1. 2022中研院地球所院區開放影片欣賞-穿雲透雨雷達現：透視地表祕密的合成孔徑雷達 (in Chinese)
2. Performance Study of Landslide Detection Using Multi-Temporal SAR Images
1. Natural Hazard Monitoring with InSAR
My team implements multiple InSAR processing flows on the High Performance Computing (HPC) environment within the Institute of Earth Sciences, Academia Sinica. The processing flows established include small baseline subsets approach, persistent scatterer approach and phase linking approach. We use these methods to monitor different types of natural hazards in Taiwan and in Southeast Asia. We also open up the HPC to students and collaborators from various universities/institutes.
Nguyen, M., Y.N. Lin, Q.C. Tran, C.-F. Ni, Y.-C. Chan, K.-H. Tseng, C.-P. Chang (2022) Assessment of long-term ground subsidence and groundwater depletion in Hanoi, Vietnam. Engineering Geology, 299, 106555, https://doi.org/10.1016/j.enggeo.2022.106555
Y.N. Lin, E. Park, Y. Wang, Y.P. Quek, J. Lim, E. Alcantara, H. Ho (2021) Multi-sensor data integration for slope failure investigation: A case study of the 2020 Hpakant Jade Mine incident, Myanmar, ISPRS Journal of Photogrammetry and Remote Sensing, 177, 291-305, https://doi.org/10.1016/j.isprsjprs.2021.05.015
2. SAR-based Change Detection
We develop the Growing Split-Based Approach (GSBA) algorithm for automatic change detection based on multi-temporal Synthetic Aperture Radar (SAR) imagery. The detection kernel is the Bayesian probability generalized to a three-class problem rather than the conventional two-class problem. GSBA is statistically robust and computationally efficient, with a user-friendly driver file for operational applications. It can also be utilized for other scenarios, such as changes associated with landslides.
Lin, Y.N., Chen, Y.-C., Kuo, Y.-T., Chao, W.-A. (2022) Performance Study of Landslide Detection Using Multi-Temporal SAR Images. Remote Sensing, 1-14, doi:10.3390/rs14102444.
Y.N. Lin, H.-S. Yun, A. Bhardwaj and Emma M. Hill (2019), Urban flood detection with Sentinel-1 Multi-Temporal Synthetic Aperture Radar (SAR) Observations in a Bayesian Framework: A case study for Hurricane Matthew, Remote Sens. 2019, 11, 1778; https://doi.org/10.3390/rs11151778
3. Mobile Shales in Active Orogeny
There are many active mud volcanoes onshore and offshore southwestern Taiwan, yet the source area at depth is usually not clearly imaged. We utilize the industry-level seismic processing software, Geovation, to reprocess long-offset seismic data in order to figure out the geometry and physical properties associated with the patches of mobile shales at depth. The goal is to unravel the role mobile shales play in an active orogenic belt.
Wang, Y., Y. N. Lin, Y. Ota, L.-H. Chung, J. B. H. Shyu, H.-W. Chiang, Y.-G. Chen, H.-H. Hsu, and C.-C. Shen (2022), Mud Diapir or fault-related fold? On the development of an active mud-cored anticline offshore southwestern Taiwan, Tectonics, e2022TC007234, https://doi.org/10.1029/2022TC007234
Lai, S.-Y., Y.N. Lin, H.-H. Hsu (2022) Efficient 2D multiple attenuation using SRME with curvelet-domain subtraction. Marine Geophysical Research, 43, 1, https://doi.org/10.1007/s11001-021-09464-8
Automated Synthetic Aperture Radar (SAR) processing system is a collaborative project with NASA Jet Propulsion Laboratory and the Earth Observatory of Singapore, Nanyang Technological University. The whole system is developed on a public cloud to respond to natural hazards around the globe in near real time by using multiple SAR satellites. We also engage end users actively in order to make the real use of the products from this automated system, which gives both academic and applied dimension to this project.
- Nguyen Minh
Earth System Science, Taiwan International Graduate Program
- Timothy Day
Department of Geosciences, National Taiwan University
- Chong-You (Kevin) Wang
School of Earth Sciences
Ohio State University
- Yi-Ching Chen
CGG Satellite Mapping (UK)
- Jui-Chi (Vickie) Lee
Department of Geosciences, Virginia Tech
Subsurface Imaging Lab
The Subsurface Imaging Lab adopts the Geovation system from the petroleum service company CGG. With industry-level processing techniques and special knowledge about velocity model building, we bring the expertise from industry to academia and aim to explore the ultimate information in each of the seismic profiles!
- Ching-Ye (Jack) Chen
- Yi-Wei Lin
Exploration and Development Research Institute (EDRI), Chinese Petroleum Corporation (CPC)
Institute of Oceanography, National Taiwan University
- Yiping Ko
Media Tek Inc.
- Feisal Dirgantara
- Jui-Chun Freya Chen
Department of the Geophysical Sciences
University of Chicago
- Szu-Ying (Lucine) Lai
School of Ocean and Earth Science, University of Southampton