Abstract
Monitoring active volcanoes is necessary to analyze their current status to pose a mitigation hazard. Mount Melbourne is an active volcano that has erupted in the past, and future eruptions are possible. This condition could threaten future eruptions, particularly near scientific bases. Jang Bogo, a South Korean research station, is located only 30 km from the summit and could be affected by significant ash fallout in case of an explosive eruption. This condition leads to the necessity of observing Mount Melbourne’s activity frequently. This study used Sentinel-1 SAR data acquired from 2017 to 2024 to monitor the volcanic activity of Mount Melbourne by utilizing InSAR multitemporal time-series analysis implementing the improved combined scatterers interferometry with optimized point scatterers (ICOPS) method. The ICOPS method combined persistent scatterer (PS) and distributed scatterer (DS) with measurement point (MP) optimization based on convolutional neural network (CNN) and optimized hot spot analysis (OHSA). The ICOPS measurement results maintain reliable MP along the Mount Melbourne summit and around Jang Bogo station. The absence of GPS stations around these two areas makes it difficult to validate the result with the ground truth measurement, so the comparison with another method, small baseline (SBAS) measurement, is made to evaluate the reliability of the ICOPS measurement points. The comparison between the MP from ICOPS and the SBAS methods shows a good correlation with R2 of about 0.8134 in the Melbourne area and 0.8678 in the Jang Bogo area. The selected time-series plot around the summit of Mount Melbourne and the Jang Bogo area shows a stable trend of surface deformation. Thus, a total accumulated deformation of around 0.82 cm and an average deformation of about 0.10 cm/year was found around Mount Melbourne. Meanwhile, the Jang Bogo area exhibits a total deformation of about 0.15 cm with an average deformation of about 0.02. Overall, this research is a preliminary study of the ability of the ICOPS algorithm to monitor volcanic activity in snow-covered areas.
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References
Albawi, S., Mohammed, T.A., and Al-Zawi, S., 2018, Understanding of a convolutional neural network. Proceedings of 2017 International Conference on Engineering and Technology, Antalya, Turkey, Jan. 21–23, p. 1–6. https://doi.org/10.1109/ICEngTechnol.2017.8308186
Albino, F., Biggs, J., Yu, C., and Li, Z., 2020, Automated methods for detecting volcanic deformation using sentinel-1 InSAR time series illustrated by the 2017–2018 unrest at Agung, Indonesia. Journal of Geophysical Research: Solid Earth, 125. https://doi.org/10.1029/2019JB017908
Barletta, V.R., Bevis, M., Smith, B.E., Wilson, T., Brown, A., Bordoni, A., Willis, M., Khan, S.A., Rovira-Navarro, M., Dalziel, I., Smalley, R., Kendrick, E., Konfal, S., Caccamise, D.J., Aster, R.C., Nyblade, A., and Wiens, D.A., 2018, Observed rapid bedrock uplift in Amundsen Sea Embayment promotes ice-sheet stability. Science, 360, 1335–1339. https://doi.org/10.1126/science.aao1447
Bürgmann, R., Rosen, P.A., and Fielding, E.J., 2000, Synthetic aperture radar interferometry to measure earth’s surface topography and its deformation. Annual Review of Earth and Planetary Sciences, 28, 169–209.
