Korea Institute of Geoscience and Mineral Resources

The aim of this study is to evaluate the hazard of landslides at Penang, Malaysia, using a Geographical Information System (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from Thematic Mapper (TM) satellite images; and the vegetation index value from Système Probatoire de l'Observation de la Terre (SPOT) satellite images. Landslide hazardous areas were analysed and mapped using the landslide‐occurrence factors by logistic regression model. The results of the analysis were verified using the landslide location data and compared with probabilistic model. The validation results showed that the logistic regression model is better in prediction than probabilistic model.

The last 60 years has seen unprecedented groundwater extraction and overdraft as well as development of new technologies for water treatment that together drive the advance in intentional groundwater replenishment known as managed aquifer recharge (MAR). This paper is the first known attempt to quantify the volume of MAR at global scale, and to illustrate the advancement of all the major types of MAR and relate these to research and regulatory advancements. Faced with changing climate and rising intensity of climate extremes, MAR is an increasingly important water management strategy, alongside demand management, to maintain, enhance and secure stressed groundwater systems and to protect and improve water quality. During this time, scientific research—on hydraulic design of facilities, tracer studies, managing clogging, recovery efficiency and water quality changes in aquifers—has underpinned practical improvements in MAR and has had broader benefits in hydrogeology. Recharge wells have greatly accelerated recharge, particularly in urban areas and for mine water management. In recent years, research into governance, operating practices, reliability, economics, risk assessment and public acceptance of MAR has been undertaken. Since the 1960s, implementation of MAR has accelerated at a rate of 5%/year, but is not keeping pace with increasing groundwater extraction. Currently, MAR has reached an estimated 10 km3/year, ~2.4% of groundwater extraction in countries reporting MAR (or ~1.0% of global groundwater extraction). MAR is likely to exceed 10% of global extraction, based on experience where MAR is more advanced, to sustain quantity, reliability and quality of water supplies.

The concept of feedback is key in assessing whether a perturbation to a system is amplified or damped by mechanisms internal to the system. In polar regions, climate dynamics are controlled by both radiative and non-radiative interactions between the atmosphere, ocean, sea ice, ice sheets and land surfaces. Precisely quantifying polar feedbacks is required for a process-oriented evaluation of climate models, a clear understanding of the processes responsible for polar climate changes, and a reduction in uncertainty associated with model projections. This quantification can be performed using a simple and consistent approach that is valid for a wide range of feedbacks, offering the opportunity for more systematic feedback analyses and a better understanding of polar climate changes.

Graphene is considered a promising ultracapacitor material toward high power and energy density because of its high conductivity and high surface area without pore tortuosity. However, the two-dimensional (2D) sheets tend to aggregate during the electrode fabrication process and align perpendicular to the flow direction of electrons and ions, which can reduce the available surface area and limit the electron and ion transport. This makes it hard to achieve scalable device performance as the loading level of the active material increases. Here, we report a strategy to solve these problems by transforming the 2D graphene sheet into a crumpled paper ball structure. Compared to flat or wrinkled sheets, the crumpled graphene balls can deliver much higher specific capacitance and better rate performance. More importantly, devices made with crumpled graphene balls are significantly less dependent on the electrode mass loading. Performance of graphene-based ultracapacitors can be further enhanced by using flat graphene sheets as the binder for the crumpled graphene balls, thus eliminating the need for less active binder materials.

Unlike flat sheets, crumpled paper balls have both high free volume and high compressive strength, and can tightly pack without significantly reducing the area of accessible surface. Such properties would be highly desirable for sheet-like materials such as graphene, since they tend to aggregate in solution and restack in the solid state, making their properties highly dependent on the material processing history. Here we report the synthesis of crumpled graphene balls by capillary compression in rapidly evaporating aerosol droplets. The crumpled particles are stabilized by locally folded, π-π stacked ridges as a result of plastic deformation, and do not unfold or collapse during common processing steps. In addition, they are remarkably aggregation-resistant in either solution or solid state, and remain largely intact and redispersible after chemical treatments, wet processing, annealing, and even pelletizing at high pressure. For example, upon compression at 55 MPa, the regular flat graphene sheets turn into nondispersible chunks with drastically reduced surface area by 84%, while the crumpled graphene particles can still maintain 45% of their original surface area and remain readily dispersible in common solvents. Therefore, crumpled particles could help to standardize graphene-based materials by delivering more stable properties such as high surface area and solution processability regardless of material processing history. This should greatly benefit applications using bulk quantities of graphene, such as in energy storage or conversion devices. As a proof of concept, we demonstrate that microbial fuel electrodes modified by the crumpled particles indeed outperform those modified with their flat counterparts.

