Korea Institute of Geoscience and Mineral Resources

A river sand sample from the Hongcheon River was magnetically fractionated to concentrate heavy minerals to determine the potential of the river for the recovery of valuable minerals. Separate magnetite/haematite-rich, ilmenite-rich, monazite-rich and zircon-rich fractions were characterised using chemical, X-ray and electron beam techniques. The magnetite particles were high-grade (93.7wt% Fe2O3), fully liberated and with minor or trace amounts of Mn, Mg, and Al. The ilmenite contained, on average, 53.6% Fe2O3 and 50.8% TiO2 and mineral impurities in the fraction included garnet, olivine, and magnetite, present as discrete grains. Two populations of monazite grains were found (Ce-rich and Th-rich), both dominated by LREEs. The zircon fraction contained ∼70% zircon particles with impurity phases including titanite and fluorapatite. Characterisation of the concentrates showed that the ilmenite could be commercially processed but further refinement of the processing conditions is required to generate higher grade concentrates that are potentially suitable for commercial applications.

The safe management and long-term disposal of high-level radioactive waste are crucial for safeguarding humanity and the environment from radioactive hazards and are regarded as a significant challenge for the global nuclear industry. This paper analyzes site selection criteria related to geological investigations in the cases of Sweden, Finland, Switzerland and France, which have either selected or proposed deep geological disposal sites. It reviews the investigation parameters and methods defined according to these criteria and comprehensively analyzes the rationale and importance behind deriving these parameters. Based on the analysis of international cases related to geological site selection criteria and evaluation factors, this paper proposes geological performance verification factors and items tailored to the geological characteristics of Korea. The geological performance factors of the natural barrier are categorized into nine detailed fields: rock and mineralogy, faults and discontinuities, seismicity, Quaternary geology, hydrogeology, hydrochemistry, thermal properties and rock mechanics. These factors are suggested by reflecting the geological characteristics of Korea, grounded in the case studies of leading countries in disposal research and the background and rationale for each verification item are described.

With the explosive growth of lithium‐ion batteries (LIBs), research on the recycling of spent batteries is widely conducted. However, conventional processes involve complex procedures, high costs, and environmental issues. This study introduces the electrochemical upcycling of spent LiMn2O4 (LMO) cathode material, incorporating pre‐filtration (PF) and pre‐reduction (PR) processes to enable its direct application in redox flow batteries (RFBs). Moreover, a double membrane system is applied to address the low operating voltage and energy density by balancing the different pH levels of the zinc anode and manganese cathode. The aqueous zinc‐manganese RFB, derived from spent LMO pouch full cells, achieves coulombic efficiency of 90% and energy efficiency (EE) exceeding 70% over 250 cycles. The LMO‐containing electrolyte is further treated to precipitate the manganese ions by a simple pH adjustment, enabling 100% separation of lithium, thereby enhancing the sustainability of rare metal resources. This work presents a remarkable advancement in the upcycling method of LIB cathode materials and contributes to establishing a circular system for battery materials.

This study investigates borehole thermal resistance (BTR) in ground source heat pump (GSHP) systems, focusing on the impact of grout thermal conductivity and borehole design on system performance. Using three calculation methods: Paul, multipole, and Koenig, the study assesses BTR across seven borehole heat exchangers (BHEs) with varying grout types, including conventional bentonite and thermally enhanced grout (TEG). Thermal response tests (TRTs) and simulations were conducted to evaluate thermal properties across different grout thermal conductivities (0.76 to 2.0 W/mK) and pipe configurations. The results demonstrate that TEG significantly reduces BTR, enhances heat transfer, and allows for shorter borehole lengths, thereby reducing installation costs. Among the methods, Paul’s approach consistently overestimated BTR compared to multipole and Koenig, underscoring the need to select suitable models for accurate GSHP design. This research highlights the importance of optimizing grout selection and pipe configuration to improve GSHP efficiency, providing reliable insights for sustainable system design and reduced environmental impact.

Rare earth elements (REEs) have become essential components of clean energy technologies, driving the transition toward a carbon–neutral and digitized future. Among REE-bearing minerals, bastnaesite is a key source of high-demand REEs, such as neodymium and praseodymium. Over the years, significant efforts have been made to develop robust and efficient methods for recovering REEs from bastnaesite ores. This review paper comprehensively examines the entire bastnaesite processing chain, from beneficiation to metallurgical treatment. It examines various techniques for concentrating bastnaesite minerals, including comminution, flotation, gravity separation, and magnetic separation, while providing insights into their underlying mechanisms. Moreover, the review highlights the critical interplay between beneficiation and subsequent metallurgical processes by analyzing the key properties of bastnaesite concentrates and their influence on downstream metallurgical treatment. A range of metallurgical techniques for REE recovery from bastnaesite concentrate is reviewed, with emphasis on established methods such as roasting, caustic digestion, and leaching. The review also explores recent technological advances in this field, including mechanochemical treatment, microwave heating, and the use of ionic liquids, particularly regarding their potential to improve both process efficiency and sustainability. Finally, the paper identifies key challenges and explores new directions for developing sustainable and resilient bastnaesite processing technologies.

