA) maps and is essential for gravity studies. Traditional methods for terrain correction can be time-consuming, and many computerized programs are computationally intensive. To address these challenges, we introduce MAETEC, an optimized algorithm MATLAB-based approach designed to enhance the efficiency and accuracy of terrain correction in CBA computations. MAETEC uses an effective algorithm and MATLAB's parallel computing capabilities to speed up the terrain correction process. Our approach integrates simple gravity equations, e.g. Nagy prism and its simplification techniques, zonation strategy, and parallel processing to significantly reduce computation time without compromising accuracy. Validation of MAETEC against other terrain correction software and methods demonstrates its effectiveness in producing precise CBA maps with reduced processing times. We illustrate the application of MAETEC through a case study in a geothermal exploration area and also for a regional gravity study. The results show a marked improvement in the efficiency of terrain correction compared to existing available software, e.g. GTeC, Oasis, and TCDEMF, making MAETEC a valuable gravity processing tool. Overall, MAETEC offers a fast, accurate, and user-friendly solution for terrain correction, facilitating the generation of high-quality CBA maps and advancing our understanding of subsurface geological structures.
Applied Mathematics, Physics, Space Sciences
Copyright (c) 2025 Indra Arifianto, Jun Nishijima, Rahmat Catur Wibowo https://creativecommons.org/licenses/by/4.0
An investigation on the potential recovery of copper from a low-grade copper resource containing high silica content
The present research aimed to experimentally peruse the tank leaching of copper from a low-grade ore containing 0.57% Cu, 24.69% Si, and 10.367% Fe. To this end, firstly, the behaviour of important variables influencing the dissolution process was examined using the one factor at a time (OFAT) method and response surface modelling (RSM), and then the leaching kinetics was scrutinized to obtain further recognition of the leaching operations. The findings derived from the OFAT technique demonstrated that the particle size of 0-2 mm was a better choice for the tank leaching relative to other fractions. The presence of clay compounds in the ore led to a high increase in acid consumption. The leaching rate improved with an increase in the solid percentage from 20 to 40% and then decreased with a further increase due to lack of proper mixing. Additionally, the pulp stirring speed had a little effect on the dissolution of copper, confirming that the mixing rate of pulp should be at a level that suspends the particles and prevents them from settling. Then, the RSM was utilized to model and optimize the process and determine the possible interaction between operating parameters. The results showed that copper leaching was significantly dependent on the interaction between the factors. The optimized amounts of parameters were found to be 90 kg/t acid concentration, 35% solid content, 150 rpm pulp mixing rate, 2.5 h leaching time, and 0-2 mm size range. The kinetic analysis showed that the diffusion model through the production layer with an activation energy of 14.15 kJ/mol was the rate-controlling stage of the reaction, and based on that, a new model describing the dissolution kinetics of copper was proposed.
Copyright (c) 2025 Milad Abbassi, Asghar Azizi https://creativecommons.org/licenses/by/4.0
Geology and mineralogy of Fe-Sn deposit in Batubesi, Belitung Island, Indonesia
Batubesi deposit is a primary Fe-Sn deposit located in the eastern part of Belitung, Indonesia. Previously, mineralisation was known as greisen, but recent studies show that the region has developed skarn-type deposits. Despite that, skarn-type mineralisation has not been well studied. This research aims to study Fe-Sn mineralisation in Batubesi with a geological and mineralogical approach, primarily from field observation and diamond drill core logs and samples. The laboratory methods used are petrology, SEM-EDX, micro-XRF, and XRD analysis to determine mineralogical assemblage, as well as fused bead XRF to determine the geochemical composition of the deposit. The results show that the Batubesi deposit is dominated by skarn-type mineralisation with minor greisen. Two skarn stages have been observed: prograde and retrograde. Prograde stages are characterised by the precipitation of calc-silicate minerals and magnetite then often fractured or brecciated. The fractures and breccia are then altered by the retrograde stage, composed of amphibole, muscovite, and biotite. Sometimes, the alteration is so intense that the previous prograde stage is completely altered. Furthermore, the retrograde stage consists of sulfide minerals as veins such as pyrite, chalcopyrite, and arsenopyrite. Magnetite is the common ore mineral, followed by cassiterite, scheelite, ferberite, pyrite, chalcopyrite, arsenopyrite, glaucocerinite, and sphalerite.
