[논문]활성단층 조사에 활용되는 원격탐사 기술과 사례의 고찰

권오상; 손효록; 배상열; 박기웅; 최호석; 김영석; 이승국

doi:10.7780/kjrs.2021.37.6.2.12

활성단층 조사에 활용되는 원격탐사 기술과 사례의 고찰
A Review on Remote Sensing Techniques and Case Studies for Active Fault Investigation 원문보기

대한원격탐사학회지 = Korean journal of remote sensing, v.37 no.6 pt.2, 2021년, pp.1901 - 1922

권오상 (부경대학교 지구환경과학부) , 손효록 (부경대학교 지구환경과학부) , 배상열 (부경대학교 지구환경과학부) , 박기웅 (부경대학교 지구환경과학부) , 최호석 (부경대학교 지구환경과학부) , 김영석 (부경대학교 지구환경과학과) , 이승국 (부경대학교 지구환경과학과)

초록
AI-Helper

대부분의 대규모 지진은 기존의 활성단층이 재활하여 발생하므로, 이러한 활성단층의 위치와 특성을 파악하는 것은 지진재해 연구와 지진방재 측면에서 매우 중요하다. 최근에는 활성단층 조사에서 지표지질조사에 앞서 실시하는 선형구조 분석에 다양한 원격탐사 기술이 유용하게 활용되고 있다. 본 논문에서는 원격탐사 기술 중 이러한 활성단층 조사에 널리 활용되는 위성원격탐사, 항공원격탐사, 그리고 InSAR, LiDAR 기법의 간단한 원리와 적용사례를 소개하고자 한다. 또한, GIS를 활용하여 단층활동에 의해 형성된 경사급변점과 주향이동단층의 수평변위를 분석한 사례를 소개하고자 한다. 토의에서는 항공사진을 활용하여 DEM을 구축할 때 발생할 수 있는 문제점들과 해결방안, 항공 LiDAR 기반 DEM의 문제점을 극복하여 개발한 새로운 기법인 RRIM에 대해 논의하고자 한다. 활성단층 조사에서 어떤 원격탐사 기술이 활용되는지 이해하고 각 원격탐사기법의 장단점과 한계점을 이해하여 상황에 따라 적절한 방법을 활용하는 것은 효율적인 활성단층조사를 위해 중요하다.

Abstract ▼ AI-Helper

Since most large earthquakes occur by reactivation of preexisting active faults, it is important to understand the locations and characteristics of active faults in terms of earthquake hazard research and earthquake disaster prevention. Recently, several remote sensing techniques are broadly used for lineament analysis performed prior to field surveys in active fault surveys. The aim of this paper is introducing simple principles and application examples of each remote sensing technique (satellite remote sensing, airborne remote sensing, InSAR, LiDAR) widely used for active fault investigation. This paper also explains the analytical methods for the slope break generated by fault activity based on GIS and the horizontal displacement of the strike-slip fault. In discussion, we would like to discuss the problems and solutions on making DEM based on aerial photography, and a new developed technique (RRIM) to overcome the problems of DEM based on aerial LiDAR. Understanding remote sensing techniques used for active fault investigation and utilizing appropriate methods depending on the situation and limitations of each remote sensing technique are important for effective active fault investigation.

주제어

표/그림 (13)

그림 Fig. 1. Before and after the landslide that occurred during 2005 Mw 7.6 earthquake in Kashmir (https://earthobservatory.sg).
그림 Fig. 2. Aerial photographs (taken in 1954, 2021 respectively). Note that small streams (blue lines) are deflected across the inferred fault scarp (modified from Gwon et al., 2021).
그림 Fig. 3. Measuring surface movement with InSAR. An orbiting satellite sends a coherent radar signal to the surface and measures the backscattered radiation. The phase difference (position in the wave cycle) between the signal returning at two different times (time 1 in black and time 2 in red) can be used to estimate ground movement caused by a range of mechanisms (Biggs and Wright, 2020).
그림 Fig. 4. Observed, modeled, and residual interferograms for ALOS-2 and Sentinel-1A data. Heavy black lines indicate the modeled fault patches to have slipped during the earthquake. LOS, line of sight (Hamling et al., 2017).
그림 Fig. 5. (a) Unwrapped phase from the co-seismic interferogram in Fig. 2a. Black areas correspond to the city of Bam and Bavarat village, and with the fault near field, (b) synthetic interferogram. The white line is the trace of the modelled fault, (c) residual map (Stramonodo et al., 2005).
그림 Fig. 7. Comparisons between the aerial photograph and the DEM images of the same study area, (a) aerial orthophoto with a 25 cm resolution, (b) 40 m DTM, (c) 5 m resolution DEM, (d) 1 m resolution LiDAR-derived DEMs filtering buildings and vegetation (Chen et al., 2015).
그림 Fig. 6. (a) Schematic diagram showing the principle of airborne LiDAR, (b) schematic diagram showing the detection of multiple echos (Höfle and Rutzinger, 2011).
그림 Fig. 8. Result of topographic surface classification using the airborne LiDAR (analysis radius: 20 cells), (a) result of analysis using the classification of fans, (b) result of analysis using 0.5 m resolution LiDAR, (c) result of analysis using 1.0 m resolution LiDAR, (d) result of analysis using 5.0 m resolution LiDAR (Kim and Oh, 2019).
그림 Fig. 9. (a) Photograph of the Inbo-ri trench site, (b) photograph of the reproduced Inbo-ri trench site using point cloud data obtained with terrain LiDAR (Lee and Kim, 2021).
그림 Fig. 10. (a) Example profile of a composite fault scarp with two slope breaks, The last displacement increment is recorded by the steep free face. (b) Field photograph of a composite fault scarp with three slope breaks in its upper part, the free face represents the last displacement increment (LDI). (c) Example profile of a multiple fault scarp consisting of two ruptures, measurement of cumulative displacement is based on the location of slope breaks (XR1-1, XR1-2, XR2-1, XR2-2). The cumulative displacement is approximated by the sum of surface offsets. (d) Field photograph of the multiple fault scarp shown in Fig. 10(c) (Ewiak et al., 2015).
그림 Fig. 11. (A) Hillshade map of channel ZA10792a (Zielke et al., 2010) with fault trace (turquoise) and profile locations (red and blue), (B, C) schematic plot of channel thalweg, fault trace, and profile locations, this channel is not parallel to the fault trace so that the trends of up-fault and downfault channel section have to be determined (yellow dashed line in C). (D, E, F) Profile position and morphology parameters and their effect on the summed elevation difference which are area shown in gray (Zielke and Arrowsmith, 2012).
그림 Fig. 13. Various digital elevation model visualizations, (a) shaded relief (hillshade) map illuminated from the northwest, (b) shaded relief (hillshade) map illuminated from the southwest, (c) slope map, (d) slope map overlaid with half-transparent elevation coloring, (e) original RRIM, these examples are from Kadowaki area, a section along the active left-lateral Nobi fault zone, central Japan. Yellow arrows indicate the trace of the active Ibigawa fault (modified from Kaneda and Chiba, 2019).
그림 Fig. 12. Colour diagram of RRIM (Chiba et al., 2008).

