• The Journal of Engineering Geology
• /
• v.33 no.1
• /
• pp.39-49
• /
• 2023

Soil contaminants measured and managed in Korea include those of geologic origin such as arsenic, cadmium, copper, lead, zinc, nickel, mercury, and fluoride. This study identifies the distribution of these contaminants using GIS modeling to analyze the spatial probability of soil contamination originating from geology. The modeling found that cadmium, copper, lead, nickel, and mercury often exceed the regulated standard by <1%. Concentrations of arsenic and zinc greatly exceeded the standard in the vicinity of mines and industrial complexes: mining and industry seemed to have substantial effects on the concentrations of these metals. Although fluoride was sampled at the lowest number of points, its frequency of exceeding the standard was the highest. No obvious source of artificial contamination has been identified, and fluoride's distribution characteristics showed continuity over a wide area, suggesting a strong correlation between geological characteristics and fluoride concentration. The highest frequencies of fluoride exceeding the standard were in Jurassic granite (40.00%) and Precambrian banded gneiss (34.12%). As these rocks contributed to the formation of soil through their weathering, high fluoride concentrations can be expected in soil in areas where these rocks are distributed.

• Journal of the Korean Institute of Gas
• /
• v.27 no.2
• /
• pp.39-48
• /
• 2023

This study presents the experimental results to measure the adsorption amount of methane gas by coal according to the conditions of a coalbed methane (CBM) reservoir. Adsorbed gas to coal seam particles was measured under reservoir conditions (normal pressure ~ 1,200 psi pressure range, temperature range15 ~ 45℃) using coal samples obtained from random mines in Kalimantan Island, North Indonesia. The obtained amount of absolute adsorbed gas was applied to triangular with linear interpolation to calculate the maximum amount of adsorbed gas according to temperature and pressure change, at which no experiment was performed. As a result, it was revealed that the amount of adsorbed gas to coal particles increased as the pressure increased and temperature decreased, but the increase of the amount of adsorbed gas decreased at more than an appropriate depth(1,000 ft). In the cleat permeability and cleat porosity for each depth of the coal bed considering the effective stress, the cleat permeability was 28.86 ~ 46.81 md, and the cleat porosity was 0.83 ~ 0.98%. This means that the gas productivity varies significantly with the depth because the reduction of the permeability according to the depth in the coal seam is significant. Therefore, a coalbed depth should be considered essential when designing the spacing of production wells in a coalbed methane reservoir in further study.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.27 no.2
• /
• pp.34-44
• /
• 2024

Recently, due to abnormal climate caused by climate change, natural disasters such as floods, landslides, and soil outflows are rapidly increasing. In Korea, more than 63% of the land is vulnerable to slope disasters due to the geographical characteristics of mountainous areas, and in particular, Quarry mines soil and rocks, so there is a high risk of landslides not only inside the workplace but also outside.Accordingly, this study built a DEM using UAV and aviation LiDAR for monitoring the quarry, conducted a time series change analysis, and proposed an optimal DEM construction method for monitoring the soil collection site. For DEM construction, UAV and LiDAR-based Point Cloud were built, and the ground was extracted using three algorithms: Aggressive Classification (AC), Conservative Classification (CC), and Standard Classification (SC). UAV and LiDAR-based DEM constructed according to the algorithm evaluated accuracy through comparison with digital map-based DEM.

• Journal of Internet Computing and Services
• /
• v.15 no.3
• /
• pp.101-107
• /
• 2014

With the development of online service, recent forms of databases have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment of marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artificial intelligence. Landmark window-based frequent pattern mining, one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, instead of all the data. In this paper, we analyze and evaluate the techniques of the well-known landmark window-based frequent pattern mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns as soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reason, such algorithms are also called online mining approaches. We evaluate and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms' total runtime and average processing time per transaction. In addition, to compare the efficiency of storage structures between them, their maximum memory usage is also evaluated. Lastly, we show how stably the two algorithms conduct their mining works with respect to the databases that feature gradually increasing items. With respect to the evaluation results of mining time and transaction processing, hMiner has higher speed than that of Lossy counting. Since hMiner stores candidate frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a lattice manner; thus, it has to search for multiple nodes in order to access the candidate frequent patterns. On the other hand, hMiner shows worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash's buckets, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner presents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is increased, shared items are decreased in contrast; thereby, Lossy counting's memory efficiency is weakened. Furthermore, if the number of transactions becomes higher, its pruning effect becomes worse. From the experimental results, we can determine that the landmark window-based frequent pattern mining algorithms are suitable for real-time systems although they require a significant amount of memory. Hence, we need to improve their data structures more efficiently in order to utilize them additionally in resource-constrained environments such as WSN(Wireless sensor network).

