• Korean Journal of Mineralogy and Petrology
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• v.34 no.2
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• pp.107-120
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• 2021

The Komdok Pb-Zn deposit, which is the largest Pb-Zn deposit in Korea, is located at the Hyesan-Riwon metallogenic zone in Jiao Liao Ji belt included Paleoproterozoic Macheolryeong group. The geology of this deposit consists of Paleoproterozoic metasedimentary rocks, Jurassic Mantapsan intrusive rocks and Cenozoic basalt. The Komdok deposit which is a SEDEX type deposit occurs as layer ore and vein ore in the Paleoproterozoic metasedimentary rocks. Based on mineral petrography and paragenesis, dolomites from this deposit are classified four types (1. dolomite (D₀) as hostrock, 2. early dolomite (D₁) associated with tremolite, actinolite, diopside, sphalerite and galena from amphibolite facies, 3. late dolomite (D₂) associated with talc, calcite, quartz, sphalerite and galena from amphibolite facies, 4. dolomite (D₃) associated with white mica, chlorite, sphalerite and galena from quartz vein). The structural formulars of dolomites are determined to be Ca_1.00-1.20Mg_0.80-0.99Fe_0.00-0.01Zn_0.00-0.02(CO₃)₂(D₀), Ca_1.00-1.02M_0.97-0.99Fe_0.00-0.01Zn_0.00-0.02(CO₃)₂(D₁), Ca_0.99-1.03Mg_0.93-0.98Fe_0.01-0.05Mn_0.00-0.01As_0.00-0.01(CO₃)₂(D₂) and Ca_0.95-1.04Mg_0.59-0.68Fe_0.30-0.36Mn_0.00-0.01 (CO₃)₂(D₃), respectively. It means that dolomites from Komdok deposit have higher content of trace elements (FeO, MnO, HfO₂, ZnO, PbO, Sb₂O₅ and As₂O₅) compared to the theoretical composition of dolomite. These trace elements (FeO, MnO, ZnO, Sb₂O₅ and As₂O₅) show increase and decrease trend according to paragenetic sequence, but HfO₂ and PbO elements no show increase and decrease trend according to paragenetic sequence. Dolomites correspond to Ferroan dolomite (D₀, D₁ and D₂), and Ferroan dolomite and ankerite (D₃), respectively. Therefore, 1) dolomite (D₀) as hostrock was formed by subsequent diagenesis after sedimentation of Paleoproterozoic (2012~1700 Ma) silica-bearing dolomite in the marine evaporative environment. 2) Early dolomite (D₁) was formed by hydrothermal metasomatism origined metamorphism (amphibolite facies) associated with intrusion (1890~1680 Ma) of Paleoproterozoic Riwon complex. 3) Late dolomte (D₂) was formed from residual fluid by a decrease of temperature and pressure. and dolomite (D₃) in quartz vein was formed by intrusion (213~181 Ma) of Jurassic Mantapsan intrusive rocks.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1565-1576
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• 2023

An atmospheric correction algorithm based on the radiative transfer model is required to obtain remote-sensing reflectance (R_rs) from the Geostationary Ocean Color Imager-II (GOCI-II) observed at the top-of-atmosphere. This R_rs derived from the atmospheric correction is utilized to estimate various marine environmental parameters such as chlorophyll-a concentration, total suspended materials concentration, and absorption of dissolved organic matter. Therefore, an atmospheric correction is a fundamental algorithm as it significantly impacts the reliability of all other color products. However, in clear waters, for example, atmospheric path radiance exceeds more than ten times higher than the water-leaving radiance in the blue wavelengths. This implies atmospheric correction is a highly error-sensitive process with a 1% error in estimating atmospheric radiance in the atmospheric correction process can cause more than 10% errors. Therefore, the quality assessment of R_rs after the atmospheric correction is essential for ensuring reliable ocean environment analysis using ocean color satellite data. In this study, a Quality Assurance (QA) algorithm based on in-situ R_rs data, which has been archived into a database using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Archive and Storage System (SeaBASS), was applied and modified to consider the different spectral characteristics of GOCI-II. This method is officially employed in the National Oceanic and Atmospheric Administration (NOAA)'s ocean color satellite data processing system. It provides quality analysis scores for R_rs ranging from 0 to 1 and classifies the water types into 23 categories. When the QA algorithm is applied to the initial phase of GOCI-II data with less calibration, it shows the highest frequency at a relatively low score of 0.625. However, when the algorithm is applied to the improved GOCI-II atmospheric correction results with updated calibrations, it shows the highest frequency at a higher score of 0.875 compared to the previous results. The water types analysis using the QA algorithm indicated that parts of the East Sea, South Sea, and the Northwest Pacific Ocean are primarily characterized as relatively clear case-I waters, while the coastal areas of the Yellow Sea and the East China Sea are mainly classified as highly turbid case-II waters. We expect that the QA algorithm will support GOCI-II users in terms of not only statistically identifying R_rs resulted with significant errors but also more reliable calibration with quality assured data. The algorithm will be included in the level-2 flag data provided with GOCI-II atmospheric correction.