Copyright (c) 2025 Aryo Dwi Handoko, Mochamad Slamet Sugiharto, Syafrizal, Alfend Rudyawan, Benyamin Sapiie https://creativecommons.org/licenses/by/4.0
Comparison of the Polynomial Regression Zonal Estimation Comparison of the Inverse Distance Weighting and Ordinary Kriging Linear Interpolator: Case Study of Porosity Estimation in Neogene Sandstone Reservoirs in the Sava Depression, Croatia
Two reservoir porosity datasets are mapped using the Polynomial Regression (PR; zonal estimation), Inverse Distance Weighting (IDW) and Ordinary Kriging (OK; both are interpolations). Data were collected from sandstone reservoirs "K" (19 samples) and "L" (25 samples), of the Lower Pontian age, and located in the Sava Depression, Croatia. Maps were compared via cross-validation (mean square error) and visual inspection of key features. Datasets are considered as small ("K" reservoir) and large ("L" reservoir). In the "K" reservoir, the MSE values are 0.000119 for IDW vs. 0.05401 for PR. In the "L" reservoir the MSE are 0.000676 (OK) vs. 0.040117 (PR). Zonal estimation obviously did not prove as primary mapping in the sets with about 20 datasets. A linear interpolator like the IDW or OK are much better choices, especially if a spatial model can be reliably modelled, like in this case for porosity characterised with normal distribution, which favoured the OK. However, zonal estimation can be a useful addon in interpretation, especially in zones where interpolation is considering less reliable, like in transitional areas.
Copyright (c) 2025 Tomislav Malvić, Josip Ivšinović, Uroš Barudžija, Ivana Brajnović, Maria Rudec https://creativecommons.org/licenses/by/4.0
Lithofacies types of Eocene-Oligocene deposits from the top of Promina Mountain, (Karst Dinarides, Croatia)
Clastic deposits from the upper part of the Promina Beds, situated at the top of Promina Mt. (External-Karst Dinarides) in Croatia, were investigated. The aim of this study is to present and promote geological peculiarities of the Promina Mt. and its surroundings. Sedimentary succession in the study area comprises 3 lithofacies types, as the main findings of this study: carbonate conglomerates, calcarenites and calcareous mudstones. The results obtained by the combined field recording of the sedimentary succession and micropetrography are presented in the lithological column PRO-21, showing the determined lithofacies types and their characteristics. Attributed shelf to delta sedimentary environments were further discussed.
Copyright (c) 2025 Uroš Barudžija, Tomislav Puncet, Ana Maričić https://creativecommons.org/licenses/by/4.0
Stratigraphic Age and Morphometric Features of Upper Pleistocene Flow Till Pebbles from the Papuča Gravel Pit (Gospić, Croatia)
This research was conducted in the Papuča gravel pit southeast of Gospić (Croatia). The fan-shaped sediment body, up to 14 m thick, is composed of poorly sorted, coarse-grained, and unconsolidated sandy gravel. Over 200 blocks and pebbles were selected from the gravel pit. The main criteria for sample selection were based on their potential for determination of their stratigraphic age. The clasts are Jurassic, Triassic, and Permian provenance. Selected clasts were measured for morphometric analyses. According to the median (Md = 7.5 mm), the gravel is medium-grained, with 50% gravel size clasts. The Trask coefficient (So = 6.44) indicates poor sorting, while the skewness coefficient (Sk = 0.313) indicates an asymmetric clast size distribution, with a prevalence of clasts larger than the median value. The Zingg diagram points to a predominance of spheroidal clasts followed by discoidal and rod-shaped clasts while blade-shaped clasts are scarce. The flatness ratio of clasts is 1.7, which is consistent with traditional calculations for fluvio-glacial and moraine deposits. The clast lithological analysis (macro and micro) was performed. The results indicate the dominance of limestone clasts. Glacially formed troughs, including ravines Ljutik and Široka Draga on the northern, steep slope of Mt. Velebit, served as pathways for meltwater, which transported clasts of various sizes and stratigraphic ages, originating from the glacier base and from the hillsides. The age of the gravel is estimated at 20,700 ± 200 years, during the glacier retreat at the beginning of Greenland Interstadial No. 1 of the Late Pleistocene.