• Economic and Environmental Geology
• /
• v.48 no.4
• /
• pp.337-349
• /
• 2015

This study has focused on investigation of correlation for As and heavy metals in paddy soil and rice crops sampled in the vicinity of the abandoned Munmyung Au-Ag mine. Soil samples extracted by various methods including aqua regia, 1 M $MgCl_2$, 0.01 M $CaCl_2$ and 0.05 M EDTA were analyzed for As and heavy metals (Cd, Cu, Pb and Zn). Rice grain samples grown on the soils were also analyzed for the same elements to evaluate the relationships between soils and rice crops. According to soil extraction methods, As and heavy metal contents in the soils were decreased in the order of aqua regia > 0.01 M $CaCl_2$ > 1 M $MgCl_2$ > 0.05 M EDTA. In addition to correlation analysis, statistically significant correlation with the four extraction methods (p<0.01) were found in the soil and rice samples. As calculation of biological accumulation coefficients (BACs) of the rice crops for As and heavy metals, the BACs for Cd, Zn and Cu were relatively higher than those for As and Pb. This study also carried out a stepwise multiple linear regression analysis to identify the dominant factors influencing metal extraction rates of the paddy soils. Furthermore, daily intakes of As and heavy metals from regularly consumed the rice grain (287 g/day) grown on the contaminated soils by the mining activities were estimated, and found that Cd and As intakes from the rice reached up to 73.7% and 51.8% for maximum allowance levels of trace elements suggested by WHO, respectively. Therefore, long-term consumption of the rice poses potential health problems to residents around the mine, although no adverse health effects have yet been observed.

• Journal of Intelligence and Information Systems
• /
• v.23 no.2
• /
• pp.39-70
• /
• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.

• Journal of the Korean Society of Groundwater Environment
• /
• v.4 no.3
• /
• pp.130-143
• /
• 1997

To investigate geochemical characteristics and the sources of the dissolved ion species in the river water in the Han river drainage basin, samples were collected at 60 sites from the Han river drainage basin. The data for. pH, conductivity, TDS (total dissolved solid), temperature, and concentrations of dissloved ions were obtained as follows : (1) The geochemical characteristics of the surface water in the South and North Han river drainage basins are mainly controlled by bed rock geology in the drainage basin and in the main stream of the Han river considerably affected by anthropogenic pollution. The South Han river water samples have high concentrations of $Ca^{2＋}$ (ave. 15.42 ppm), $Mg^{2＋}$ (ave. 2.74 ppm), HC $O_3$$^{[-10]}$ (ave. 51.9 ppm), which evidently indicates that the bed rock geology in a limestone area mainly controls the surface water chemistry. The concentration of S $O_4$$^{2-}$ is remarkably high (SHR10-2 : 129.9 ppm) because of acid mine drainage from the metal and coal mines in the upper reaches of the South Han river. (2) The South Han river and the North Han river join the Han river. in the Yangsuri, Kyounggido and flow through Seoul metropolitan city. The mixing ratio is about 60:40 at the meeting point (sample number HRl0). (3) The result of factor analysis suggests that the pollution factor accounts for about 79% and the bed rock type factor accounts for about 7% of the data variation. This means that the geochemical characteristics of the Han river water mainly controlled by anthropogenic pollution in the South Han river and main stream of the Han river drainage basin. (4) The chemical data for four tributaries such as the Wangsukcheon, the Tancheon, the Zunuangcheon, and the Anyangcheon show that the concentration of pollution elements such as N $O_2$, C $l^{－}$, P $O_4$$^{3-}$, S $O_4$$^{2-}$ and Mn are high due to municipal waste disposal.

