• The Korean Journal of Petroleum Geology
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• v.2 no.2 s.3
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• pp.51-57
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• 1994

Analysis of the aeromagnetic data aquired by US Navy in the year 1969 permits us to predict a new sedimentary basin, Heugsan Basin, south of the known Gunsan Basin in Block Ⅱ. The basin appears to consist of three sub-basins trending NNW-SSE. The results of our analysis provide not only an independent assessment of the Gunsan Basin, but also new important information on the tectonic origin and mechanism for the two basins as well as for the entire region. The basin forming tectonic style is interpreted as rhombochasm associated with double overstepped left-lateral wrench faults. From the magnetic evidence, a few NE-SW trending major onshore faults are extended to the study area. We also interpreted the nature of the faults to be left-lateral wrenches. This new gross structural style is consistent with the results of recent Yeongdong Basin analysis by Lee. The senses of fault movement are also supported by the paleomagnetic evidence that the Philippine Sea had experienced an 80-degree clockwise rotation since the Eocene. Based on a 2 $\frac{1}{2}$ model study the probable maximum thickness of the sediments in the Gunsan Basin is approximately 7500 meters. We believe that the new Heugsan Basin was left unidentified because a high velocity layer may be overlying the basin. Because the overall structural configuration of the Heugsan Basin appears to be favorable for hydrocarbon accumulation, a detailed airborne magnetic survey is recommended in the area in order to verify the magnetic expression of this thick basin. A detailed subsequent marine gravity survey is also recommended in order to delineate the sedimentary section and to acquire supplemental data to the magnetic method only if an overlying high velocity layer is confirmed. Otherwise a high energy source seismic survey may be more effective.

• Korean Journal of Ecology and Environment
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• v.48 no.3
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• pp.147-152
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• 2015

Nitrogen (N) loading from domestic, agricultural and industrial sources can lead to excessive growth of macrophytes or phytoplankton in aquatic environment. Many studies have used nitrogen stable isotope ratios to identify anthropogenic nitrogen in aquatic systems as a useful method for studying nitrogen cycle. In this study to evaluate the precision and accuracy of Kjeldahl processes, two reference materials (IAEA-NO-3, N-1) were analyzed repeatedly. Measured the ${\delta}^{15}N-NO_3$ and ${\delta}^{15}N-NH_4$ values of IAEA-NO-3 and IAEA-N-1 were $4.7{\pm}0.2$‰ and $0.4{\pm}0.3$‰, respectively, which are within recommended values of analytical uncertainties. Also, we investigated spatial patterns of ${\delta}^{15}N-NO_3$ and ${\delta}^{15}N-NH_4$ in effluent plumes from a waste water treatment plant in Han River, Korea. ${\delta}^{15}N-NO_3$ and ${\delta}^{15}N-NH_4$ values are enriched at downstream areas of water treatment plant suggesting that dissolved nitrogen in effluent plumes should be one of the main N sources in those areas. The current study clarifies the reliability of Kjeldahl analytical method and the usefulness of stable isotopic techniques to trace the contamination source of dissolved nitrogen such as nitrate and ammonia.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.265-274
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• 2021

Outlier detection research in ocean data has traditionally been performed using statistical and distance-based machine learning algorithms. Recently, AI-based methods have received a lot of attention and so-called supervised learning methods that require classification information for data are mainly used. This supervised learning method requires a lot of time and costs because classification information (label) must be manually designated for all data required for learning. In this study, an autoencoder based on unsupervised learning was applied as an outlier detection to overcome this problem. For the experiment, two experiments were designed: one is univariate learning, in which only SST data was used among the observation data of Deokjeok Island and the other is multivariate learning, in which SST, air temperature, wind direction, wind speed, air pressure, and humidity were used. Period of data is 25 years from 1996 to 2020, and a pre-processing considering the characteristics of ocean data was applied to the data. An outlier detection of actual SST data was tried with a learned univariate and multivariate autoencoder. We tried to detect outliers in real SST data using trained univariate and multivariate autoencoders. To compare model performance, various outlier detection methods were applied to synthetic data with artificially inserted errors. As a result of quantitatively evaluating the performance of these methods, the multivariate/univariate accuracy was about 96%/91%, respectively, indicating that the multivariate autoencoder had better outlier detection performance. Outlier detection using an unsupervised learning-based autoencoder is expected to be used in various ways in that it can reduce subjective classification errors and cost and time required for data labeling.

• 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.