Copyright (c) 2025 Josipa Velić, Ivo Velić https://creativecommons.org/licenses/by/4.0
Magnetic and Geochemical Studies of Iron sand Deposits around Tambora Volcano in Sumbawa, Indonesia: A proxy for search high quality iron sand
Tambora Volcano is known as a volcano that had a catastrophic eruption in 1815, with an enormous amount of material released, including pyroclastic flows and fall deposits. From these volcanic products, erosion and transportation processes acted towards the coastline and formed iron sand, which contains economically valuable minerals such as magnetite. Iron sand is one of the important elements/components for the production of steel and titanium, but its use is still limited. In Indonesia, iron sand has only been mined and used as a mixture in the production of cement and building materials because of its low iron content (45-48%). Therefore, it is important to know the characteristics and content of iron sand to maximise the results of iron sand mining. The characteristics of iron sand deposits from Mount Tambora are still unclear and have never been studied. Therefore, we conducted a study to identify the geochemical characteristics and magnetic properties of iron sand around Tambora Volcano to determine the concentration of economic elements from different volcanic products. This study uses magnetic susceptibility measurements, X-Ray Diffraction (XRD), X-Ray Fluorescence (XRF), and Inductively Coupled Plasma - Optical Emission Spectrometry (ICP-OES) measurements. Sampling was carried out at three locations in the area, namely Nanga Miro, Baringin Jaya, and Hodo. The Nanga Miro and Baringin Jaya areas are the lava flow deposit zones of the 1815 eruption and lava flows from previous eruptions, while the Hodo area is just the pyroclastic flow zone of the 1815 eruption. The results showed that the distribution of sand grain sizes in Baringin Jaya and Nanga Miro that are from the lava flow deposit zones are dominantly medium sized (MS) and fine sized (FS). Meanwhile, in Hodo, which is from the pyroclastic flow deposit zone, it is dominantly coarse sized (CS) and medium sized (MS). Areas from the lava flow zones have high susceptibility values, high Fe concentrations, and magnetic mineral content of magnetite and haematite. In addition, the REE elements (Ce, Gd, and Pr) have a high concentration in iron sand from the lava flow area and have a good Pearson correlation value. The combination of grain size distribution and magnetic and geochemical properties has shown differences in the characteristics of iron sand in the Tambora Volcano area.
Copyright (c) 2025 Putu Billy Suryanata, Adella Ulyandana Jayatri, Satria Bijaksana, Silvia Jannatul Fajar, Uivienin Harlianti https://creativecommons.org/licenses/by/4.0
Redefining Subsidence and Uplift Hotspots on Java Island, Indonesia: Multi-Temporal InSAR for Large-Scale Land Deformation Mapping
Java Island experiences complex and nonlinear land deformation resulting from the interplay of natural and anthropogenic factors. Previous studies predominantly investigated urban subsidence, leaving island-wide patterns less understood. This research addresses that gap by utilizing Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) data to identify comprehensive subsidence and uplift patterns across Java Island. Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) data reveal previously underrepresented subsidence at Karawang (-1795 mm), Cilacap (-902 mm), and Madiun (-742 mm) to be higher than coastal Jakarta's subsidence. Notably, Greater Jakarta's most severe subsidence occurs inland at Bekasi and Cikarang, which challenges common assumptions for urban subsidence. This challenges traditional perspectives on urban subsidence, offering a broader understanding of Java's regional geodynamics. By integrating hotspot analysis and MT-InSAR, the study enhances land deformation monitoring and requires continuous monitoring to enable efficient land management and infrastructure planning, particularly in high-risk, poorly monitored areas such as Karawang, Cilacap, and Madiun. These findings can be applied by geologists, urban planners, and policymakers to mitigate geological hazards and ensure sustainable development.
Copyright (c) 2025 Argo Galih Suhadha, Mohammad Ardha, Farikhotul Chusnayah, Rido Dwi Ismanto, Rizky Ahmad Yudanegara, Atriyon Julzarika https://creativecommons.org/licenses/by/4.0
Evaluating Liquefaction Susceptibility Through HVSR and MASW Methods: A Case Study in Mamuju, West Sulawesi, Indonesia
The 2021 Mamuju earthquake (Mw 6.2) highlighted the region's high vulnerability to liquefaction due to its proximity to active tectonic zones, as well as the presence of Holocene alluvial deposits, unconsolidated sedimentary formations, and water-saturated soils, all of which contribute to high susceptibility to seismic shaking. This research assesses the liquefaction potential of the Mamuju area using an integrated approach that combines the Horizontal-to-Vertical Spectral Ratio (HVSR) and Multichannel Analysis of Surface Waves (MASW) methods. This study investigates an inverse correlation between Vs30 and the seismic vulnerability index (Kg), where lower Vs30 values correspond to higher Kg values, indicating increased liquefaction susceptibility. The HVSR analysis shows that the dominant frequency (f₀) ranges from 0.4 to 11 Hz, while the amplification factor (A₀) varies between 1.1 and 11. The results indicate that coastal zones with thick alluvial deposits exhibit the lowest f₀ values (<2.5 Hz), which correlate with Vs30 < 175 m/s and Kg > 10, suggesting a higher likelihood of liquefaction. Conversely, areas underlain by the Mamuju Formation and Adang Volcanics, characterized by higher Vs30 (>175 m/s), f₀ (>10 Hz), and Kg (<10), show lower susceptibility. These findings contribute to developing a detailed microzonation map for liquefaction risk, which is essential for improved urban planning and disaster mitigation in Mamuju. This study demonstrates that integrating HVSR and MASW methods is an effective approach to characterizing soil properties and enhancing liquefaction risk assessment in seismically active regions.
