Surface deformation simulation for InSAR detection using a machine learning approach on the hantangang river volcanic field: A case study on the orisan mountain - Fadhillah, Muhammad Fulki; Hakim, Wahyu Luqmanul; Park, Sungjae; Kim, Daewoo; Park, Yu-Chul; Kim, Chang-Hwan; Lee, Chang-Wook (Frontiers in Environmental Science, 2022)
Recent developments in remote sensing research have resulted in a large amount of variability in the data provided by researchers. Synthetic aperture radar (SAR) is a tool used to measure surface deformation and assess changes in the Earth's surface. Here, we consider the usefulness of Interferometric Synthetic Aperture Radar (InSAR) in assessing past volcanic activity as a key to learning the characteristics of the deformation around a volcano. The Hantangang River volcanic field (HRVF) is a geoheritage site in the Korean Peninsula that has interesting geological characteristics. This volcanic field has formed along 110 km of the paleochannel of the Hantangang River. Since the eruptions occurred from 0.15 to 0.51 Ma, the source is limited, which has raised interest in the assessment of volcanic landforms. The recent integration of machine learning and InSAR processing has shown promising results for many purposes, such as classifying, modeling, and detecting surface deformation. To examine the future impact based on information from the past, we utilized a synthetic interferogram with the Okada model and transferred it to a machine learning algorithm. The synthetic interferogram was formed based on Sentinel-1 C-band satellite data to simulate the deformation phases. The orbital errors, the topographical data errors, and the atmospheric effect were also simulated and added to the synthetic interferogram to enrich the learning input. A convolutional neural network (CNN) trained with the unwrapped simulated interferogram data and its performance was evaluated. Our proposed method exhibits the capability to detect volcanic activity's deformation patterns with synthetic interferogram data. The results show that an overall accuracy of more than 80% was achieved using the CNN algorithms on the validation dataset. This study is the first to use machine learning approaches for detecting prehistorical volcanic deformation and demonstrates potential techniques for developing an approach based on satellite imagery. In addition, this study has introduced the possibility of developing a rapid detection of surface deformation using InSAR data based on a machine learning approach.