Generalized Additive Models untuk Cauchy Cluster Process (Studi Kasus: Data Gempabumi di Sulawesi-Maluku)

Mukhti, Tessy Octavia (2021) Generalized Additive Models untuk Cauchy Cluster Process (Studi Kasus: Data Gempabumi di Sulawesi-Maluku). Masters thesis, Institut Teknologi Sepuluh Nopember.

Text 06211850012007-Master_Thesis.pdf - Accepted Version Restricted to Repository staff only until 1 April 2023. Download (5MB) | Request a copy

Abstract

Pemodelan gempabumi berdasarkan spatial point process telah sering dilakukan, mulai dari pemodelan fungsi momen pertama (intensity) dan fungsi momen kedua (Pair Correlation Function atau K-function). Beberapa penelitian terdahulu melakukan pemodelan yang hanya bergantung pada proses stationer dan penelitian lainnya yang melakukan pemodelan melibatkan proses tidak stationer hanya mengandalkan model intensity log-linier. Oleh sebab itu, penelitian ini menggunakan pendekatan Generalized Additive Models (GAMs) untuk memodelkan fungsi intensitas dari gempabumi dengan magnitude 5 SR atau lebih, dimana studi kasus yang diambil yaitu gempabumi di Sulawesi-Maluku dengan melibatkan faktor geologis yaitu jarak episentrum gempabumi terhadap sesar aktif, zona subduksi, dan gunung api terdekat. Metode yang digunakan untuk estimasi parameter pada model intensitas menggunakan penalized iteratively re-weighted least squares (PIRLS). Selain itu, untuk menangkap efek clustering pada gempabumi, penelitian ini melibatkan model Cauchy cluster process, dimana metode yang digunakan untuk estimasi parameter cluster adalah second order composite likelihood. Hasil analisis eksplorasi data menunjukkan bahwa gempabumi di Sulawesi-Maluku menyebar secara inhomogen dan cenderung berpola cluster. Hasil pemodelan intensitas dengan model inhomogeneous Cauchy cluster process dengan pendekatan GAMs menunjukkan bahwa covariate yang signifikan memicu gempabumi M≥5 SR di Sulawesi-Maluku adalah jarak titik episentrum gempabumi ke subduksi dan gunung api terdekat, dan estimasi parameter cluster menunjukkan bahwa terdapat sekitar 78 titik mainshock dengan scale persebaran aftershock di sekitar mainshock adalah 15,2 km.
====================================================================================================
Cluster-type point process models are the popular model for modeling the arrangement of locations of earthquake occurrences. When a spatial trend is presence due to e.g. geological factors, the log-linear intensity of the point process is often considered to exploit inhomogeneity due to such factors. However, this could be a major drawback, especially in seismology when the relation between the intensity of earthquake occurrences and environmental covariates is not log-linear. In this paper, we consider the Cauchy cluster process with a log-additive intensity model to quantify two effects in modeling the distribution of locations of major earthquakes in Sulawesi-Maluku: (1) spatial trend due to geological covariates such as subduction zones, faults, and volcanoes and (2) clustering effect due to seismic activities. The Cauchy cluster process could detect the clustering effect even when the aftershocks are extremely distant to the mainshocks while log-additive intensity is a more flexible model to study inhomogeneity due to the environment. To estimate the parameters, we apply two-step estimation procedure, in the first step, we estimate the regression parameter corresponding to effects of the geological variables by maximum composite likelihood by involving penalized iteratively re-weighted least squares (PIRLS) technique, and in the second step, we obtain the cluster estimates by maximum second order composite likelihood. The results show that subduction and volcanoes are significant covariates that trigger earthquakes, the estimated mainshock intensity is around 78, and the aftershocks are distributed around it with a distance of 15.2 km due to mainshock activity.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Cauchy cluster process, Earthquake,Gempabumi, Generalized Additive Models, PIRLS, Second order composite likelihood
Subjects:	H Social Sciences > HA Statistics > HA30.6 Spatial analysis
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49101-(S2) Master Thesis
Depositing User:	Tessy Octavia Mukhti
Date Deposited:	12 Mar 2021 21:37
Last Modified:	12 Mar 2021 21:37
URI:	http://repository.its.ac.id/id/eprint/84180

Actions (login required)

View Item