Methanesulfonic acid (MSA) exhibits several advantageous properties rendering it a promising candidate for circular hydrometallurgical processes. These properties include a high acidity (pKa = − 1.9) comparable to that of classical mineral acids as well as biodegradability, high stability, and high solubility of metal-MSA complexes in aqueous solutions. In this study, MSA was employed as a lixiviant for the leaching of metals (lithium, nickel, cobalt, and manganese) from the black mass of spent lithium-ion batteries (LIBs). The effect of various parameters, including MSA concentration, H2O2 concentration, temperature, and pulp density, was systematically investigated. Under the optimized conditions (1.5 M MSA, 0.2 M H2O2, 60 ℃, and 50 g/L pulp density), quantitative leaching of lithium was achieved within 30 min, while for nickel and cobalt it was after 2 h, and 4 h for manganese leaching. The leaching kinetics of Li, Ni, Co, and Mn were studies using the shrinking particle models (SPM) and the Avrami model. The results indicated that the Avrami model provided the best fit to the kinetic data, with apparent activation energies of 46.81 kJ/mol for Li, 58.61 kJ/mol for Ni, 59.69 kJ/mol for Co, and 58.86 kJ/mol for Mn, within the temperature range of 25–70 ℃ (except for Li, which was analyzed in the range of 25–60 ℃), consistent with chemical reaction control. Subsequently, residual contaminants in the leaching residue were eliminated through pyrolysis. The quantitative leaching of metals in MSA solution (a green lixiviant), combined with the pyrolytic treatment of leaching residues, represents a circular strategy for the total recovery of valuable components from the black mass of spent LIBs.

High‐resolution sequence analysis of three drill‐cores with well‐constrained optically simulated luminescence ages reveals a stratigraphic evolution of the Baeksu tidal flat (south‐west coast of Korea) that documents the development of tide‐dominated estuary infill interacting with minimal fluvial processes since marine isotope stage 6. The stratigraphic architecture and its correlation along an upper mudflat‐to‐subtidal transect showcase two different hierarchies of sequences and small‐scale (fifth‐order) subsequences that are nested within larger‐scale (fourth‐order) sequences. Two fourth‐order sequences with a frequency of 100 kyr consist of lower fluvial deposits and overlying tidal deposits, respectively, reflecting a twofold regressive–transgressive sedimentary sequence related to glacial–interglacial cycles. The sequence boundary is marked by an abrupt facies change from oxidised spit gravels (marine isotope stage 3) to tidal muds (marine isotope stage 1). An interesting feature is the presence of two packages of retrograding spit/tidal deposits corresponding to marine isotope stage 5a/b and marine isotope stage 3 interstadials deposited during short‐lived sea‐level rise, in spite of the long‐term phase of glacial sea‐level fall spanning from marine isotope stage 5d to marine isotope stage 2. Identification of deeply oxidised tidal deposits dated to marine isotope stage 5a/b confirms that two short‐term fluctuations in sea level occurred during the long‐lived glacial period. Such nested successions are assigned as fifth‐order subsequences with 40 kyr time intervals. The late Quaternary Baeksu tidal flat succession was thus mainly controlled by two different frequencies and magnitudes of sea‐level changes, associated with major interglacials (marine isotope stage 5e and 1) and minor interstadials (marine isotope stage 5a and 3). More importantly, transgressive episodes are significantly better preserved than regressive phases. This study provides a good example of how multi‐order, multiple transgressive deposition may be preserved in a tide‐dominated estuarine setting, particularly where river inputs are negligible.

