Source parameters and slip distribution of the 2018 M 7.5 Palu, Indonesia earthquake estimated from space-based geodesy

He, Leye; Feng, Guangcai; Li, Zhiwei; Zhang, Feng; Gao, Hua; Wu, Xilin

doi:10.1016/j.tecto.2019.228216

Cited by 34 publications

(43 citation statements)

References 47 publications

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“…Other coseismic mechanisms, derived from geodetic observations, yield larger vertical seabed motions and, therefore, could be more tsunamigenic (e.g., ∼ 3 m just south of the Balaesang Peninsula, Figure 1A, Song et al, 2019;Fang et al, 2019;He et al, 2019). These, however, are not within Palu Bay but farther north and, hence, cannot explain the tsunami here, particularly the fast arrival of large waves that impacted Palu City (the timing of events and waves will be detailed later).…”

Section: Introductionmentioning

confidence: 95%

New High-Resolution Modeling of the 2018 Palu Tsunami, Based on Supershear Earthquake Mechanisms and Mapped Coastal Landslides, Supports a Dual Source

2021

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The Mw 7.5 earthquake that struck Central Sulawesi, Indonesia, on September 28, 2018, was rapidly followed by coastal landslides and destructive tsunami waves within Palu Bay. Here, we present new tsunami modeling that supports a dual source mechanism from the supershear strike-slip earthquake and coastal landslides. Up until now the tsunami mechanism: earthquake, coastal landslides, or a combination of both, has remained controversial, because published research has been inconclusive; with some studies explaining most observations from the earthquake and others the landslides. Major challenges are the numerous different earthquake source models used in tsunami modeling, and that landslide mechanisms have been hypothetical. Here, we simulate tsunami generation using three published earthquake models, alone and in combination with seven coastal landslides identified in earlier work and confirmed by field and bathymetric evidence which, from video evidence, produced significant waves. To generate and propagate the tsunamis, we use a combination of two wave models, the 3D non-hydrostatic model NHWAVE and the 2D Boussinesq model FUNWAVE-TVD. Both models are nonlinear and address the physics of wave frequency dispersion critical in modeling tsunamis from landslides, which here, in NHWAVE are modeled as granular material. Our combined, earthquake and coastal landslide, simulations recreate all observed tsunami runups, except those in the southeast of Palu Bay where they were most elevated (10.5 m), as well as observations made in video recordings and at the Pantoloan Port tide gauge located within Palu Bay. With regard to the timing of tsunami impact on the coast, results from the dual landslide/earthquake sources, particularly those using the supershear earthquake models are in good agreement with reconstructed time series at most locations. Our new work shows that an additional tsunami mechanism is also necessary to explain the elevated tsunami observations in the southeast of Palu Bay. Using partial information from bathymetric surveys in this area we show that an additional, submarine landslide here, when simulated with the other coastal slides, and the supershear earthquake mechanism better explains the observations. This supports the need for future marine geology work in this area.

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Section: Introductionmentioning

confidence: 95%

New High-Resolution Modeling of the 2018 Palu Tsunami, Based on Supershear Earthquake Mechanisms and Mapped Coastal Landslides, Supports a Dual Source

2021

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“…The Dingri earthquake is an example to validate the algorithm in dealing with the real deformation of earthquakes. In addition to the Dingri earthquake, we also tested several real earthquake cases with different magnitudes and types based on SQS and QS (2020/2/3 Chengdu Mw4.8 earthquake, 2009/4/6 L'Aquila Mw6.3 earthquake, and 2018/9/28 Palu Mw7.5 earthquake) (Supplementary Materials Figures S6-S8) [10,27]. The sampling parameters are shown in Supplementary Materials Table S1.…”

Section: Discussionmentioning

confidence: 99%

“…However, in the actual research, these extreme situations are rare. Usually, the co-seismic deformation obtained by InSAR is mainly co-seismic deformation, and it will be affected by local deformation, atmospheric error, or noise, resulting in local high deformation gradient region [10,20,24,[27][28][29] (Supplementary Materials Figures S6-S8). In these cases, SQS will have different degrees of improvement compared with QS.…”

