• Korean Journal of Mineralogy and Petrology
• /
• v.36 no.1
• /
• pp.55-72
• /
• 2023

This study aims to identify the fault zone architecture and geometric and kinematic characteristics of the Yeongdeok Fault, based on the geometry and kinematic data of various structural elements obtained by detailed field survey and anisotropy of magnetic susceptibility (AMS) of the fault rocks. The Yeongdeok Fault extends from Opo-ri, Ganggu-myeon, Yeongdeok-gun to Gilgok-ri, Maehwa-myeon and Bangyul-ri, Giseong-myeon, Uljin-gun, and cuts various rock types from the Paleo-proterozoic to the Mesozoic with a range of 4.6-5.0 km (4.77 km in average) of right-lateral offset or forms the rock boundaries. The fault is divided into four segments based on its geometric features and shows N-S to NNW strikes and dips of an angle of ≥ 54° to the east at most outcrops, even though the outcrops showing the westward dipping (a range of 54°-82°) of fault surface increase as it goes north. The Yeongdeok Fault shows the difference in the fault zone architecture and in the fault core width ranging from 0.3 to 15 m depending on the bedrock type, which is interpreted as due to differences in the physical properties of bedrock such as ductility, mineral composition, particle size, and anisotropy. Combining the results of paleostress reconstruction and AMS in this and previous studies, the Yeongdeok Fault experienced (1) sinistral strike-slip under NW-SE maximum horizontal principle stress (σ_Hmax) and NE-SW minimum horizontal principle stress (σ_Hmin) in the late Cretaceous to early Cenozoic, and then (2) dextral strike-slip under NE-SW maximum horizontal principle stress (σ_Hmax) and NW-SE minimum horizontal principle stress (σ_Hmin) in the Paleogene. It is interpreted that the deformation caused by the Paleogene dextral strike-slip movement was the most dominant, and the crustal deformation was insignificant thereafter.

• Journal of Wetlands Research
• /
• v.25 no.4
• /
• pp.306-314
• /
• 2023

Stream sediments are an important component of water quality management because they are receptors of various pollutants such as heavy metals and organic matters emitted from upland sources and can be secondary pollution sources, adversely affecting water environment. To effectively manage the stream sediments, identification of primary sources of sediment contamination and source-associated control strategies will be required. We evaluated the performance of machine learning models in identifying primary sources of sediment contamination based on the physico-chemical properties of stream sediments. A total of 356 stream sediment data sets of 18 quality parameters including 10 heavy metal species(Cd, Cu, Pb, Ni, As, Zn, Cr, Hg, Li, and Al), 3 soil parameters(clay, silt, and sand fractions), and 5 water quality parameters(water content, loss on ignition, total organic carbon, total nitrogen, and total phosphorous) were collected near abandoned metal mines and industrial complexes across the four major river basins in Korea. Two machine learning algorithms, linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were used to classify the sediments into four cases of different combinations of the sampling period and locations (i.e., mine in dry season, mine in wet season, industrial complex in dry season, and　industrial complex in wet season). Both models showed good performance in the classification, with SVM outperformed LDA; the accuracy values of LDA and SVM were 79.5% and 88.1%, respectively. An SVM ensemble model was used for multi-label classification of the multiple contamination sources inlcuding landuses in the upland areas within 1 km radius from the sampling sites. The results showed that the multi-label classifier was comparable performance with sinlgle-label SVM in classifying mines and industrial complexes, but was less accurate in classifying dominant land uses (50~60%). The poor performance of the multi-label SVM is likely due to the overfitting caused by small data sets compared to the complexity of the model. A larger data set might increase the performance of the machine learning models in identifying contamination sources.