Snow avalanches pose a significant threat to both individuals and infrastructure. Deep learning algorithms have been shown to be an efficient tool for modeling snow avalanche and other similar natural disasters, but they require a large sample size for training. However, some regions do not have availability to the required amount of data. This study utilizes established techniques and approaches to address this shortcoming so that these advanced algorithms can be applied even in regions with limited data. It utilizes the recurrent neural network algorithm to model snow avalanche susceptibility, applies a robustness maximization approach to prevent overfitting, and uses three meta-heuristic algorithms for hyperparameter optimization: grey wolf optimizer, particle swarm optimizer, and artificial bee colony optimizer. A performance comparison with other models , including deep neural networks and support vector machines, using the same training strategy, revealed that optimized recurrent neural network models are significantly better suited for datasets with limited sample sizes. The RNN-ABC model demonstrated superior predictive performance (AUC = 0.9710, accuracy = 0.9318, RMSE = 0.2354, sensitivity = 0.9090, and specificity = 0.9545) compared to the RNN-PSO and RNN-GWO models. Relief-F variable importance analysis identified lithology, aspect, land use, slope position, and proximity to streams and roads as key factors in this region. The designed process shows significant effectiveness in regions with limited data size and quality. This hybrid approach can theoretically be applied to many different regions with data scarcity, and possibly even for other natural hazards, providing significant prediction reliability improvement over previous methodologies.

Multiple successive tracer tests are often conducted to obtain reliable breakthrough curve results under regional groundwater flow, especially when the accuracy is crucial. In such cases, the period of rest between the end of the first divergent tracer test and the initiation of the second divergent tracer test allows the tracer from the first test to travel along with the background regional flow, thereby influencing the distribution of residual tracer concentration. This residual tracer could potentially interfere with breakthrough curve results from the tracer injection in the second tracer test. Additionally, the conventional analytical solution used for the divergent tracer test considers only radial flow; regional flow and consecutive tracer tests are ignored. Consequently, interpreting the behaviour of the tracer in consecutive divergent tracer tests under regional flow conditions is challenging using conventional measures because of background regional concentration. This study proposes a new semi‐analytical solution, considering the effects of divergent radial and regional flows in consecutive tracer tests, addressing a critical gap in the conventional analytical solutions that, despite their practical necessity, have not been previously developed. The proposed semi‐analytical solution was subjected to parameter studies under various scenarios. In our case studies, the conventional analytical solution based on a single tracer test can be safely used for parameter estimation only in cases where the injected mass for the subsequent tracer test is approximately six‐fold that of the first tracer test or if the drift time is longer than 10 days.

Biostratigraphic research has been conducted over the last four decades in the southwestern Ulleung Basin and has provided a chronostratigraphic framework to understand basin evolution. However, absolute geologic ages for the basin are almost absent owing to the lack of deep-drilled cores. To obtain the absolute age from the zircon, we extensively sampled and measured five deep-drilled conventional cores previously obtained for hydrocarbon exploration from the southwestern shelf of the Ulleung Basin. Maximum Depositional Ages (MDAs) for each cored interval are determined by the youngest age groups of zircons and are calculated to 12.1 ± 0.1 Ma for Gorae I-1, 12.9 ± 0.2 for Gorae I-2, 10.2 ± 0.8 for Gorae V-3, 11.4 ± 0.7 for Gorae V-4, and 16.9 ± 0.3 for Dolgorae V-1. All age groups exhibited relatively narrow errors (avg. ± 0.4 Ma), indicating good accuracy of MDA. The MDAs for the cores were first obtained by Laser Ablation-Multi Collector-Inductively Coupled Plasma Mass Spectrometry (LA-MC-ICPMS), and the youngest zircon grains were subsequently reanalyzed by Sensitive High-Resolution Ion Microprobe (SHRIMP) to prevent age underestimation due to possible Pb loss. The differences in absolute ages of Gorae I-1 and I-2 between LA-MC-ICPMS and SHRIMP methods are below than 0.8 Ma, indicating good verification of MDAs. The four seismic horizons that overlapped with the cored interval were newly established as GR I-2 MDA, GR I-1 MDA, GR V-4 MDA, and GR V-3 MDA (except for Dolgorae V-1 because of the poor quality of the seismic profiles). The newly defined MDA horizons were compared to the biostratigraphic sequence boundaries of (Yi et al., Marine and Petroleum Geology 122, 2020), indicating ages 1 to 3 Ma younger than the biostratigraphic estimates. The four MDA horizons provide a new time framework for sequence stratigraphic interpretation of the Miocene deposits as well as reconstruction of the basin-evolution model in the southwestern shelf of the Ulleung Basin.