AI 본문요약
AI-Helper

문제 정의

이 논문에서는 다양한 원격탐사 기법 중에서 활성단층 조사에 활용되는 원격탐사 기술과 이들을 활용한 사례들에 대해 간단히 살펴보았다. 활성단층 조사에서 원격탐사 기술은 지표지질조사를 실시하기 전 선형구조 분석에 유용하게 활용된다.

제안 방법

이런 단층운동으로 인한 변형을 정량적으로 측정 및 분석하기 위해 최근 GIS를 활용한 다양한 기법들이 널리 적용되고 있다. 본 절에서는 GIS를 활용하여 경사이동단층에 서는 경사급변점의 수직변위량을 어떻게 측정하고 해석하였는지 그리고 주향이동단층에서 변위하천(offset channel)의 수평변위량을 측정하고 해석한 일부 사례들을 소개하고자 한다.

성능/효과

위성원격탐사를 활용하면 넓은 지역을 동시에 촬영할 수 있고, 동일 지역을 주기적으로 촬영할 수 있다. 그리고 ASTER와 SRTM 기반의 DEM 자료를 활용하면 비교적 짧은 시간 내에 넓은 지역에 대한 자료를 획득할 수 있으나, 항공사진과 비교했을 때 해상도가 떨어진다. 과거 항공사진은 산업화와 도시화로 인해 현재 지형이 심하게 훼손된 우리나라와 같은 환경에서 활용하기 좋은 장점이 있지만, 활성단층 조사에서 주로 활용되는 과거 항공사진은 일부 지역에서만 촬영되어 선택의 폭이 좁고 수치영상 추출을 위한 스캔 과정에서 오차가 발생할 수 있다는 단점이 있다.
이를 극복하기 위해 최근 GIS를 활용하여 넓은 지역에서 획득한 DEM 자료를 활용하여 단층을 가로지르는 고도분석을 실시하여 단층활동과 관련된 경사급변점을 인지하여 활용하고 있다. 주향이동단층의 수평변위를 산출하기 위해서는 수차례의 굴착조사를 실시하여야 하지만, 선형적인 특징을 보이는 기준점을 활용하여 단층활동 전으로 복원함으로써 GIS를 활용하여 수평변위량을 도출할 수 있다.

후속연구

이러한 원격탐사 기술을 바탕으로 단층대를 따라 몇 개의 지점에서 고지진학적인 굴착조사를 성공적으로 수행한다면 지진의 재발주기나 규모를 비교적 정확하게 특정할 수 있다. 그러나 수십~수백 km의 단층대 전 구간을 따라 굴착조사를 수행하기에는 시간과 경비가 많이 소요되어 현실적으로 매우 어렵다.
항공사진을 활용하여 제작한 DEM 자료는 여러 장점이 있지만, 2차원 자료를 3차원 자료로 변환하는 과정에서 발생하는 문제점이 있어 분석과정에서 주의 깊게 사용하여야 한다. 항공 LiDAR 기반의 DEM 자료를 시각화하는 절차에서의 한계점을 극복하고자 개발된, 지형학적 경사와 openness를 활용한 RRIM 기법도 적절하게 적용된다면 매우 유용하게 활용될 수 있다. 본 논문에서 소개된 원격탐사 기술 외에도 활성단층 조사에 활용되는 많은 원격탐사 기술들이 존재하며, 방법마다 장단점이 존재하기 때문에 각 방법의 특성, 장단점 등을 이해하고 상황과 목적에 맞는 적절한 방법을 선택하는 것이 원하는 목적과 양질의 자료획득에 중요하다.

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