Chauhan, R., Ghanshala, K.K., and Joshi, R.C., 2018, Convolutional neural network (CNN) for image detection and recognition. 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), Jalandhar, India, Dec. 15–17, p. 278–282. https://doi.org/10.1109/ICSCCC.2018.8703316
Del Carlo, P., Di Roberto, A., Di Vincenzo, G., Re, G., Albert, P.G., Nazzari, M., Smith, V.C., and Cannata, A., 2022, Tephrostratigraphy of proximal pyroclastic sequences at Mount Melbourne (northern Victoria Land, Antarctica): insights into the volcanic activity since the last glacial period. Journal of Volcanology and Geothermal Research, 422. https://doi.org/10.1016/j.jvolgeores.2021.107457
Del Carlo, P., Di Roberto, A., Re, G., Albert, P.G., Smith, V.C., Giudice, G., Larocca, G., Scateni, B., and Cannata, A., 2023, Historical explosive activity of Mount Melbourne Volcanic Field (Antarctica) revealed by englacial tephra deposits. Bulletin of Volcanology, 85. https://doi.org/10.1007/s00445-023-01651-2
Dzurisin, D., 2003, A comprehensive approach to monitoring volcano deformation as a window on the eruption cycle. Reviews of Geophysics, 41. https://doi.org/10.1029/2001RG000107
Esmaeili, M. and Motagh, M., 2016, Improved persistent scatterer analysis using amplitude dispersion index optimization of dual polarimetry data. ISPRS Journal of Photogrammetry and Remote Sensing, 117, 108–114. https://doi.org/10.1016/j.isprsjprs.2016.03.018
Fadhillah, M.F., Achmad, A.R., and Lee, C.-W., 2022, Improved combined scatterers interferometry with optimized point scatterers (ICOPS) for interferometric synthetic aperture radar (InSAR) time-series analysis. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–14. https://doi.org/10.1109/TGRS.2021.3138763
Fadhillah, M.F., Hakim, W.L., Lee, S.K., Lee, K.J., Lee, S.J., Chae, S.H., Lee, H., and Lee, C.W., 2024, Multitemporal analysis of land subsidence induced by open-pit mining activity using improved combined scatterer interferometry with deep learning algorithm optimization. Scientific Reports, 14. https://doi.org/10.1038/s41598-024-56347-0
Gambino, S., Aloisi, M., Falzone, G., and Ferro, A., 2016, Tilt signals at Mount Melbourne, Antarctica: evidence of a shallow volcanic source. Polar Research, 35. https://doi.org/10.3402/polar.v35.28269
Geyer, A., Di Roberto, A., Smellie, J.L., Van Wyk de Vries, M., Panter, K.S., Martin, A.P., Cooper, J.R., Young, D., Pompilio, M., Kyle, P.R., and Blankenship, D., 2023, Volcanism in Antarctica: an assessment of the present state of research and future directions. Journal of Volcanology and Geothermal Research, 444. https://doi.org/10.1016/j.jvolgeores.2023.107941
Giordano, G., Lucci, F., Phillips, D., Cozzupoli, D., and Runci, V., 2012, Stratigraphy, geochronology and evolution of the Mt. Melbourne volcanic field (North Victoria Land, Antarctica). Bulletin of Volcanology, 74, 1985–2005. https://doi.org/10.1007/s00445-012-0643-8
Gordeev, E.I., Girina, O.A., Lupyan, E.A., Sorokin, A.A., Kramareva, L.S., Efremov, V.Yu., Kashnitskii, A.V., Uvarov, I.A., Burtsev, M.A., Romanova, I.M., Mel’nikov, D.V., Manevich, A.G., Korolev, S.P., and Verkhoturov, A.L., 2016, The VolSatView information system for monitoring the volcanic activity in Kamchatka and on the Kuril Islands. Journal of Volcanology and Seismology, 10, 382–394. https://doi.org/10.1134/S074204631606004X
Gündüz, H.İ., Yılmaztürk, F., and Orhan, O., 2023, An investigation of volcanic ground deformation using InSAR observations at Tendürek Volcano (Turkey). Applied Sciences, 13. https://doi.org/10.3390/app13116787