Copyright (c) 2025 Andri Moh Wahyu Laode, Muh. Altin Massinai, Muhammad Fawzy Ismullah Massinai, Erfan Syamsuddin https://creativecommons.org/licenses/by/4.0
Laboratory investigation of the performance of two-component grout behind the segment in sandy soils below the water table level
The increasing use of TBMs in urban areas has heightened the significance of grouting behind tunnel segments. A key aspect of mechanized tunnelling is the application of grout for injection behind the tunnel segments (support system) to prevent ground settlement. This study aims to investigate the extent of two-component grout washout behind the segments and also simulates the presence of water flow in laboratory tests, which distinguishes it from other studies. Issues such as groundwater seepage and grout washout behind the segments—particularly when there are failures in the brush (caused by factors like reduced tail thickness from friction with the segment ring, misalignment in the shield from improper assembly, or damage to articulation jacks)—can lead to incomplete filling of the space and cause surface settlements. As a result, selecting an appropriate grout mixing design under these conditions is critical. A study was conducted to assess the injection of two-component grout with varying amounts of bentonite and sodium silicate. Different injection pressures and water conditions were simulated to evaluate grout penetration in coarse-grained soils. The grout injection tests at pressures of 1 and 2 bars showed that increasing the bentonite and sodium silicate in the grout mix not only reduces penetration into the soil but also helps prevent washout due to water flow. It is important to note that sodium silicate has a greater effect on this process than bentonite.
Copyright (c) 2025 Samaneh Khodaei, Erfan Khoshzaher, Hamid Chakeri, Shahla Miri Darmarani https://creativecommons.org/licenses/by/4.0
Unveiling the dynamics of deep-focus earthquakes: Insights from the 2020 Jepara event Mw 6.7 and subduction processes beneath Java
The 2020 Jepara earthquake (Mw 6.7) represents a significant deep-focus seismic event within the subducting Indo-Australian Plate beneath Java Island. This study investigates its characteristics using automatic moment tensor inversion and double-difference relocation based on seismic data from the Meteorology, Climatology, and Geophysics Agency (BMKG). A total of 1,899 earthquakes recorded between 2010 and 2022 were analyzed, focusing on events with a depth of up to 600 km. Moment tensor analysis revealed a normal faulting mechanism with two nodal planes: the first with a strike of 310º, dip of 59º, and rake of -68º, and the second with a strike of 92º, dip of 37º, and rake of -121º. The centroid depth was refined to 544 km, and the variance reduction (VR) exceeded 60%, which classifies the results as high-quality (Category A). Approximately 85% of the hypocenters were successfully relocated, improving root mean square (RMS) travel times to less than 1 second. The earthquake is associated with slab dehydration and the polymorphic phase transition of olivine to spinel under high pressure and temperature. These processes contribute to changes in fault mechanisms and the structural variation of the mantle transition zone. The findings enhance understanding of the deep-focus earthquakes beneath Java and can significantly contribute to future earthquake mitigation in Indonesia.
