Estimasi Basement Relief Menggunakan Optimasi Algoritma Comprehensive Learning Jaya Berbasis Data Gravitasi

Arifin, Widya Nurul (2023) Estimasi Basement Relief Menggunakan Optimasi Algoritma Comprehensive Learning Jaya Berbasis Data Gravitasi. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Metode gravitasi dapat digunakan untuk mengidentifikasi basement relief. Basement relief dapat digunakan untuk mengetahui letak patahan aktif dan daerah rawan gempa bumi. Penelitian ini dilakukan untuk mengetahui performa algoritma Comprehensive Learning Jaya (CLJaya) dalam inversi data gravitasi sintetik (tanpa noise dan dengan Gaussian noise 10%) dalam menentukan kedalaman basement relief. Fungsi topografi yang dihasilkan dari analisis PCA (Principal Component Analysis) menunjukkan bahwa model dengan fungsi objektif terkecil seringkali tidak dapat digunakan untuk solusi inversi pada data yang terkontaminasi noise. Dengan demikian, algoritma CLJaya digunakan untuk menghasilkan PDM (Posterior Distribution Model) agar dapat mengetahui ketidakpastian parameter model sebagai solusi inversi. Statistik PDM yang dihasilkan dari inversi data gravitasi sintetik menggunakan CLJaya menunjukkan bahwa rata-rata dari PDM sesuai dengan parameter model yang sebenarnya. Selain itu, standar deviasi dari PDM mengindikasikan bahwa ketidakpastian parameter model hasil inversi CLJaya rendah. Selanjutnya, algoritma CLJaya diterapkan untuk inversi data gravitasi pengukuran dari daerah Anatolia Barat, Turki. Hasil inversi CLJaya menunjukkan bahwa statistik PDM kedalaman basement yang hampir sama dengan hasil inversi menggunakan algoritma DE (Differential Evolution) serta relatif dekat dengan data sumur bor.
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Gravity method can be used to identify basement relief. Basement relief can be used to determine the location of active faults and earthquake-prone areas. This study was conducted to determine the performance of the Comprehensive Learning Jaya (CLJaya) algorithm in the inversion of synthetic gravity data (without noise and with 10% Gaussian noise) to determine the depth of basement relief. The topographic functions generated from PCA (Principal Component Analysis) analysis show that the model with the smallest objective function often cannot be used for inversion solutions on noise-contaminated data. Thus, the CLJaya algorithm is used to generate a PDM (Posterior Distribution Model) in order to determine the uncertainty of the model parameters as an inversion solution. The PDM statistics generated from the inversion of synthetic gravity data using CLJaya show that the mean of the PDM matches the true model parameters. In addition, the standard deviation of the PDM indicates that the uncertainty of the model parameters resulting from the CLJaya inversion is low. Furthermore, the CLJaya algorithm was applied to the inversion of measured gravity data from Western Anatolia, Turkey. The CLJaya inversion results show that the PDM statistics of basement depth are almost the same as the inversion results using DE (Differential Evolution) algorithm and relatively close to the borehole data.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Algoritma CLJaya, Data Gravitasi, Kedalaman Basement, Ketidakpastian Parameter Model,Basement Depth, CLJaya Algorithm, Gravity Data, Model Parameter Uncertainty, PDM.
Subjects:	Q Science > QA Mathematics > QA76 Computer software Q Science > QA Mathematics > QA76.9 Computer algorithms. Virtual Reality. Computer simulation. Q Science > QC Physics Q Science > QC Physics > QC111 Density and specific gravity T Technology > TN Mining engineering. Metallurgy > TN269 Prospecting--Geophysical methods
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Physics > 45201-(S1) Undergraduate Thesis
Depositing User:	Widya Nurul Arifin
Date Deposited:	07 Aug 2023 04:32
Last Modified:	07 Aug 2023 04:32
URI:	http://repository.its.ac.id/id/eprint/102527

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