Hakim, W.L., Fadhillah, M.F., Lee, K.-J., Lee, S.-J., Chae, S.-H., and Lee, C.-W., 2023a, Land subsidence and groundwater storage assessment using ICOPS, GRACE, and susceptibility map** in Pekalongan, Indonesia. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–25. https://doi.org/10.1109/TGRS.2023.3324043
Hakim, W.L., Fadhillah, M.F., Park, S., Pradhan, B., Won, J.-S., and Lee, C.-W., 2023b, InSAR time-series analysis and susceptibility map** for land subsidence in Semarang, Indonesia using convolutional neural network and support vector regression. Remote Sensing of Environment, 287. https://doi.org/10.1016/j.rse.2023.113453
Hooper, A., 2008, A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches. Geophysical Research Letters, 35. https://doi.org/10.1029/2008GL034654
Jiang, M., Ding, X., Hanssen, R.F., Malhotra, R., and Chang, L., 2015, Fast statistically homogeneous pixel selection for covariance matrix estimation for multitemporal InSAR. IEEE Transactions on Geoscience and Remote Sensing, 53, 1213–1224. https://doi.org/10.1109/TGRS.2014.2336237
Jiang, M., Ding, X., and Li, Z., 2014a, Hybrid approach for unbiased coherence estimation for multitemporal InSAR. IEEE Transactions on Geoscience and Remote Sensing, 52, 2459–2473. https://doi.org/10.1109/TGRS.2013.2261996
Jiang, M., Ding, X., Li, Z., Tian, X., Wang, C., and Zhu, W., 2014b, InSAR coherence estimation for small data sets and its impact on temporal decorrelation extraction. IEEE Transactions on Geoscience and Remote Sensing, 52, 6584–6596. https://doi.org/10.1109/TGRS.2014.2298408
Jiang, M. and Guarnieri, A.M., 2020, Distributed scatterer interferometry with the refinement of spatiotemporal coherence. IEEE Transactions on Geoscience and Remote Sensing, 58, 3977–3987. https://doi.org/10.1109/TGRS.2019.2960007
Jiang, M., Yong, B., Tian, X., Malhotra, R., Hu, R., Li, Z., Yu, Z., and Zhang, X., 2017, The potential of more accurate InSAR covariance matrix estimation for land cover map**. ISPRS Journal of Photogrammetry and Remote Sensing, 126, 120–128. https://doi.org/10.1016/j.isprsjprs.2017.02.009
Keys, J.R., McIntosh, W.C., and Kyle, P.R., 1983, Volcanic activity of Mount Melbourne, northern Victoria Land. Antarctic Journal of the United States, 18, 10–11.
Kim, P., 2017, Convolutional neural network. In: Kim, P. (ed.), MATLAB Deep Learning. Apress, Berkeley, USA, p. 121–147. https://doi.org/10.1007/978-1-4842-2845-6_6
Lazecký, M., Spaans, K., González, P.J., Maghsoudi, Y., Morishita, Y., Albino, F., Elliott, J., Greenall, N., Hatton, E., Hooper, A., Juncu, D., McDougall, A., Walters, R.J., Watson, C.S., Weiss, J.R., and Wright, T.J., 2020, LiCSAR: an automatic InSAR tool for measuring and monitoring tectonic and volcanic activity. Remote Sensing, 12. https://doi.org/10.3390/rs12152430
Lee, M.J. and Lee, J.I., 2017, Holocene eruptive history of Mt. Melbourne volcano, Antarctica: insights from tephra layers recorded from Talos Dome ice core. Journal of the Geological Society of Korea, 53, 509–519. https://doi.org/10.14770/jgsk.2017.53.4.509
Lu, P., Bai, S., Tofani, V., and Casagli, N., 2019, Landslides detection through optimized hot spot analysis on persistent scatterers and distributed scatterers. ISPRS Journal of Photogrammetry and Remote Sensing, 156, 147–159. https://doi.org/10.1016/j.isprsjprs.2019.08.004
Lyon, G.L. and Giggenbach, W.F., 1974, Geothermal activity in Victoria Land, Antarctica. New Zealand Journal of Geology and Geophysics, 17, 511–521. https://doi.org/10.1080/00288306.1973.10421578
Mark, H.F., Wiens, D.A., Ivins, E.R., Richter, A., Ben Mansour, W., Magnani, M.B., Marderwald, E., Adaros, R., and Barrientos, S., 2022, Lithospheric erosion in the patagonian slab window, and implications for glacial isostasy. Geophysical Research Letter, 49. https://doi.org/10.1029/2021GL096863t