Large amounts of CH4 in the form of solid hydrates are stored on continental margins and in permafrost regions. If these CH4 hydrates could be converted into CO2 hydrates, they would serve double duty as CH4 sources and CO2 storage sites. We explore here the swapping phenomenon occurring in structure I (sI) and structure II (sII) CH4 hydrate deposits through spectroscopic analyses and its potential application to CO2 sequestration at the preliminary phase. The present 85% CH4 recovery rate in sI CH4 hydrate achieved by the direct use of binary N2+CO2 guests is surprising when compared with the rate of 64% for a pure CO2 guest attained in the previous approach. The direct use of a mixture of N2+CO2 eliminates the requirement of a CO2 separation/purification process. In addition, the simultaneously occurring dual mechanism of CO2 sequestration and CH4 recovery is expected to provide the physicochemical background required for developing a promising large-scale approach with economic feasibility. In the case of sII CH4 hydrates, we observe a spontaneous structure transition of sII to sI during the replacement and a cage-specific distribution of guest molecules. A significant change of the lattice dimension caused by structure transformation induces a relative number of small cage sites to reduce, resulting in the considerable increase of CH4 recovery rate. The mutually interactive pattern of targeted guest-cage conjugates possesses important implications for the diverse hydrate-based inclusion phenomena as illustrated in the swapping process between CO2 stream and complex CH4 hydrate structure.

Submicrometer-sized capsules made of Si nanoparticles wrapped by crumpled graphene shells were made by a rapid, one-step capillary-driven assembly route in aerosol droplets. Aqueous dispersion of micrometer-sized graphene oxide (GO) sheets and Si nanoparticles were nebulized to form aerosol droplets, which were passed through a preheated tube furnace. Evaporation-induced capillary force wrapped graphene (a.k.a., reduced GO) sheets around the Si particles, and heavily crumpled the shell. The folds and wrinkles in the crumpled graphene coating can accommodate the volume expansion of Si upon lithiation without fracture, and thus help to protect Si nanoparticles from excessive deposition of the insulating solid electrolyte interphase. Compared to the native Si particles, the composite capsules have greatly improved performance as Li ion battery anodes in terms of capacity, cycling stability, and Coulombic efficiency.

Microbial life inhabits deeply buried marine sediments, but the extent of this vast ecosystem remains poorly constrained. Here we provide evidence for the existence of microbial communities in ~40° to 60°C sediment associated with lignite coal beds at ~1.5 to 2.5 km below the seafloor in the Pacific Ocean off Japan. Microbial methanogenesis was indicated by the isotopic compositions of methane and carbon dioxide, biomarkers, cultivation data, and gas compositions. Concentrations of indigenous microbial cells below 1.5 km ranged from <10 to ~10(4) cells cm(-3). Peak concentrations occurred in lignite layers, where communities differed markedly from shallower subseafloor communities and instead resembled organotrophic communities in forest soils. This suggests that terrigenous sediments retain indigenous community members tens of millions of years after burial in the seabed.

A prestack reverse time‐migration image is not properly scaled with increasing depth. The main reason for the image being unscaled is the geometric spreading of the wavefield arising during the back‐propagation of the measured data and the generation of the forward‐modelled wavefields. This unscaled image can be enhanced by multiplying the inverse of the approximate Hessian appearing in the Gauss–Newton optimization technique. However, since the approximate Hessian is usually too expensive to compute for the general geological model, it can be used only for the simple background velocity model.We show that the pseudo‐Hessian matrix can be used as a substitute for the approximate Hessian to enhance the faint images appearing at a later time in the 2D prestack reverse time‐migration sections. We can construct the pseudo‐Hessian matrix using the forward‐modelled wavefields (which are used as virtual sources in the reverse time migration), by exploiting the uncorrelated structure of the forward‐modelled wavefields and the impulse response function for the estimated diagonal of the approximate Hessian. Although it is also impossible to calculate directly the inverse of the pseudo‐Hessian, when using the reciprocal of the pseudo‐Hessian we can easily obtain the inverse of the pseudo‐Hessian. As examples supporting our assertion, we present the results obtained by applying our method to 2D synthetic and real data collected on the Korean continental shelf.