Drought is a global phenomenon with significant negative impacts on water availability, agricultural production, livelihoods, and socioeconomic conditions. Despite its destructive effects, spatially predicting drought hazards remains a challenging task. This study developed an innovative framework by leveraging two state-of-the-art deep learning models: convolutional neural networks (CNNs) and the long short-term memory (LSTM) model. Key predictive factors, including the topographic wetness index, soil depth, mean annual precipitation, elevation, slope, sand content, clay content, and plant-available water-holding capacity (PAWC), were carefully selected for analysis. An agricultural drought inventory map was generated based on the relative departure of soil moisture. The performance of the CNN and LSTM models was evaluated using root mean square error (RMSE), standard deviation (StD), and the area under the receiver operating characteristic curve (AUC). The results indicated that certain parts of the research area were highly susceptible to drought. Both models performed well, achieving AUC values of 81.9% (CNN) and 81.7% (LSTM). The RMSE and StD further confirmed the predictive capabilities of these models. Sensitivity analyses highlighted the importance of PAWC, mean annual precipitation, and clay fraction in detecting drought-prone areas. The drought susceptibility map provides valuable insights into the vulnerability and likelihood of an area experiencing drought conditions, offering essential information for decision-makers to effectively prioritize resources and mitigate drought impacts.

Soil contamination with metalloids such as arsenic (As) and antimony (Sb) and heavy metals such as lead (Pb) in agricultural area surrounding mines affects growth of crops. Because As and Sb are stabilized by iron (Fe) hydroxide and heavy metals are stabilized by phosphate, iron phosphate-coated biochar (IPCB) simultaneously stabilizes metal(loid)s and prevents detrimental effect of metal(loid)s on crops. Therefore, the objective of the study was to evaluate lettuce growth followed by metal stabilization in soil by treating metal contaminated soil with IPCB. The lettuce grown in single and mixed metal(loid)-contaminated soil treated with IPCB showed higher dry biomass, chlorophyll content measured by soil plant analysis development (SPAD) meter, and Fv/Fm values than without IPCB indicating that IPCB mitigated toxic effect of metal(loid)s. The IPCB decreased bioavailable As, Sb, and Pb by 40.8 ± 3.0%, 23.5 ± 0.5%, and 99.0 ± 0.4% in single contaminated soil, which further decreased in mixed metal(loid)-contaminated soil. Arsenic, Sb, and Pb uptake of lettuce shoots decreased by 21.6 ± 9.4%, 19.1 ± 1.1%, and 74.5 ± 17.3% in single contaminated soil, respectively, compared to the control. Arsenic (78.8 ± 5.5% reduction compared to the control) and Pb (80.6 ± 13.4%) uptake as well as Sb (100.0 ± 0.0%) and Pb (12.2 ± 0.7%) uptake further reduced in mixed contaminated soil. In mixed contaminated soil, immobilization of metal(loid)s by IPCB was enhanced because of phosphate substitution by oxyanions reacted with Fe and subsequent immobilization of phosphate with Pb. In addition, increased soil pH by IPCB contributed to stabilization of metal(loid)s. The simultaneous stabilization of metal(loid)s and nutrient supply by IPCB mitigated adverse effects of metal(loid)s on plants and promoted plant growth, thereby remediating metal(loid)-contaminated soil.

Tracing the sources of each contaminant and its geochemical reactions requires a variety of geochemical tools. In this study, chemical compositions and isotopic ratios of O–H, Mo, and Zn were utilized to identify the sources and geochemical reactions of water, As, Mo, and Zn in the seepage from a mine tailings dump. The distinct chemical compositions observed between the seepage and monitoring well, along with the O–H isotopic ratios, suggested that the seepage originated from creek water rather than nearby groundwater, which was supported by a large seasonal variation of δ98Mo in both the seepage and creek. Interpretation results indicated that Mo was predominantly supplied from the creek, while the majority of As and Zn originated from the tailings. During the transport of Mo and Zn, δ98Mo and δ66Zn increased and decreased, respectively, suggesting adsorptive removal, despite the δ66Zn increase during the leaching of the tailings. Notably, the combined use of Mo and Zn isotopic ratios proved to be a valuable tool for identifying geochemical reactions and determining sources and pathways in complex environmental systems. Additionally, although As does not have multiple isotopes, possible adsorption of As onto Fe (oxy)hydroxides could be inferred based on the isotopic behavior of Mo and Zn, as these two isotopes effectively reflected isotopic fractionation during adsorption.