• Korean Journal of Soil Science and Fertilizer
• /
• v.40 no.5
• /
• pp.418-428
• /
• 2007

Physical and chemical properties of the aggregate by-products including sludge and crushed dust samples collected from the 21 private companies throughout the country were analyzed to evaluate possible usage of the by-products as artificial soil materials for plantation. The pH of the materials ranged from 8.0 to 11.0. The organic matter content was $2.85g\;kg^{-1}$, and the total nitrogen content and available phosphate content were low as 0.7 percents and $12.98mg\;kg^{-1}$, respectively. Exchangeable $Ca^{2+}$, $Mg^{2+}$, $K^+$, and $Na^+$ were 2.29, 0.47, 0.02 and $0.05cmol\;kg^{-1}$, respectively. Heavy metal contents were lower than the limits regulated by environmental law of Korea. Textural analysis showed that most of the materials were silt loam with low water holding capacity ranged from 0.67 to 7.41 percents, and with low hydraulic conductivity ranged from 0.4 to $2.8m\;s^{-1}$. Mineralogical analysis showed that the aggregate by product materials were mostly composed of silicate, alumina and ferric oxides except calcium oxide dominant materials derived from limestones. The primary minerals were quartz, feldspars and dolomites derived from granite and granitic gneiss materials. Some samples derived from limestone material showed calcite and graphite together with the above minerals. According to the result, it can be concluded that the materials could be used as the artificial soil material for plantation after proper improvement of the physico-chemical properties and fertility.

• Journal of Korean Society of Forest Science
• /
• v.89 no.5
• /
• pp.618-629
• /
• 2000

This study was carried out to investigate ecological niche of Quercus acuta communities in Bogildo-island from July to October, 1998. This island is occupied by a subtropical evergreen broad-leaved forests. The study on community ecology of Q. acuta, mostly dominant species of subtropical forests, is very important for successful forest management. Sampling areas were selected in 16 quadrats, dominated by Q. acuta to examine the vegetation characteristics(plant identification, D.B.H.) and environmental elements (microtopography, altitude, slope degree, aspect, illumination and soil physicochemical properties). On the basis of data from field surveys, importance values were calculated for the dominance of Q. acuta and volume growth was analyzed by tree ring widths. The results obtained were as follows ; 1. The lists of vascular plants in the investigations were identified as 54 families, 91 genera, 113 species, 9 varieties, 1 formae. It appeared that 45 kinds were evergreen, 6 kinds(Camellia japonica, Ligustrum japonicum, Eurya japonica, Smilax china, Trachelospermum asiaticum var. intermedium, Carex lanceolata) were commonly observed in all plots and 5 species(Cinnamomum japonicum, Ardisia japonica, Cymbidium goeringii, Dryopteris bissetiana, Viburnum erosum) were most highly observed in all plots(over 80%). 2. The dominating species per strata were, Quercus acuta, Castanopsis cuspidata sp. Quercus salicina, Pinus thunbergii, Prunus sargentii in tree layer, Camellia Japonica, Ligustrum japonicum, Quercus acuta, Eurya japonica, Castanopsis cuspidata sp. in subtree layer, Camellia japonica, Ligustrum japonicum, Smilax china, Cinnamomum japonicum, Viburnum erosum in shrub layer and Trachelospermum asiaticum var. intermedium, Ardisia japonica, Carex lanceolata, Camellia japonica(seedlings), Quercus acuta(seedlings) in herb layer, all in descending orders. 3. Quercus acuta could be suggested as shade intolerant tree, considering the distribution in southern, western, nothern and eastern slopes in the descending orders. 4. Mean relative illumination in the forest is 0.89 % and it is relatively low in brightness. 5. Sustainment of Quercus acuta community couldn't be confirmed by judging from their reverse J curve in even-aged forest, as shown in D.B.H. distribution analysis. 6. The result of annual ring width analysis(mean ; 2.44 mm) showed three stages, such as a gentle increasing(1~12 year ; 2.04 mm), a relatively steep increasing(13~22 year ; 2.95 mm) and decreasing or stagnating(23 year after ; 2.41 mm).

