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Paleomagnetism, Stratigraphy and Geologic Structure of the Tertiary Pohang and Changgi Basins; K-Ar Ages for the Volcanic Rocks (포항(浦項) 및 장기분지(盆地)에 대한 고지자기(古地磁氣), 층서(層序) 및 구조연구(構造硏究); 화산암류(火山岩類)의 K-Ar 연대(年代))

  • Lee, Hyun Koo;Moon, Hi-Soo;Min, Kyung Duck;Kim, In-Soo;Yun, Hyesu;Itaya, Tetsumaru
    • Economic and Environmental Geology
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    • v.25 no.3
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    • pp.337-349
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    • 1992
  • The Tertiary basins in Korea have widely been studied by numerous researchers producing individual results in sedimentology, paleontology, stratigraphy, volcanic petrology and structural geology, but interdisciplinary studies, inter-basin analysis and basin-forming process have not been carried out yet. Major work of this study is to elucidate evidences obtained from different parts of a basin as well as different Tertiary basins (Pohang, Changgi, Eoil, Haseo and Ulsan basins) in order to build up the correlation between the basins, and an overall picture of the basin architecture and evolution in Korea. According to the paleontologic evidences the geologic age of the Pohang marine basin is dated to be late Lower Miocence to Middle Miocene, whereas other non-marine basins are older as being either Early Miocene or Oligocene(Lee, 1975, 1978: Bong, 1984: Chun, 1982: Choi et al., 1984: Yun et al., 1990: Yoon, 1982). However, detailed ages of the Tertiary sediments, and their correlations in a basin and between basins are still controversial, since the basins are separated from each other, sedimentary sequence is disturbed and intruded by voncanic rocks, and non-marine sediments are not fossiliferous to be correlated. Therefore, in this work radiometric, magnetostratigraphic, and biostratigraphic data was integrated for the refinement of chronostratigraphy and synopsis of stratigraphy of Tertiary basins of Korea. A total of 21 samples including 10 basaltic, 2 porphyritic, and 9 andesitic rocks from 4 basins were collected for the K-Ar dating of whole rock method. The obtained age can be grouped as follows: $14.8{\pm}0.4{\sim}15.2{\pm}0.4Ma$, $19.9{\pm}0.5{\sim}22.1{\pm}0.7Ma$, $18.0{\pm}1.1{\sim}20.4+0.5Ma$, and $14.6{\pm}0.7{\sim}21.1{\pm}0.5Ma$. Stratigraphically they mostly fall into the range of Lower Miocene to Mid Miocene. The oldest volcanic rock recorded is a basalt (911213-6) with the age of $22.05{\pm}0.67Ma$ near Sangjeong-ri in the Changgi (or Janggi) basin and presumed to be formed in the Early Miocene, when Changgi Conglomerate began to deposit. The youngest one (911214-9) is a basalt of $14.64{\pm}0.66Ma$ in the Haseo basin. This means the intrusive and extrusive rocks are not a product of sudden voncanic activity of short duration as previously accepted but of successive processes lasting relatively long period of 8 or 9 Ma. The radiometric age of the volcanic rocks is not randomly distributed but varies systematically with basins and localities. It becomes generlly younger to the south, namely from the Changgi basin to the Haseo basin. The rocks in the Changgi basin are dated to be from $19.92{\pm}0.47$ to $22.05{\pm}0.67Ma$. With exception of only one locality in the Geumgwangdong they all formed before 20 Ma B.P. The Eoil basalt by Tateiwa in the Eoil basin are dated to be from $20.44{\pm}0.47$ to $18.35{\pm}0.62Ma$ and they are younger than those in the Changgi basin by 2~4 Ma. Specifically, basaltic rocks in the sedimentary and voncanic sequences of the Eoil basin can be well compared to the sequence of associated sedimentary rocks. Generally they become younger to the stratigraphically upper part. Among the basin, the Haseo basin is characterized by the youngest volcanic rocks. The basalt (911214-7) which crops out in Jeongja-ri, Gangdong-myon, Ulsan-gun is $16.22{\pm}0.75Ma$ and the other one (911214-9) in coastal area, Jujon-dong, Ulsan is $14.64{\pm}0.66Ma$ old. The radiometric data are positively collaborated with the results of paleomagnetic study, pull-apart basin model and East Sea spreading theory. Especially, the successively changing age of Eoil basalts are in accordance with successively changing degree of rotation. In detail, following results are discussed. Firstly, the porphyritic rocks previously known as Cretaceous basement (911213-2, 911214-1) show the age of $43.73{\pm}1.05$$49.58{\pm}1.13Ma$(Eocene) confirms the results of Jin et al. (1988). This means sequential volcanic activity from Cretaceous up to Lower Tertiary. Secondly, intrusive andesitic rocks in the Pohang basin, which are dated to be $21.8{\pm}2.8Ma$ (Jin et al., 1988) are found out to be 15 Ma old in coincindence with the age of host strata of 16.5 Ma. Thirdly, The Quaternary basalt (911213-5 and 911213-6) of Tateiwa(1924) is not homogeneous regarding formation age and petrological characteristics. The basalt in the Changgi basin show the age of $19.92{\pm}0.47$ and $22.05{\pm}0.67$ (Miocene). The basalt (911213-8) in Sangjond-ri, which intruded Nultaeri Trachytic Tuff is dated to be $20.55{\pm}0.50Ma$, which means Changgi Group is older than this age. The Yeonil Basalt, which Tateiwa described as Quaternary one shows different age ranging from Lower Miocene to Upper Miocene(cf. Jin et al., 1988: sample no. 93-33: $10.20{\pm}0.30Ma$). Therefore, the Yeonil Quarterary basalt should be revised and divided into different geologic epochs. Fourthly, Yeonil basalt of Tateiwa (1926) in the Eoil basin is correlated to the Yeonil basalt in the Changgi basin. Yoon (1989) intergrated both basalts as Eoil basaltic andesitic volcanic rocks or Eoil basalt (Yoon et al., 1991), and placed uppermost unit of the Changgi Group. As mentioned above the so-called Quarternary basalt in the Eoil basin are not extruded or intruaed simultaneously, but differentiatedly (14 Ma~25 Ma) so that they can not be classified as one unit. Fifthly, the Yongdong-ri formation of the Pomgogri Group is intruded by the Eoil basalt (911214-3) of 18.35~0.62 Ma age. Therefore, the deposition of the Pomgogri Group is completed before this age. Referring petrological characteristics, occurences, paleomagnetic data, and relationship to other Eoil basalts, it is most provable that this basalt is younger than two others. That means the Pomgogri Group is underlain by the Changgi Group. Sixthly, mineral composition of the basalts and andesitic rocks from the 4 basins show different ground mass and phenocryst. In volcanic rocks in the Pohang basin, phenocrysts are pyroxene and a small amount of biotite. Those of the Changgi basin is predominant by Labradorite, in the Eoil by bytownite-anorthite and a small amount pyroxene.

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Twitter Issue Tracking System by Topic Modeling Techniques (토픽 모델링을 이용한 트위터 이슈 트래킹 시스템)

  • Bae, Jung-Hwan;Han, Nam-Gi;Song, Min
    • Journal of Intelligence and Information Systems
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    • v.20 no.2
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    • pp.109-122
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    • 2014
  • People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.