• Economic and Environmental Geology
• /
• v.34 no.5
• /
• pp.423-435
• /
• 2001

The gold-silver vein deposits in the Mugeug mineralized area are emplaced in late Cretaceous biotite granite associated with the pull-apart type Cretaceous Eumseong basin. Mugeug mine in northern part is composed of multiple veins showing relatively high gold fineness and is characterized by sericitization, chloritization and epidotization. The ore-forming fluids were evolved by dilution and cooling mechanisms at relatively high temperature and salinity (=30$0^{\circ}C$,1~9 equiv. wt. % NaCl) and highly-evolved meteoric water ($\delta$$^{18}$ O;-1.2~3.7$\textperthousand$) and gold mineralization associated with sulfides tormed at temperatures between 260 and 22$0^{\circ}C$ and within sulfur fugacity range of 10$^{-11.5}$ ~ 10$^{-13.5}$ atm. In contrast, Geumwang, Geumbong and Taegueg mines show the low fineness values, in southern part are characterized by increasing tendency of simple and/or stockwork veins and by kaolinitization, silicificatitan, carbonatization and smectitization. These droposits formed at relatively low temperature and salinity (<23$0^{\circ}C$, <3 equiv. wt. % NaCl) from ore-forming fluids containing greater amounts of less-evolved meteoric waters ($\delta$$^{18}$ O;-5.5~4.0$\textperthousand$), and silver mineralization representing various gold-and/or silver-bearing minerals formed at temperatures between 200 and 15$0^{\circ}C$ and from sulfur fugacity range of 10$^{-15}$ ~10$^{-18}$ atm These results imply that mineralization in the Mugueg area formed at shallow-crustal level and categorize these deposits as low-sulfidation epithermal type. The genetic differences between the northern and southern parts reflect the evolution of the hydrothermal system due to a different physicochemical environment from heat source area (Mugeug mine) to marginal area (Taegeum mine) in a geothermal field.

• Economic and Environmental Geology
• /
• v.37 no.1
• /
• pp.73-86
• /
• 2004

In order to estimate the post-ingestion bioavailability of heavy metals and to assess the risk of adverse health effects on human exposure to toxic heavy metals, environmental geochemical surveys were undertaken around the Dogok Au-Ag-Cu and the Hwacheon Au-Ag-Pb-Zn mine sites. Human risk assessment of toxic heavy metals was performed with the results of the SBET(simple bioavailability extraction test) analysis for soil and chemical analytical data for crop plant and water. Arsenic and other heavy metals were highly elevated in tailings from the Dogok(218 As mg/kg, 90.2 Cd mg/kg, 3,053 Cu mg/kg, 9,473 Pb mg/kg, 14,500 Zn mg/kg) and the Hwacheon(72 As mg/kg, 12.4 Cd mg/kg. 578 Pb mg/kg, 1,304 Zn mg/kg) mines. These significant concentrations can impact on soils and waters around the tailing dumps. The quantities of As, Cd and Zn extracted from paddy soils in the Hwacheon mine using the SBET analysis were 55.4%, 20.8% and 26.4% bioavailability, respectively, and for farmland soils in the Dogok mine, 40.8%, 37.6% and 33.0% bioavailability, respectively. From the results of human risk assessment, HI(Hazard Index) value exceeded 1.0 for As in the Hwacheon mine and for Cd in the Dogok mine. Thus, toxic risks for As and Cd exist via exposure(ingestion) of contaminated soil, water and rice grain in these mine sites. The cancer risk for As by the consumption of rice and groundwater in the Hwacheon mine area was 8E-4 and 1E-4, respectively. This risk level exceeds the acceptable risk(1 in 100,000) for regulatory purpose. Therefore, regular ingestion of locally grown rice and ground-water by the local population can pose a potential health threat due to long-term arsenic exposure.

• Economic and Environmental Geology
• /
• v.38 no.6 s.175
• /
• pp.663-673
• /
• 2005

This study investigates the relationship between the geochemical maps and the gold-silver deposit locations. Geochemical maps of 21 elements, which are published by KIGAM, locations of gold-silver deposits, and 1:1,000,000 scale geological map of Korea are utilized far this investigation. Pixel size of the basic geochemical maps is 250m and these data are resampled in 1km spacing for the statistical analyses. Relationship between the mine location and the geochemical data are investigated using bayesian statistics and decision tree algorithms. For the bayesian statistics, each geochemical maps are reclassified by percentile divisions which divides the data by 5, 25, 50, 75, 95, and $100\%$ data groups. Number of mine locations in these divisions are counted and the probabilities are calculated. Posterior probabilities of each pixel are calculated using the probability of 21 geochemical maps and the geological map. A prediction map of the mining locations is made by plotting the posterior probability. The input parameters for the decision tree construction are 21 geochemical elements and lithology, and the output parameters are 5 types of mines (Ag/Au, Cu, Fe, Pb/Zn, W) and absence of the mine. The locations for the absence of the mine are selected by resampling the overall area by 1 km spacing and eliminating my resampled points, which is in 750m distance from mine locations. A prediction map of each mine area is produced by applying the decision tree to every pixels. The prediction by Bayesian method is slightly better than the decision tree. However both prediction maps show reasonable match with the input mine locations. We interpret that such match indicate the rules produced by both methods are reasonable and therefore the geochemical data has strong relations with the mine locations. This implies that the geochemical rules could be used as background values oi mine locations, therefore could be used for evaluation of mine contamination. Bayesian statistics indicated that the probability of Au/Ag deposit increases as CaO, Cu, MgO, MnO, Pb and Li increases, and Zr decreases.