• Trans-
• /
• v.12
• /
• pp.51-79
• /
• 2022

This study investigates the methods for deriving colors which can serve as a reference to users such as designers and or contents creators who search for online images from the web portal sites using specific words for color planning and more. Two experiments were conducted in order to accomplish this. Digital scenery photos within the geographic scope of Korea were downloaded from web portal sites, and those photos were studied to find out what colors were used to describe daytime and nighttime. Machine learning was used as the study methodology to classify colors in daytime and nighttime, and KSCA was used to derive the color frequency of daytime and nighttime photos and to compare and analyze the two results. The results of classifying the colors of daytime and nighttime photos using machine learning show that, when classifying the colors by 51~100%, the area of daytime colors was approximately 2.45 times greater than that of nighttime colors. The colors of the daytime class were distributed by brightness with white as its center, while that of the nighttime class was distributed with black as its center. Colors that accounted for over 70% of the daytime class were 647, those over 70% of the nighttime class were 252, and the rest (31-69%) were 101. The number of colors in the middle area was low, while other colors were classified relatively clearly into day and night. The resulting color distributions in the daytime and nighttime classes were able to provide the borderline color values of the two classes that are classified by brightness. As a result of analyzing the frequency of digital photos using KSCA, colors around yellow were expressed in generally bright daytime photos, while colors around blue value were expressed in dark night photos. For frequency of daytime photos, colors on the upper 40% had low chroma, almost being achromatic. Also, colors that are close to white and black showed the highest frequency, indicating a large difference in brightness. Meanwhile, for colors with frequency from top 5 to 10, yellow green was expressed darkly, and navy blue was expressed brightly, partially composing a complex harmony. When examining the color band, various colors, brightness, and chroma including light blue, achromatic colors, and warm colors were shown, failing to compose a generally harmonious arrangement of colors. For the frequency of nighttime photos, colors in approximately the upper 50% are dark colors with a brightness value of 2 (Munsell signal). In comparison, the brightness of middle frequency (50-80%) is relatively higher (brightness values of 3-4), and the brightness difference of various colors was large in the lower 20%. Colors that are not cool colors could be found intermittently in the lower 8% of frequency. When examining the color band, there was a general harmonious arrangement of colors centered on navy blue. As the results of conducting the experiment using two methods in this study, machine learning could classify colors into two or more classes, and could evaluate how close an image was with certain colors to a certain class. This method cannot be used if an image cannot be classified into a certain class. The result of such color distribution would serve as a reference when determining how close a certain color is to one of the two classes when the color is used as a dominant color in the base or background color of a certain design. Also, when dividing the analyzed images into several classes, even colors that have not been used in the analyzed image can be determined to find out how close they are to a certain class according to the color distribution properties of each class. Nevertheless, the results cannot be used to find out whether a specific color was used in the class and by how much it was used. To investigate such an issue, frequency analysis was conducted using KSCA. The color frequency could be measured within the range of images used in the experiment. The resulting values of color distribution and frequency from this study would serve as references for color planning of digital design regarding natural scenery in the geographic scope of Korea. Also, the two experiments are meaningful attempts for searching the methods for deriving colors that can be a useful reference among numerous images for content creator users of the relevant field.

• Korean Journal of Soil Science and Fertilizer
• /
• v.5 no.2
• /
• pp.1-38
• /
• 1972