Copyright (c) 2025 Tio Azhar Prakoso Setiadi, Anne Meylani Magdalena Sirait , Yehezkiel Halauwet, Andrean Vesalius Hasiholan Simanjuntak, Adi Susilo https://creativecommons.org/licenses/by/4.0
Machine Learning for Fully Automated Detection of Volcanic Seismic Signals in Real Seismic Records at Sinabung Volcano, North Sumatra
To mitigate damage from volcanic disasters, experts conduct thorough monitoring mainly through observing volcano earthquake types, as these patterns provide crucial insight into magma movement. Traditionally, the classification of earthquake types has been performed manually, a process that is both time-consuming and subjective. To solve this problem, several studies have developed machine-learning models to classify volcano earthquake types automatically. However, previous research typically trains and evaluates machine learning models on existing datasets. Although these models can classify volcanic earthquake types, they require manual selection of input earthquake signals for classification. To fill this gap, this study aims to develop a machine learning model that integrates with an event detection algorithm to enable fully automated detection in actual seismic recordings at Sinabung Volcano. This study employs the Short-Term Average/Long-Term Average (STA/LTA) method, which calculates the ratio between two time windows for event detection. Two machine learning models, namely a Multi-Layer Perceptron (MLP) based on neural networks and a Random Forest (RF) based on decision trees, are used to classify the events detected by the STA/LTA method. Consequently, this approach enables the machine learning models to operate fully automated. In this study, we first detect events using the STA/LTA method on a daily basis; subsequently, each detected event is classified using a machine learning model developed based on the dataset. RF and MLP successfully predict relative low difference percentage compared to the actual number in earthquake catalogue, with values of VT 5.31%, LF 46.62%, and EXs 30.95%. Automated detection and classification can improve the efficiency of mitigating the risks associated with volcanoes by identifying potential anomalies in advance.
Copyright (c) 2025 Bagas Anwar Arif Nur, Mohammad Hasib, Estu Kriswati https://creativecommons.org/licenses/by/4.0
Subsidence prediction for twin tunnels using Genetic algorithms-case study: Isfahan, Iran
Today, with the increasing pace of urbanization and population growth, the demand for metro tunnels has risen significantly. These tunnels are often constructed at shallow depths and in close proximity to urban infrastructure. In such scenarios, it becomes crucial to safeguard buildings and other structures from potential damage caused by metro tunnel excavation. Therefore, studying ground movements and surface settlements induced by these tunnels is essential to ensure the safety of surface structures. In this research, based on the geotechnical conditions of the study area and data collected during the excavation of twin metro tunnels using a Tunnel Boring Machine (TBM), an empirical method was developed to estimate subsidence, maximum subsidence, and inflection points. Boltzmann and Gaussian functions were employed to derive these parameters. Furthermore, a sensitivity analysis was conducted using three-dimensional Plaxis software for the cross-section of Si-o-se-pol alongside three other soil types. A numerical relationship for predicting maximum subsidence was then proposed using a genetic algorithm. The results revealed strong alignment between the empirical and numerical approaches derived in this study. Consequently, these findings enable the accurate prediction of ground subsidence for Isfahan Metro Line 2, which is now under excavation.
Copyright (c) 2025 Amirhossien Rostami, Hamid Chakeri, Kurosh Shahriar, Masoud Cheraghi Seifabad https://creativecommons.org/licenses/by/4.0
Assessment of the Severe Geomagnetic Storm on May 10-11, 2024 Effects on the Geomagnetic Field and Ionosphere Over the Indonesian Region
The severe geomagnetic storm on May 11, 2024, is the largest space weather phenomenon in the 25th solar cycle. This paper presents the impact of that severe geomagnetic storm (Dst = -412 nT) on the geomagnetic field and ionosphere over Indonesia. Additionally, it examines geoelectric disturbances generated during the storm and disruptions to HF radio communication in Indonesia. The study uses geomagnetic field data (HDZ) and ionospheric data (foF2) from Pontianak and Kupang, averaged minutely and hourly. The results indicate that the geomagnetic storm on May 11th, 2024 caused geomagnetic disturbances with ΔH = -471 nT in Pontianak and ΔH = -462 nT in Kupang. These disturbances led to significant geoelectric disturbances around the region with possible induced currents. In the ionosphere, the geomagnetic storm caused negative ionospheric storms during the main phase and positive storms during the recovery phase, as observed in the foF2 data from both stations. These ionospheric storms resulted in foF2 disturbances (ϕ) with values ranging from -30% to 30%, lasting for 180 minutes.