Section: Discussionmentioning

confidence: 99%

An Improved Quadtree Sampling Method for InSAR Seismic Deformation Inversion

2021

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With the development of interferometric synthetic aperture radar (InSAR), the seismic deformation observation density increases sharply. Data down-sampling can effectively reduce the observation density and the computational cost for subsequent researches. Considering the saliency of the deformation field, we introduce a saliency-based quadtree algorithm for down-sampling (SQS). Three simulation experiments show that SQS can effectively distinguish the near-field and far-field deformation, as well as reduce the amount of observation, while keeping the detailed information of the main deformation near the fault. SQS can avoid the interference of far-field local deformation better than the traditional quadtree sampling algorithm (QS), thus obtaining better inversion results. We took the Dingri earthquake on 20 March 2020 as a case study to verify the advantages of SQS in dealing with real earthquake deformation. We obtained the co-seismic deformation from the ascending and descending Sentinel-1 for the Dingri earthquake, using QS and SQS for sampling and inversion separately. The results show the advantages of SQS in data volume reduction, observation distribution, anti-interference of local deformation, and inversion accuracy. Our preferred solution based on SQS shows that the Dingri earthquake was caused by a normal fault slip. The main slip area is 2–5.5 km deep with a maximum slip of 0.68 m. The estimated geodetic moment is 3.14 × 1017 Nm, corresponding to a magnitude of Mw5.63.

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“…Beberapa penelitian terkait model bidang patahan Gempa Palu 2018 telah dilakukan oleh beberapa peneliti menggunakan data satelit dengan teknik pengolahan data InSAR (Interferometric Synthetic Aperture Radar ) dan data teleseismik [5,14,8,4]. Beberapa penelitian tersebut juga menghitung perubahan stress Coulomb di patahan atau di luar patahan menggunakan satu model bidang patahan yang dibangun [8,15,16]. Hui dkk., 2018 [17] Segmentasi Patahan Palu Koro dengan tipe en-echelon dapat di temukan di bagian utara (Donggala) yang mengindikasikan adanya zona yang lebih kompleks dan struktur yang masih aktif [21].…”

Section: Pendahuluanunclassified

Tinjauan Perubahan Stress Coulomb Ko-Seismik Pada Sekuens Gempa Palu M 7,5 28 September 2018

Mardhatillah

Anggraini

Nukman³

2020

J. Fis. Indones.

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Pada tanggal 28 September 2018 Kota Palu dan sekitarnya diguncang gempa dengan kekuatan M 7,5. Gempa tersebut diambil dari bantuan gempa di mana tujuh diberikan memiliki kekuatan yang cukup besar (M> 4,5). Untuk memahami interaksi gempa utama dan gempa susulan, pada penelitian ini dilakukan analisis perubahan stres. Dengan menggunakan program Coulomb 3.3, perubahan stres Coulomb dihitung secara statistik berdasarkan model patahan sumber (gempa utama) dan perubahan sumber patahan penerima (gempa susulan). Dalam penelitian ini digunakan enam (6) model patahan sumber dari studio yang sudah ada sebelumnya yaitu berdasarkan pengolahan data InSAR, teleseismik, dan broadband, serta satu (1) model yang disediakan bidang patahan segmen tunggal dengan distribusi slip yang seragam. Berhubungan dengan stres Coulomb statistik dilakukan dengan mode yang ditentukan kesalahan sedangkan kesalahan penerima merupakan tujuh gempa susulan yang memiliki magnitudo signifikan (M> 4,5). Model baru yang dibangun memiliki perhitungan perubahan stres Coulomb statistik di nodal pesawat I menunjukkan empat dari tujuh gempa susulan (57%) dan nodal pesawat II enam dari tujuh gempa susulan (86%) berada pada pola merah (nilai stres Coulomb positif). Hasil perhitungan besarnya gempa susulan terletak pada pola merah () yaitu daerah dengan peningkatan stres atau dengan kata lain gempa susulan tersebut dipicu oleh gempa utama. Model patahan dengan dua segmen patahan yang terletak di daratan dan di laut, merupakan model bidang patahan yang terbaik yang dapat menjelaskan tentang gempa susulan . Dibandingkan dengan model lain, model ini memiliki persentase terbesar dari tujuh gempa susulan yang terjadi pada pola merah. Peta distribusi Peningkatan stres yang disebabkan oleh gempa utama adalah data penting untuk mitigasi bencana. Penelitian ini dapat memberikan informasi awal untuk memperbaiki daerah yang memiliki potensi menjadi tempat yang disetujui gempa di masa datang. Kata kunci: stres Coulomb, gempa Palu 2018, bahaya gempa

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Source parameters and slip distribution of the 2018 M 7.5 Palu, Indonesia earthquake estimated from space-based geodesy

Cited by 34 publications

References 47 publications

New High-Resolution Modeling of the 2018 Palu Tsunami, Based on Supershear Earthquake Mechanisms and Mapped Coastal Landslides, Supports a Dual Source

New High-Resolution Modeling of the 2018 Palu Tsunami, Based on Supershear Earthquake Mechanisms and Mapped Coastal Landslides, Supports a Dual Source

An Improved Quadtree Sampling Method for InSAR Seismic Deformation Inversion

Tinjauan Perubahan Stress Coulomb Ko-Seismik Pada Sekuens Gempa Palu M 7,5 28 September 2018

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