Milisavljević, N., Closson, D., Holecz, F., Collivignarelli, F., and Pasquali, P., 2015, An approach for detecting changes related to natural disasters using synthetic aperture radar data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-7/W3, 819–826. https://doi.org/10.5194/isprsarchives-XL-7-W3-819-2015
Mirjalili, S., Mirjalili, S.M., and Lewis, A., 2014, Grey wolf optimizer. Advances in Engineering Software, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Nathan, S. and Schulte, F.J., 1967, Recent thermal and volcanic activity on Mount Melbourne, northern Victoria Land, Antarctica. New Zealand Journal of Geology and Geophysics, 10, 422–430. https://doi.org/10.1080/00288306.1967.10426746
Ord, J.K. and Getis, A., 1995, Local spatial autocorrelation statistics: distributional issues and an application. Geographical Analysis, 27, 286–306. https://doi.org/10.1111/j.1538-4632.1995.tb00912.x
Osmanoğlu, B., Sunar, F., Wdowinski, S., and Cabral-Cano, E., 2016, Time series analysis of InSAR data: methods and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 90–102. https://doi.org/10.1016/j.isprsjprs.2015.10.003
Palomaki, R.T. and Sproles, E.A., 2023, Assessment of L-band InSAR snow estimation techniques over a shallow, heterogeneous prairie snowpack. Remote Sensing of Environment, 296. https://doi.org/10.1016/j.rse.2023.113744
Park, Y., Yoo, H.J., Lee, W.S., Lee, J., Kim, Y., Lee, S.-H., Shin, D., and Park, H., 2014, Deployment and performance of a broadband seismic network near the new Korean Jang Bogo Research Station, Terra Nova Bay, East Antarctica. Seismological Research Letters, 85, 1341–1347. https://doi.org/10.1785/0220140107
Rao, Y.S., 2011, Synthetic aperture radar (SAR) interferometry for glacier movement studies. In: Singh, V.P., Singh, P., and Haritashya, U.K. (eds.), Encyclopedia of Snow, Ice and Glaciers. Encyclopedia of Earth Sciences Series, Springer, Dordrecht, Netherlands, p. 1133–1142. https://doi.org/10.1007/978-90-481-2642-2_618
Rezaei, H., Bozorg-Haddad, O., and Chu, X., 2018, Grey wolf optimization (GWO) algorithm. In: Bozorg-Haddad, O. (ed.), Advanced Optimization by Nature-Inspired Algorithms. Studies in Computational Intelligence, Springer, Singapore, 720, p. 81–91. https://doi.org/10.1007/978-981-10-5221-7_9
Tampubolon, W., Reinhardt, W., and Behr, F.J., 2023, Investigations on InSAR data processing standard for volcano island monitoring in Indonesia. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-1/W2-2023, 1281–1287. https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1281-2023
Wang, X., Aoki, Y., and Chen, J., 2019, Surface deformation of Asama volcano, Japan, detected by time series InSAR combining persistent and distributed scatterers, 2014–2018. Earth, Planets and Space, 71. https://doi.org/10.1186/s40623-019-1104-9
Yunjun, Z., Fattahi, H., and Amelung, F., 2019, Small baseline InSAR time series analysis: unwrap** error correction and noise reduction. Computers & Geosciences, 133. https://doi.org/10.1016/j.cageo.2019.104331
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This work was supported by the Korea Polar Research Institute and was funded by the Ministry of Oceans and Fisheries (KOPRI, PE22900)
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Hakim, W.L., Sakina, R.N., Fadhillah, M.F. et al. Monitoring activity in Mount Melbourne, Antarctica, by multi-temporal SAR interferometry based on the ICOPS algorithm. Geosci J (2024). https://doi.org/10.1007/s12303-024-0021-5
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DOI: https://doi.org/10.1007/s12303-024-0021-5