Copyright (c) 2025 Anwar Santoso, Sismanto Sismanto, Rhorom Priyatikanto, Eddy Hartantyo, Emanuel Sungging Mumpuni, La Ode Muhammad Musafar Kilowasid, Abdul Rachman, Fitri Nuraeni, Siska Filawati, Erlansyah, Dadang Nurmali https://creativecommons.org/licenses/by/4.0
Investigation of corrosion resistance of epoxy coatings reinforced with graphene oxide and aluminium nanoparticle
Graphene oxide (GO) is a two-dimensional carbon nanomaterial known for its exceptional mechanical strength and tunable, functional surface, making it a promising material for protective barriers against harmful external influences. For this purpose, it is used as a filler in epoxy coatings to increase the tortuous diffusion path of the corrosive medium within the coating. Additionally, the incorporation of aluminum nanoparticles (Al NP) onto the GO surface can improve the dispersion of GO sheets, reduce their aggregation, and maintain the passive oxidation film formed by the degradation of Al NP. In this study, epoxy coatings containing Al NP–GO powder with different weight fractions of GO material were prepared. The physical, anticorrosive, and antibacterial properties of unmodified and modified coatings were evaluated. On the macroscopic electrochemical micro-level, the EP+MAX sample provided stable corrosion protection for 60 days, whereas the resistance of other samples decreased. Measurements performed on micro-surfaces (500 μm) showed an increase in impedance per unit area with the addition of Al NPs-GO powder. The best corrosion resistance was demonstrated by the EP+MIN and EP+MAX samples. Furthermore, using the ic-dc microscopic SECM method, it was determined that Fe²⁺ dissolution at the scratch area was reduced in the modified samples. Additionally, it was found that the epoxy coating acquires new antibacterial properties, thus gaining dual functionality.
Copyright (c) 2025 Marin Kurtela, Marina Samardžija, Maro Bujak, Ivan Stojanović, Krunoslav Bojanić, Gabrijela Ljubek https://creativecommons.org/licenses/by/4.0
Modelling the seasonal dynamics of heavy metal pollution of water bodies within a mining area
Waste heaps from coal mines pollute the environment through spontaneous combustion of waste and contaminated wastewater leaking into underground and surface aquifers. Together with pollutants, a large concentration of heavy metals gets into the water. The object of this research is water bodies in the impact zone of the Chervonohrad mining district, a part of the Lviv-Volyn coal basin (Ukraine). Water chemical contamination level of anthropogenic reservoirs in the mining district was measured by indicators developed based on geochemical and hygienic environmental investigations. Methods of statistics of parameters of chemical pollution of water and soil; correlation analysis; data mining methods; assessment of similarity of ecotopes and grouping of chemical elements based on cluster analysis; multidimensional ordination of ecotopes in the space of geochemical indicators and graphical visualization based on Principle Component Analysis were used in the research. Chemical pollution of rivers and reservoirs in Male Polissia with heavy metals during the season is characterized by significant heterogeneity. The main reason of the deterioration of the ecological state of water bodies is related to the increase in the content of Mn, Ni, Cd, Pb compared to the maximum permissible concentration. The highest level of pollution during the entire monitoring season was observed in the water bodies at the foot of the Mezhyrichanska mine spoil tip near the city of Chervonohrad. Based on the correlation between the heavy metal content and the complex gradients of the aquatic environment, the following associations (groups) of chemical elements were identified: I - Cu, Pb, Co, Cd; II - Mn, Ni; III - Zn, Cr, Sr. The multidimensional ordination of water bodies on the axes of complex geochemical gradients of the environment reflects the seasonal dynamics of water pollution and its spatial features. The practical significance of the results obtained lies in the fact that forecasting dynamic trends, protection and restoration of ecosystem components are impossible without taking into account their relationships with environmental conditions, in particular, with the level of chemical pollution of water.
Copyright (c) 2025 Vasyl Popovych, Viktor Skrobala, Oleh Tyndyk, Mykhailo Petlovanyi, Kateryna Sai, Natalya Popovych, Roman Konanets, Bohdan Ilkiv https://creativecommons.org/licenses/by/4.0
Application of GIS technology in management of brownfield sites containing NORM residues
Since “brownfield sites” (BFS) are one of main environmental engineering challenges, countries in the European Union are obligated to consider their management. One of the main tools for BFS management is geographic information system (GIS) technology on two levels: representation of all BFS in the country and representation of material/pollutant distribution (surface, amount and concentrations, etc.) for the respective sites. The Republic of Croatia has several brownfield sites, including the site of the decommissioned factory of plastics and chemical products, Jugovinil, in the city of Kaštela, Croatia, as a significant example. The decommissioned chemical plant and its coal powered powerplant caused the contamination of the industrial site with heavy metals and naturally occurring radioactive materials (NORM) residues. Since the decommissioning of the factory, other, small-scale industrial activities (shipyards) caused the site’s additional contamination. This BFS has several locations containing a total of 552,101.29 m3 of different types of deposited material (coal ash and slag, brought in soil material, construction waste, etc.), of which ≈ 240.893 m3 are relevant for this research. The research presented here is based upon studies done in 2010-2011. Since the research data is overwhelmingly large, only part of it represented in this paper: measurements of three radionuclide concentrations from the 232Th decay chain. The computer GIS software ArcGIS Pro was used to create maps showing concentrations of radioisotopes, which enabled the identification of hotspots and potential sources of pollution. The interpolation was performed using the geostatistical method Empirical Bayesian Kriging. Recommended actions for the management of this location include remediation and caveat concerning urbanisation of the site.
