• Title/Summary/Keyword: EC 및 온도 검층

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Replacement of Saline Water through Injecting Fresh Water into a Confined Saline Aquifer at the Nakdong River Delta Area (염수로 충진된 낙동강 델타지역 피압대수층에서 담수주입에 의한 염수치환 연구)

  • Won, Kyung-Sik;Chung, Sang Yong;Lee, Chang-Sup;Jeong, Jae-Hoon
    • The Journal of Engineering Geology
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    • v.25 no.2
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    • pp.215-225
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    • 2015
  • We performed injection tests in a deep-seated confined aquifer to assess the potential of artificial recharge as a means of preventing saltwater contamination, thereby securing groundwater resources for the Nakdong Delta area of Busan City, Korea. The study area comprises a confined aquifer, in which a 10-21-m-thick clay layer overlies 31.5-36.5 m of sand and a 2.8-11-m-thick layer of gravel. EC logging of five monitoring wells yielded a value of 7-44 mS/cm, with the transition between saline and fresh water occurring at a depth of 15-38 m. Above 5 m depth, water temperature is 10-15.5℃, whereas between 5 and 50 m depth the temperature is 15.5-17℃. Approximately 950 m3 of fresh water was injected into the OW-5 injection well at a rate of 370 m3/day for 62 hours, after which the fresh water zone was detected by a CTD Diver installed at a depth of 40 m. The persistence of the fresh water zone was determined via EC and temperature logging at 24 hours after injection, and again 21 days after injection. We observed a second fresh water zone in the OW-2 well, where the first injection test was performed more than 20 days before the second injection test. The contact between fresh and saline water in the injection well is represented by a sharp boundary rather than a transitional boundary. We conclude that the injected fresh water occupied a specific space and served to maintain the original water quality throughout the observation period. Moreover, we suggest that artificial recharge via long-term injection could help secure a new alternative water resource in this saline coastal aquifer.

온양 지역 온천수의 수질 특성 : 천부 지하수와 혼합 비율 분석

  • 정복선;구민호;김형수
    • Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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    • 2001.09a
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    • pp.199-203
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    • 2001
  • 온양온천지구의 13개 온천공에서 채취한 온천수의 수질 자료를 이용하여 심부 온천수와 천부 지하수의 혼합비를 추정하였다. 온천수의 pH, EC, 및 주요 이온의 농도는 40~54$^{\circ}C$의 범위를 나타내는 온천수의 온도와 뚜렷한 선형의 비례관계를 나타내며, 수온이 낮아질수록 천부 지하수의 수질 특성에 가까워지는 특성을 보인다. pH, $K^{+}$, F$^{-}$, 및 Si는 온도와 정(+)의 비례관계를, $Ca^{2+}$, $Mg^{2+}$, Cl$^{-}$, HCO$_3$$^{-}$, SO$_4$$^{2-}$ , NO$_3$$^{-}$ 및 EC는 부(-)의 비례관계를 나타낸다. 온천수의 온도와 수질과의 이러한 상관성은 수질 특성이 상이한 고온의 심부 온천수와 저온의 천부 지하수가 각 온천공에서 서로 다른 비율로 혼합되어 나타난 결과로 해석된다. 최고 온도를 나타내는 온천수와 온천지구 내 지하수의 수질을 끝 성분(end member)으로 가정하고 혼합비를 계산한 결과, 온천지구에서 현재 채수되는 온천수에는 20% 내외의 천부 지하수가 혼합되고 있는 것으로 나타났다. 온천수와 지하수의 수질 자료를 파이퍼 다이어그램에 도시한 결과 $Na^{+}$-HCO$_3$$^{-}$의 유형을 나타내는 13개 온천수는 전체적으로 직선 상에 분포하는 경향을 보였으며, $Ca^{2+}$-HCO$_3$$^{-}$의 유형을 나타내는 지하수는 직선의 연장선상에 분포하여 온천수와 지하수의 혼합이 일어나고 있음을 보여준다. 온천공 수질 검층 결과, 심도 145m를 경계로 지하수와 온천수가 상하부에 부존되어 있으며, 경계부에서 혼합이 발생하고 있는 것으로 추정된다.

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A Study on the Applicability of Machine Learning Algorithms for Detecting Hydraulic Outliers in a Borehole (시추공 수리 이상점 탐지를 위한 기계학습 알고리즘의 적용성 연구)

  • Seungbeom Choi; Kyung-Woo Park;Changsoo Lee
    • Tunnel and Underground Space
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    • v.33 no.6
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    • pp.561-573
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    • 2023
  • Korea Atomic Energy Research Institute (KAERI) constructed the KURT (KAERI Underground Research Tunnel) to analyze the hydrogeological/geochemical characteristics of deep rock mass. Numerous boreholes have been drilled to conduct various field tests. The selection of suitable investigation intervals within a borehole is of great importance. When objectives are centered around hydraulic flow and groundwater sampling, intervals with sufficient groundwater flow are the most suitable. This study defines such points as hydraulic outliers and aimed to detect them using borehole geophysical logging data (temperature and EC) from a 1 km depth borehole. For systematic and efficient outlier detection, machine learning algorithms, such as DBSCAN, OCSVM, kNN, and isolation forest, were applied and their applicability was assessed. Following data preprocessing and algorithm optimization, the four algorithms detected 55, 12, 52, and 68 outliers, respectively. Though this study confirms applicability of the machine learning algorithms, it is suggested that further verification and supplements are desirable since the input data were relatively limited.