The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses. In this study, we applied two novel deep learning algorithms, the recurrent neural network (RNN) and convolutional neural network (CNN), for national-scale landslide susceptibility mapping of Iran. We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors (altitude, slope degree, profile curvature, distance to river, aspect, plan curvature, distance to road, distance to fault, rainfall, geology and land-sue) to construct a geospatial database and divided the data into the training and the testing dataset. We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset. We calculated the receiver operating characteristic (ROC) curve and used the area under the curve (AUC) for the quantitative evaluation of the landslide susceptibility maps using the testing dataset. Better performance in both the training and testing phases was provided by the RNN algorithm (AUC ​= ​0.88) than by the CNN algorithm (AUC ​= ​0.85). Finally, we calculated areas of susceptibility for each province and found that 6% and 14% of the land area of Iran is very highly and highly susceptible to future landslide events, respectively, with the highest susceptibility in Chaharmahal and Bakhtiari Province (33.8%). About 31% of cities of Iran are located in areas with high and very high landslide susceptibility. The results of the present study will be useful for the development of landslide hazard mitigation strategies.

Since flood frequency increases with the impact of climate change, the damage that is emphasized on flood-risk maps is based on actual flooded area data; therefore, flood-susceptibility maps for the Seoul metropolitan area, for which random-forest and boosted-tree models are used in a geographic information system (GIS) environment, are created for this study. For the flood-susceptibility mapping, flooded-area, topography, geology, soil and land-use datasets were collected and entered into spatial datasets. From the spatial datasets, 12 factors were calculated and extracted as the input data for the models. The flooded area of 2010 was used to train the model, and the flooded area of 2011 was used for the validation. The importance of the factors of the flood-susceptibility maps was calculated and lastly, the maps were validated. As a result, the distance from the river, geology and digital elevation model showed a high importance among the factors. The random-forest model showed validation accuracies of 78.78% and 79.18% for the regression and classification algorithms, respectively, and boosted-tree model showed validation accuracies of 77.55% and 77.26% for the regression and classification algorithms, respectively. The flood-susceptibility maps provide meaningful information for decision-makers regarding the identification of priority areas for flood-mitigation management.

The 15 January 2022 climactic eruption of Hunga volcano, Tonga, produced an explosion in the atmosphere of a size that has not been documented in the modern geophysical record. The event generated a broad range of atmospheric waves observed globally by various ground-based and spaceborne instrumentation networks. Most prominent was the surface-guided Lamb wave (≲0.01 hertz), which we observed propagating for four (plus three antipodal) passages around Earth over 6 days. As measured by the Lamb wave amplitudes, the climactic Hunga explosion was comparable in size to that of the 1883 Krakatau eruption. The Hunga eruption produced remarkable globally detected infrasound (0.01 to 20 hertz), long-range (~10,000 kilometers) audible sound, and ionospheric perturbations. Seismometers worldwide recorded pure seismic and air-to-ground coupled waves. Air-to-sea coupling likely contributed to fast-arriving tsunamis. Here, we highlight exceptional observations of the atmospheric waves.

The sol-gel technique has many advantages over the other mechanism for synthesizing metal oxide nanoparticles such as being simple, cheap and having low temperature and pressure. Utilization of waste materials as a precursor for synthesis makes the whole process cheaper, green and sustainable. Calcium Oxide nanoparticles have been synthesized from eggshell through the sol-gel method. Raw eggshell was dissolved by HCl to form CaCl2 solution, adding NaOH to the solution dropwise to agitate Ca (OH)2 gel and finally drying the gel at 900 °C for 1 h. The synthesized nanoparticle was characterized by scanning electron microscope (SEM), Fourier-transform infrared spectroscopy (FTIR), X-Ray fluorescence (XRF) and X-ray diffraction (XRD). The FTIR and XRD results have clearly depicted the synthesis of calcium oxide from eggshell, which is mainly composed of calcium carbonate. The FE-SEM images of calcium oxide nanoparticles showed that the particles were almost spherical in morphology. The particle size of the nanoparticles was in the range 50 nm–198 nm. Therefore, waste eggshell can be considered as a promising resource of calcium for application of versatile fields.