Copyright (c) 2025 Želimir Veinović, Dario Perković, Tonka Radas, Ivica Prlić https://creativecommons.org/licenses/by/4.0
The effects of test specimen shape tolerances on determining the mechanical and energy dissipation properties of limestone rock
The uniaxial compressive strength (UCS), Young's modulus (E) and Poisson's ratio (ν) of intact rock depend on the cylindrical specimen shape tolerances (flatness of ends R, their parallelism P and perpendicularity to the specimen axis O). Today's specimen acceptance criteria (allowable R/P/O) are based on scarce research data from the 1970s that mostly relate to UCS without considering the rock strength category (worst case scenario). They are also very strict and, in some cases, difficult to implement, requiring engineering judgment. To increase reliability and facilitate judgment of specimen acceptability, this study investigates the influence of shape tolerances on all UCS/E/ν properties and related energy dissipation properties (total, elastic and dissipated energy) for limestone and comparable medium-strength rock with UCS around 100-150 MPa. Ninety specimens were prepared with target (wider) shape tolerances (R up to 0.5 mm; P, O up to 2°) using specially developed equipment for accurate R/P/O determination. These specimens were further tested in uniaxial compression with several relevant measurement settings and all mechanical and energy dissipation properties were determined. From many experimental results and additional statistical/numerical/energy analyses, reliable behaviour models for UCS/E/ν dependence on R/P/O have been established that can be further used to assess the consequences of shape tolerances and specimen acceptability. If limits of natural variability for 'ideal' specimens are applied to these models, critical tolerances that reduce the existing requirements are obtained (e.g. R = 0.08 mm instead of 0.05 mm), which are proposed as supplementary to optimize the testing process for medium-strength rocks.
Copyright (c) 2025 Nataša Štambuk Cvitanović, Boris Kavur, Ivan Vrkljan, Predrag Miščević https://creativecommons.org/licenses/by/4.0
Thermal degradation of diverse rock suites: Insights from fractures and physical and mechanical analysis
Understanding the effects of thermal treatment on the physical and mechanical properties of rocks is essential for evaluating their behaviour under extreme conditions and optimizing engineering designs. Thermal treatment significantly influences these properties, either enhancing or degrading them, depending on the mineralogical composition of the rocks. This study provides a detailed investigation into the thermal impact on the physical and mechanical properties of various rock types, including limestone, sandstone, granitic gneiss, rhyolite, quartzite, dolerite, gabbronorite, amphibolite, and granulite. The rock samples were exposed to heat treatment at temperatures ranging from 150°C to 1000°C. Subsequently, destructive (uniaxial compressive strength, point load index) and non-destructive tests (specific gravity, ultrasonic pulse wave velocity, porosity, and water absorption) were conducted to assess the rocks' responses to varying temperatures. The density of thermally induced fractures is calculated for each rock type at the investigated temperatures. The results indicate a reduction in uniaxial compressive strength, specific gravity, ultrasonic pulse velocity, and point load strength as the temperature increases. Conversely, elevated temperatures increased fracture density, porosity, and water absorption levels. A noticeable and abrupt change in all properties is observed when the temperature exceeds 300°C. These results highlight the deterioration of rocks and the alterations in their physical and mechanical properties caused by heat exposure, which hold critical importance for industries like mining and construction. Combining the physical and mechanical test results at different temperature levels, equations were derived for each rock type that can be used to estimate the respective properties at the desired temperatures. This study is significant for understanding how thermal exposure affects the integrity of rocks, providing crucial understanding for industries such as mining, construction, and geothermal energy. It enables better prediction and management of rock behaviour under high-temperature conditions, enhancing safety and performance in engineering applications.