This study reviews 254 papers on artificial intelligence (AI) applications in the geoenergy sector, categorized into conventional and future-oriented technologies. Conventional geoenergy includes reservoir, production, and drilling, while future-oriented technologies cover geological CO2 storage (GCS), gas hydrates (GH), and underground hydrogen storage (UHS). The 254 papers were analyzed systematically based on authorship, publication year, key findings, input-output data relationships, applied AI methods, and sample sizes for AI training. Results highlight the extensive use of machine learning (ML) and deep learning (DL) for tasks such as proxy modeling, dimensionality reduction, data generation, and optimization. Proxy modeling was the most commonly applied method, effectively predicting key economic and engineering parameters. Future-oriented applications require integrating multiple factors for safety and efficiency, emphasizing the need for advanced AI techniques and larger datasets. Conventional studies used larger datasets (average 49,794 samples) compared to future-oriented applications (average 13,779 samples), reflecting their longer research history. The average input-output data sizes (log scale) for ML and DL were 2.01 and 0.34 vs. 4.12 and 2.00, respectively, showing DL’s versatility in handling larger datasets. As geoenergy systems grow more complex, advanced AI methods must address interconnected geo-scales from pore to field levels. Future research should focus on developing automated, user-independent AI systems to enhance model selection and broaden AI applications across diverse datasets, driving greater efficiency and innovation in the geoenergy sector.

Radon is a naturally occurring radioactive gas found in many terrestrial materials, including rocks and soils. Due to the potential health risks linked to persistent exposure to high radon concentrations, it is essential to investigate indoor radon accumulation. This study generated indoor radon index maps for Chungcheongbuk-do, South Korea, selected factors such as lithology, soil depth texture, drainage, material composition, surface texture, soil thickness, calcium oxide and strontium levels, slope, topographic wetness index, wind exposure, valley depth, and the LS factor. These factors were analyzed using frequency ratios (FRs) to assess the influence on indoor radon distribution. The resulting maps were validated with several techniques, including FR, convolutional neural network, long short-term memory, and group method of data handling. The establishment of a geospatial database provided a basis for the integration and analysis of indoor radon levels, along with relevant geological, soil, topographical, and geochemical data. The study calculated the correlations between indoor radon and diverse factors statistically. The indoor radon potential was mapped for Chungcheongbuk-do by applying these techniques, to assess the potential radon distribution. The robustness of the validated model was assessed using the area under the receiver operating curve (AUROC) for both training and testing datasets.

High-grade limestone (HL) is a crucial resource in a wide range of industries. Geophysical surveys are effective for targeting HL deposits because of their distinct chemical and physical characteristics compared to surrounding rock. However, existing studies estimating limestone grades have primarily focused on case studies and literature reviews, often overlooking the insights available from geophysical survey data. In this study, a geophysical survey strategy was optimized by characterizing the geophysical properties of an HL mine in South Korea where contact metamorphism had altered the rock. Rock samples included HL and surrounding lithologies affected by igneous intrusion, comprising dolomite marble, skarn, altered igneous rock, and unaltered igneous rock. Laboratory analyses were conducted to determine the geophysical properties of the rock samples, including the density, porosity, magnetic susceptibility, resistivity, and induced polarization. Optical microscopy, X-ray diffraction, and X-ray fluorescence helped identify the rock lithology. HL exhibited low magnetic susceptibility, chargeability, and percent frequency effect compared to other lithologies. Consequently, magnetic and induced polarization surveys were proposed as effective geophysical techniques for detecting this type of limestone deposit. The proposed optimized geophysical survey approach enables more efficient identification and delineation of HL deposits, improving resource exploration accuracy and reducing time and costs in locating valuable deposits.

To develop an optimal roasting additive for vanadium extraction, the mixing ratio of sodium carbonate (Na2CO3) and sodium sulfate (Na2SO4) was varied at a fixed temperature of 1373 K. The effect of Na2CO3 concentration on the leaching efficiency of vanadium was studied at a fixed concentration of Na2SO4. The leaching efficiency of vanadium increased when 10 wt pct Na2CO3 was added. However, as the Na2CO3 concentration increased beyond 20 wt pct, the leaching efficiency of vanadium decreased. The effect of Na2CO3 on the leaching efficiency of vanadium was evaluated via X-ray diffraction analysis. As the Na2CO3 concentration increased, the formation of a slag phase prevented the reaction between the SO2 or SO3 gas and vanadium in the vanadium titanomagnetite ore. Na2SO4 did not play a role in vanadium extraction when > 20 wt pct Na2CO3 was added. The leaching efficiencies of other impurities (Al, Si, and Na) were also investigated under various mixing ratios of Na2CO3 and Na2SO4. Compared with the case where only the roasting additive was used, higher leaching efficiencies of Al, Si, and Na were observed. Through thermodynamic considerations, the formation of the dominant phases for Al, Si, and Na leaching was investigated. In this study, the optimal mixing ratio of the roasting additive in the V extraction process was determined to be 10 wt pct Na2CO3 and 5 wt pct Na2SO4 at a roasting temperature of 1373 K.