Stabilization/solidification (S/S) is a low-cost and high-efficiency remediation method for contaminated soils, however, conventional cement-based S/S method has environmental constraints and sustainability concerns. This study proposes a low-carbon, cement-free, clay-based approach for simultaneous S/S of As and Pb in the contaminated soil, and accordingly elucidates the chemical interactions between alkali-activated clay binders and potentially toxic elements. Quantitative X-ray diffraction and 27Al nuclear magnetic resonance analyses indicated that the addition of lime effectively activated the hydration of kaolinite clay, and the presence of limestone further enhanced the polymerization of hydrates. X-ray photoelectron spectroscopy showed that approximately 19% of As[III] was oxidized to As[V] in the alkali-activated clay system, which reduced toxicity and facilitated immobilization of As. During the cement-free S/S process, As and Pb consumed Ca(OH)2 and precipitated as Ca3(AsO4)2·4H2O and Pb3(NO3)(OH)5, respectively, accounting for the low leachability of As (7.0%) and Pb (5.4%). However, the reduced amount of Ca(OH)2 decreased the degree of hydration of clay minerals, and the pH buffering capacity of the contaminated soil hindered the pH increase. Sufficient dosage of lime was required for ensuring satisfactory solidification and contaminant immobilization of the clay-based S/S products. The leachability of As and Pb in high-Ca S/S treated soil samples was reduced by 96.2% and 98.8%, respectively. This is the first study developing a green and cement-free S/S of As- and Pb-contaminated soil using clay minerals as an environmentally compatible binding material.

The warmest global temperatures of the past 85 million years occurred during a prolonged greenhouse episode known as the Early Eocene Climatic Optimum (52-50 Ma). The Early Eocene Climatic Optimum terminated with a long-term cooling trend that culminated in continental-scale glaciation of Antarctica from 34 Ma onward. Whereas early studies attributed the Eocene transition from greenhouse to icehouse climates to the tectonic opening of Southern Ocean gateways, more recent investigations invoked a dominant role of declining atmospheric greenhouse gas concentrations (e.g., CO2). However, the scarcity of field data has prevented empirical evaluation of these hypotheses. We present marine microfossil and organic geochemical records spanning the early-to-middle Eocene transition from the Wilkes Land Margin, East Antarctica. Dinoflagellate biogeography and sea surface temperature paleothermometry reveal that the earliest throughflow of a westbound Antarctic Counter Current began ~49-50 Ma through a southern opening of the Tasmanian Gateway. This early opening occurs in conjunction with the simultaneous onset of regional surface water and continental cooling (2-4 °C), evidenced by biomarker- and pollen-based paleothermometry. We interpret that the westbound flowing current flow across the Tasmanian Gateway resulted in cooling of Antarctic surface waters and coasts, which was conveyed to global intermediate waters through invigorated deep convection in southern high latitudes. Although atmospheric CO2 forcing alone would provide a more uniform middle Eocene cooling, the opening of the Tasmanian Gateway better explains Southern Ocean surface water and global deep ocean cooling in the apparent absence of (sub-) equatorial cooling.

Landslides susceptibility maps were constructed in the Pyeong-Chang area, Korea, using the Random Forest and Boosted Tree models. Landslide locations were randomly selected in a 50/50 ratio for training and validation of the models. Seventeen landslide-related factors were extracted and constructed in a spatial database. The relationships between the observed landslide locations and these factors were identified by using the two models. The models were used to generate a landslide susceptibility map and the importance of the factors was calculated. Finally, the landslide susceptibility maps were validated. Finally, landslide susceptibility maps were generated. For the Random Forest model, the validation accuracy in regression and classification algorithms showed 79.34 and 79.18%, respectively, and for the Boosted Tree model, these were 84.87 and 85.98%, respectively. The two models showed satisfactory accuracies, and the Boosted Tree model showed better results than the Random Forest model.

