A Tsunami Generated by a Strike‐Slip Event: Constraints From GPS and SAR Data on the 2018 Palu Earthquake

Simons, Wim; Broerse, Taco; Shen, Lianjun; Kleptsova, O.; Nijholt, Nicolai; Hooper, Andrew; Pietrzak, Julie D.; Morishita, Yu; Naeije, Marc; Lhermitte, Stef; Herman, M. W.; Sarsito, Dina A.; Efendi, Joni; Sofian,; Govers, Rob; Vigny, Christophe; Abidin, Hasanuddin Z.; Pramono, Gatot H.; Nugroho, Cahyo; Visser, Pieter; Riva, Riccardo

doi:10.1029/2022jb024191

Cited by 9 publications

(4 citation statements)

References 143 publications

(378 reference statements)

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“…suggesting an antithetic relationship. The present-day kinematics of the Palu-Koro Faults shows the primary force direction controlling the movement of slip is at Northwest-Southeast [3,11]. The other fault mechanism present in the research is left-lateral strike-slip faults, evident on ID1, ID6, and ID7.…”

Section: Resultsmentioning

confidence: 71%

Imaging the Fault Plane Structure of Palu-Koro Fault from Earthquake Data Using Python

At-diena Putrie,

Husein

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Palu-Koro Fault is one of the active faults located in Sulawesi and positioned in the middle of the Island, dividing the west and east-southeast arms. Its long history of earthquakes provides sufficient focal mechanisms data to be analyzed. This research used seven focal mechanism data of shallow magnitude earthquakes surrounding the pull-apart basin area of the Palu-Koro Fault. The focal mechanism data used is secondary data obtained containing the information needed. This research aims to create a 3D model of the pull-apart basin displaying the geometries of the fault planes and providing a closer look at the fault system surrounding the bounding fault zones. Python script was made to classify the mechanism of each earthquake based on its correlation to the trend of the Palu-Koro Fault. Nodal planes are categorized as whether it is presumed to be the fault plane or the auxiliary plane of the focal mechanism. The result found that not all earthquake data was classified through the assumed parameter set. Only five of seven earthquake data managed to be classified, while the rest showed a different focal mechanism. It is found from the python script created revealed the presence of three fault types: normal fault, normal left lateral oblique fault, and left lateral strike-slip. The 3D fault plane interpolation showed two distinct dipping directions, suggesting their association with the basin’s bounding fault, either the west or the east side. From the gathered information, previous research, and geological interpretation, the earthquake data can be concluded into three fault associations to the bounding fault: synthetic extension fault, antithetic extension fault, and sinistral strike-slip fault.

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Section: Resultsmentioning

confidence: 71%

Imaging the Fault Plane Structure of Palu-Koro Fault from Earthquake Data Using Python

At-diena Putrie,

Husein

2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…Both have the opposite direction to Fault 1 and the magnitude of the dip of the three faults is also relatively similar, which is around 85°. The dip value is near-vertical [37].…”

Section: Resultsmentioning

confidence: 91%

“…After the 2018 Palu events, the fault zone is dominated by normal and strike slip faults with NNE-SSW trend [36]. The fault branches into multiple semi-parallel fault segments toward shallower depth which the displacement is generally larger east of the fault [37].…”

Section: Resultsmentioning

confidence: 99%

Identification of Geological Structures in Sigi Regency, Central Sulawesi Based on Derivative Analysis of Gravity Data

Rosid¹

2023

GEOMATE

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Sigi Regency is part of the tectonic zone in Central Sulawesi which is crossed by an active transform fault, the Palu Koro Fault. The northern region of Sigi Regency has a depression zone that forms a graben by a couple of Palu Koro Faults. The study area is prone to earthquakes and has fairly high geothermal potential. Therefore, it is necessary to control the dynamics of the structure here to mitigate earthquake disasters that may occur and may also affect the surrounding geothermal system. To identify the presence and characterization of the fault structures, derivative analysis of gravity data is used, including analysis of FHD (First Horizontal Derivative), SVD (Second Vertical Derivative), and MS-SVD (Multi Scale-Second Vertical Derivative). From this analysis, 4 normal oblique fractures and 1 normal fracture were identified. Faults derived from the derivative analysis have greatly improved the fault mapping reported on geological maps, including the shifting fault position from the geological map. And significant improvement was found on a shear fault causing the fault to be separated into 2 segments, changing the direction of the strike fault, and generating a new fault due to the created weak zone.

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“…For the above studies, the main categories of methods used to obtain 3D displacements by integrating InSAR and the GNSS can be summarized as follows: (1) GNSS points were used as constraining data to remove motion or elevation errors from multiple-time InSAR (MTI) techniques. At least three different MTI observations were used to measure the LOS and azimuth directions, such as ascending and descending, to transform them into 3D displacements [22][23][24]. (2) The least squares method was used to interpolate the GNSS into the same spatial resolution as the MTI data to solve the 3D displacement, considering and balancing possible errors in the MTI observations [25,26].…”

Section: Introductionmentioning

confidence: 99%

Integrated PSInSAR and GNSS for 3D Displacement in the Wudongde Area

Huang,

Du,

Jin

et al. 2024

Land

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The major limitation of persistent scatterer interferometric synthetic aperture radar (PSInSAR) is that it detects only one- or two-dimensional displacements, such as those in the line of sight (LOS) and azimuth directions, by repeat-pass SAR observations. Three-dimensional (3D) displacement reflects the actual sliding surface and failure mechanism of a slope. To transform LOS deformation into a reliable 3D displacement, a new approach for obtaining the 3D displacement is proposed herein based on the slope deformation (Dslope). First, the deformation value calculated using the Global Navigation Satellite System (GNSS) as a constraint is used to eliminate the residual deformation of PSInSAR. Then, Dslope is obtained from the relationship between DLOS and the slope angle extracted from the digital elevation model (DEM). Finally, according to the geometric relationship between Dslope and DLOS, a novel approach for calculating 3D displacement is proposed. When comparing the 3D displacement extracted by the proposed method and that from GNSS data in Jinpingzi landslide, the root-mean-square error (RMSE) values were ±2.0 mm, ±2.8 mm, and ±2.6 mm in the vertical, north, and east directions, respectively. The proposed method shows high accuracy in 3D displacement calculation, which can help to determine the failure mechanism of a landslide. This method can be widely used in landslide monitoring in wide areas.

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A Tsunami Generated by a Strike‐Slip Event: Constraints From GPS and SAR Data on the 2018 Palu Earthquake

Cited by 9 publications

References 143 publications

Imaging the Fault Plane Structure of Palu-Koro Fault from Earthquake Data Using Python

Imaging the Fault Plane Structure of Palu-Koro Fault from Earthquake Data Using Python

Identification of Geological Structures in Sigi Regency, Central Sulawesi Based on Derivative Analysis of Gravity Data

Integrated PSInSAR and GNSS for 3D Displacement in the Wudongde Area

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