This study, designed to establish a classification system of paddy soils and suitability groups on productivity and management of paddy land based on soil characteristics, has been made for the paddy soils on the Gimje-Mangyeong plains. The morphological, physical and chemical properties of the 15 paddy soil series found on these plains are briefly as follows: Ten soil series (Baeggu, Bongnam, Buyong, Gimje, Gongdeog, Honam, Jeonbug, Jisan, Mangyeong and Suam) have a B horizon (cambic B), two soil series (Geugrag and Hwadong) have a Bt horizon (argillic B), and three soil series (Gwanghwal, Hwagye and Sindab) have no B or Bt horizons. Uniquely, both the Bongnam and Gongdeog series contain a muck layer in the lower part of subsoil. Four soil series (Baeggu, Gongdeog, Gwanghwal and Sindab) generally are bluish gray and dark gray, and eight soil series (Bongnam, Buyong, Gimje, Honam, Jeonbug, Jisan, Mangyeong and Suam) are either gray or grayish brown. Three soil series (Geugrag, Hwadong and Hwagye), however, are partially gleyed in the surface and subsurface, but have a yellowish brown to brown subsoil or substrata. Seven soil series (Bongnam, Buyong, Geugrag, Gimje, Gongdeog, Honam and Hwadong) are of fine clayey texture, three soil series (Baeggu, Jeonbug and Jisan) belong to fine loamy and fine silty, three soil series (Gwanghwal, Mangyeong and Suam) to coarse loamy and coarse silty, and two soil series (Hwagye and Sindab) to sandy and sandy skeletal texture classes. The carbon content of the surface soil ranges from 0.29 to 2.18 percent, mostly 1.0 to 2.0 percent. The total nitrogen content of the surface soil ranges from 0.03 to 0.25 percent, showing a tendency to decrease irregularly with depth. The C/N ratio in the surface soil ranges from 4.6 to 15.5, dominantly from 8 to 10. The C/N ratio in the subsoil and substrata, however, has a wide range from 3.0 to 20.25. The soil reaction ranges from 4.5 to 8.0. All soil series except the Gwanghwal and Mangyeong series belong to the acid reaction class. The cation exchange cpacity in the surface soil ranges from 5 to 13 milliequivalents per 100 grams of soil, and in all the subsoil and substrata except those of a sandy texture, from 10 to 20 milliequivalents per 100 grams of soil. The base saturation of the soil series except Baeggu and Gongdeog is more than 60 percent. The active iron content of the surface soil ranges from 0.45 to 1.81 ppm, easily-reduceable manganese from 15 to 148 ppm, and available silica from 36 to 366 ppm. The iron and manganese are generally accumulated in a similar position (10 to 70cm. depth), and silica occurs in the same horizon with that of iron and manganese, or in the deeper horizons in the soil profile. The properties of each soil series extending from the sea shore towards the continental plains change with distance and they are related with distance (x) as follows: y(surface soil, clay content) = $$-0.2491x^2+6.0388x-1.1251$$ y(subsoil or subsurface soil, clay content) = $$-0.31646x^2+7.84818x-2.50008$$ y(surface soil, organic carbon content) = $$-0.0089x^2+0.2192x+0.1366$$ y(subsoil or subsurface soil, pH) = $$-0.0178x^2-0.04534x+8.3531$$ Soil profile development, soil color, depositional and organic layers, soil texture and soil reaction etc. are thought to be the major items that should be considered in a paddy soil classification. It was found that most of the soils belonging to the moderately well, somewhat poorly and poorly drained fine and medium textured soils and moderately deep fine textured soils over coarse materials, produce higher paddy yields in excess of 3,750 kg/ha. and most of the soils belonging to the coarse textured soils, well drained fine textured soils, moderately deep medium textured soils over coarse materials and saline soils, produce yields less than 3,750kg/ha. Soil texture of the profile, available soil depth, salinity and gleying of the surface and subsurface soils etc. seem to be the major factors determining rice yields, and these factors are considered when establishing suitability groups for paddy land. The great group, group, subgroup, family and series are proposed for the classification categories of paddy soils. The soil series is the basic category of the classification. The argillic horizon (Bt horizon) and cambic horizon (B horizon) are proposed as two diagnostic horizons of great group level for the determination of the morphological properties of soils in the classification. The specific soil characteristics considered in the group and subgroup levels are soil color of the profile (bluish gray, gray or yellowish brown), salinity (salic), depositonal (fluvic) and muck layers (mucky), and gleying of surface and subsurface soils (gleyic). The family levels are classified on the basis of soil reaction, soil texture and gravel content of the profile. The definitions are given on each classification category, diagnostic horizons and specific soil characteristics respectively. The soils on these plains are classified in eight subgroups and examined under the existing classification system. Further, the suitability group, can be divided into two major categories, suitability class and subclass. The soils within a suitability class are similar in potential productivity and limitation on use and management. Class 1 through 4 are distinguished from each other by combination of soil characteristics. Subclasses are divided from classes that have the same kind of dominant limitations such as slope(e), wettness(w), sandy(s), gravels(g), salinity(t) and non-gleying of the surface and subsurface soils(n). The above suitability classes and subclasses are examined, and the definitions are given. Seven subclasses are found on these plains for paddy soils. The classification and suitability group of 15 paddy soil series on the Gimje-Mangyeong plains may now be tabulated as follows.