Recognizing the importance of methane hydrate research and the need for a coordinated effort, the United States Congress enacted the Methane Hydrate Research and Development Act of 2000. At the same time, the Ministry of International Trade and Industry in Japan launched a research program to develop plans for a methane hydrate exploratory drilling project in the Nankai Trough. India, China, the Republic of Korea, and other nations also have established large methane hydrate research and development programs. Government-funded scientific research drilling expeditions and production test studies have provided a wealth of information on the occurrence of methane hydrates in nature. Numerous studies have shown that the amount of gas stored as methane hydrates in the world may exceed the volume of known organic carbon sources. However, methane hydrates represent both a scientific and technical challenge, and much remains to be learned about their characteristics and occurrence in nature. Methane hydrate research in recent years has mostly focused on: (1) documenting the geologic parameters that control the occurrence and stability of methane hydrates in nature, (2) assessing the volume of natural gas stored within various methane hydrate accumulations, (3) analyzing the production response and characteristics of methane hydrates, (4) identifying and predicting natural and induced environmental and climate impacts of natural methane hydrates, (5) analyzing the methane hydrate role as a geohazard, (6) establishing the means to detect and characterize methane hydrate accumulations using geologic and geophysical data, and (7) establishing the thermodynamic phase equilibrium properties of methane hydrates as a function of temperature, pressure, and gas composition. The U.S. Department of Energy (DOE) and the Consortium for Ocean Leadership (COL) combined their efforts in 2012 to assess the contributions that scientific drilling has made and could continue to make to advance our understanding of methane hydrates in nature. COL assembled a Methane Hydrate Project Science Team with members from academia, industry, and government. This Science Team worked with COL and DOE to develop and host the Methane Hydrate Community Workshop, which surveyed a substantial cross section of the methane hydrate research community for input on the most important research developments in our understanding of methane hydrates in nature and their potential role as an energy resource, a geohazard, and/or as an agent of global climate change. Our understanding of how methane hydrates occur in nature is still growing and evolving, and it is known with certainty that field, laboratory, and modeling studies have contributed greatly to our understanding of hydrates in nature and will continue to be a critical source of the information needed to advance our understanding of methane hydrates.

Synthetic methods produce libraries of colloidal nanocrystals with tunable physical properties by tailoring the nanocrystal size, shape, and composition. Here, we exploit colloidal nanocrystal diversity and design the materials, interfaces, and processes to construct all-nanocrystal electronic devices using solution-based processes. Metallic silver and semiconducting cadmium selenide nanocrystals are deposited to form high-conductivity and high-mobility thin-film electrodes and channel layers of field-effect transistors. Insulating aluminum oxide nanocrystals are assembled layer by layer with polyelectrolytes to form high-dielectric constant gate insulator layers for low-voltage device operation. Metallic indium nanocrystals are codispersed with silver nanocrystals to integrate an indium supply in the deposited electrodes that serves to passivate and dope the cadmium selenide nanocrystal channel layer. We fabricate all-nanocrystal field-effect transistors on flexible plastics with electron mobilities of 21.7 square centimeters per volt-second.

Abstract The purpose of this study is to develop landslide susceptibility analysis techniques using an artificial neural network and to apply the newly developed techniques to the study area of Yongin in Korea. Landslide locations were identified in the study area from interpretation of aerial photographs, field survey data, and a spatial database of the topography, soil type and timber cover. The landslide‐related factors (slope, curvature, soil texture, soil drainage, soil effective thickness, timber age, and timber diameter) were extracted from the spatial database. Using those factors, landslide susceptibility was analysed by artificial neural network methods. The landslide susceptibility index was calculated by the back‐propagation method, which is a type of artificial neural network method, and the susceptibility map was made with a geographic information system (GIS) program. The results of the landslide susceptibility analysis were verified using landslide location data. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide location. A GIS was used to efficiently analyse the vast amount of data, and an artificial neural network to be an effective tool to maintain precision and accuracy. The results can be used to reduce hazards associated with landslides and to plan land use and construction. Copyright © 2003 John Wiley &amp; Sons, Ltd.

Floods are some of the most destructive and catastrophic disasters worldwide. Development of management plans needs a deep understanding of the likelihood and magnitude of future flood events. The purpose of this research was to estimate flash flood susceptibility in the Tafresh watershed, Iran, using five machine learning methods, i.e., alternating decision tree (ADT), functional tree (FT), kernel logistic regression (KLR), multilayer perceptron (MLP), and quadratic discriminant analysis (QDA). A geospatial database including 320 historical flood events was constructed and eight geo-environmental variables—elevation, slope, slope aspect, distance from rivers, average annual rainfall, land use, soil type, and lithology—were used as flood influencing factors. Based on a variety of performance metrics, it is revealed that the ADT method was dominant over the other methods. The FT method was ranked as the second-best method, followed by the KLR, MLP, and QDA. Given a few differences between the goodness-of-fit and prediction success of the methods, we concluded that all these five machine-learning-based models are applicable for flood susceptibility mapping in other areas to protect societies from devastating floods.