Spatiotemporal postseismic deformation due to the 2018 Palu-Donggala earthquake revealed the relative importance of viscoelastic relaxation and the afterslip distribution estimated from geodetic observations
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Irma Yusiyanti
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Sidik Tri Wibowo
Abstract
The Palu region has attracted attention due to significant seismic activity, including a destructive earthquake in 2018. This study aims to investigate postseismic deformation following the 2018 Palu-Donggala earthquake using the Interferometric Synthetic Aperture Radar (InSAR) technique. We utilized Global Navigation Satellite System (GNSS) data to obtain the viscoelastic mechanism decay time. Therefore, we subtracted the viscoelastic relaxation signal to obtain spatiotemporal afterslip distribution inferred from the 2.5D InSAR observation based on the Steepest Descent Method (SDM). Our results suggest the viscoelastic mechanism is indispensable, with an optimal decay time of about 2 years after the earthquake. Based on the chi-square statistical test, the spatiotemporal afterslip model can explain the observation with good qualification. We found anomalies indicating non-decreasing slip, which is likely due to several factors, such as the presence of Slow Slip Events (SSE). These findings provide valuable insights regarding the potential for future earthquakes and have significant implications for disaster risk assessment in the Palu region and its surroundings.
Acknowledgments
We are indebted to the anonymous reviewers and editors for constructive and valuable comments to improve this manuscript. The Generic Mapping Tools [47] generated most figures. Time series InSAR was generated by LiCSBAS, with support from COMET and LiCSAR [26]. The Steepest Descent Method (SDM) was employed to estimate afterslip distribution [27], [28].
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The author(s) have (has) accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The author(s) state(s) no conflict of interest.
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Research funding: This study was supported by Universitas Gadjah Mada through the 2023 RTA Project.
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Data availability: The raw data can be obtained on request from the corresponding author.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/jag-2024-0017).
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