ANALYSIS OF LOGARITHMIC, EXPONENTIAL, AND COMBINATION FUNCTIONS FOR POSTSEISMIC DEFORMATION PATTERNS OF THE 2018 PALU EARTHQUAKE UTILIZE GPS DATA 2019-2022
Indonesia has a high seismic frequency with earthquake activities, as seen in the case of the Palu earthquake on September 28, 2018. The earthquake was associated with the activity on the sinistral strike slip (left-lateral) Palu-Koro fault, resulting in significant losses such as a death toll of...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/76774 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Indonesia has a high seismic frequency with earthquake activities, as seen in the
case of the Palu earthquake on September 28, 2018. The earthquake was associated
with the activity on the sinistral strike slip (left-lateral) Palu-Koro fault, resulting
in significant losses such as a death toll of 2,037 people, damage to infrastructure
including 67,310 houses, 99 places of worship, and 20 healthcare facilities.
Therefore, mitigation efforts are needed to minimize future natural disaster losses,
such as monitoring using GPS. The research utilized GPS data recordings from
2019 to 2022, consisting of displacement measurements from 10 stations scattered
around the Palu-Koro fault. In the first stage, the GPS data recordings from each
station were processed to obtain information such as displacement and the change
in displacement over time (displacement vector). Based on the processing of GPS
data, the displacement vector values and directions were obtained, indicating
active movement on the eastern part of the Palu-Koro fault towards the northwest.
Subsequently, five types of modeling were conducted using single logarithmic
functions, single exponential functions, and combined functions that could
represent the post-earthquake deformation of the Palu earthquake in 2018 in the
future. In the modeling process, a calculations process was performed to obtain the
values of unknown parameters, namely offset and amplitude related to decay time,
and the optimal decay time through iteration. The obtained optimal decay times
were 2459 days (?log1.1), 3148 days (?exp2.1), 1055 days (?log3.1), 2367 days (?exp3.1),
1531 days (?log4.1), 1261 days (?log4.2), 557 days (?exp4.1), 5530 days (?log5.1), 4540
days (?exp5.1), and 2889 days (?exp5.2). Furthermore, an evaluation of the five models
was conducted, resulting in model 1 and model 2 as the best models based on
amplitude decay and the root mean square error (RMSE) values of 0.70038 mm
and 0.70044 mm, respectively |
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