Copyright (c) 2025 Abid Nawaz, Muhammad Sajid, Waqas Ahmed, Abdul Rahim Asif https://creativecommons.org/licenses/by/4.0
Morphotectonic analysis approach for active fault identification of the Cikandang Watershed, Southern part of West Java, Indonesia
This study aims to find possible active faults by analysing the morphotectonic features of the Cikandang Watershed in West Java, Indonesia. Morphometric indices including drainage density (Dd), lineament density (Ld), and bifurcation ratio (Rb) were computed using digital elevation models, topographic maps, and geological maps using Geographic Information System (GIS) software. As demonstrated by the different drainage patterns and lineament alignments that show tectonic activity, the analysis shows that tectonic forces substantially impact the watershed's shape. Higher Rb levels in sub-drainages suggest tectonic activity, while elevated Dd and Ld values indicate areas that are more susceptible to tectonic movements. These indices show possible active fault zones and strongly correlate with those shown on regional geological maps, especially in the watershed's centre and northern regions. In line with earlier studies that connected geomorphic indices to tectonic activity, the findings support the importance of these indicators in identifying active faults. This study highlights the importance of comprehensive morphotectonic assessments in determining earthquake hazards, providing vital information for disaster risk management and spatial planning in tectonically active areas. Through the alignment of geomorphic indices with geological features, the study pinpoint’s fault zones that need continuous observation, promoting safer community development and more resilient infrastructure in West Java's earthquake-prone areas.
Copyright (c) 2025 Ghina Husni Fahira, Emi Sukiyah, Dicky Muslim https://creativecommons.org/licenses/by/4.0
An investigation into the pretreating of a low-grade bauxite ore using magnetic separation and flotation processes
A bauxite reserve with an Al2O3 grade less than 50% is classified as low-grade bauxite due to the presence of impurities such as silica and iron oxide, thus pretreatment before concentration (Bayer process) is required to reduce SiO2 and Fe2O3. Hence, this study investigates the feasibility of removing SiO2 and Fe2O3 from a low-grade bauxite ore using magnetic separation and flotation processes. Different magnetic methods were utilized to eliminate iron impurities, and the results indicated that the high-gradient magnetic separation (HGMS) with an intensity of 18000 Gauss had the best output in reducing Fe2O3 with 18.03% Fe2O3 content and a 31.15% removal rate. In the next step, reverse and direct flotation techniques were employed to remove silica from the sample. The findings showed that the highest removal efficiency of SiO2 was achieved using the reverse flotation process at 750 g/t Armac C collector, 1200 g/t starch, 40 g/t MIBC and 11 pulp pH. Ultimately, the HGMS followed by reverse flotation with an Armac C collector (750 g/t) was implemented on the bauxite sample, and the content of Fe2O3, and SiO2, silica removal efficiency, and Al2O3/SiO2 mass ratio was found to be 17.76, and 22.11%, 45.22% and 1.63, respectively, indicating that HGMS followed by flotation is not effective enough in reducing iron oxide and silica, and therefore requires further pretreatment and/or the study of a suitable alternative method. The behaviour observed may be because iron oxide and silica minerals were fine-grained diseminated in the bauxite sample.
Copyright (c) 2025 Amirhossein Ashrafi, Asghar Azizi, Zahra Bahri https://creativecommons.org/licenses/by/4.0
Assessment of Geothermal Potential in Kepahiang District Using the Magnetotelluric Method for Renewable Energy Development
Indonesia, located on the Pacific Ring of Fire, has 147 volcanoes, with 76 still active, creating optimal conditions for geothermal energy development. Kepahiang Regency in Bengkulu Province is particularly promising for geothermal exploration, evidenced by hot springs, fumaroles, and other geothermal phenomena. This study aims to enhance subsurface analysis through a detailed Magnetotelluric (MT) survey, utilizing 17 strategically placed stations near geothermal manifestations and fault zones. The MT method is highly effective for deep geothermal resource exploration, as it measures natural geomagnetic field variations to assess subsurface electrical resistivity. Data were collected over 16 hours at different frequencies (128 Hz, 1024 Hz, 4096 Hz) and analyzed using MAPROS, ZONDMT1D, and ZONDMT2D software, producing detailed subsurface resistivity models. The results identified low-resistivity zones at various depths, indicating potential geothermal reservoirs with hot fluids and thermally altered stones. Notably, the measurements revealed a heat source in the upper layer with a resistivity of over 350 ohm-m, and at point 3L1, the presence of a cap rock beneath the manifestation at a depth of 1-2 km with a resistivity of 0.7-1.6 ohm-m. Below this cap rock, a hot rock layer with a resistivity exceeding 350 ohm-m was detected. These findings highlight promising targets for further exploration, including directional drilling to confirm the presence and feasibility of geothermal resources. Developing geothermal energy in Kepahiang could offer a sustainable energy source, reduce fossil fuel dependence, and support regional economic growth by creating jobs and lowering greenhouse gas emissions.
Copyright (c) 2025 Muchammad Farid, Hery Suhartoyo, Arif Ismul Hadi, Refrizon, Andre Al Ansory, Hana Raihana https://creativecommons.org/licenses/by/4.0