Inversi Data Gravitasi untuk Menentukan Basement Relief Menggunakan Algoritma Generalized Normal Distribution Optimization

Faradila, Fany (2023) Inversi Data Gravitasi untuk Menentukan Basement Relief Menggunakan Algoritma Generalized Normal Distribution Optimization. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Basement relief dapat dihasilkan dari proses inversi data gravitasi. Inversi dapat dilakukan melalui metode optimasi lokal dan optimasi global. Algoritma Generalized Normal Distribution Optimization (GNDO) merupakan salah satu metode optimasi global yang tidak membutuhkan tuning parameter. Penelitian ini dilakukan untuk mengetahui performa algoritma GNDO dalam menentukan basement relief dari data gravitasi sintetik serta data pengukuran. Kedalaman basement yang dihasilkan dari inversi data gravitasi sintetik tanpa noise maupun dengan Gaussian noise 10% menunjukkan bahwa statistik dari Posterior Distribution Model (PDM) yang dihasilkan algoritma GNDO mendekati parameter model yang sebenarnya. Selain itu, analisis Principal Component Analysis (PCA) menunjukkan bahwa parameter model yang memiliki fungsi objektif terbaik tidak dapat digunakan sebagai solusi pada proses inversi, khususnya untuk data yang terkontaminasi noise. Selanjutnya, algoritma GNDO diaplikasikan pada data gravitasi pengukuran yang terukur di Wilayah Sistem Graben Aegean, barat daya Turki, untuk profil 3 dan profil 4. Hasil data profil 3 menunjukkan kedekatan antara nilai kedalaman hasil inversi menggunakan GNDO dan hasil analisis algoritma Differential Evolution (DE), namun kedua nilai tersebut berbeda dengan nilai kedalaman hasil pengukuran sumur bor. Sementara itu, pada data profil 4, kedalaman basement hasil inversi GNDO menunjukkan kesesuaian yang lebih baik dengan kondisi sumur bor dibandingkan dengan hasil inversi DE. Dengan demikian, dapat diketahui bahwa algoritma GNDO memiliki performa yang baik dalam dalam menentukan basement relief melalui inversi data gravitasi.
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Basement relief can be obtained from the inversion of gravity data. Inversion can be performed through local optimization and global optimization methods. Generalized Normal Distribution Optimization (GNDO) algorithm is one of the global optimization methods that does not require parameter tuning. This study aims to determine the performance of the GNDO algorithm in determining basement relief from synthetic gravity data and measurement data. The basement depths generated from the inversion of synthetic gravity data without noise and with 10% Gaussian noise show that the statistics of the Posterior Distribution Model (PDM) generated by the GNDO algorithm are close to the actual model parameters. Moreover, Principal Component Analysis (PCA) analysis shows that the model parameters that have the best objective function cannot be used as a solution in the inversion process, especially for noise-contaminated data. Next, the GNDO algorithm was applied to gravity data measured in the Aegean Graben System, southwest Turkey, for profile 3 and profile 4. The results of profile 3 data show the closeness between the depth values of inversion results using GNDO and the results of Differential Evolution (DE) algorithm analysis, but both values are different from the depth values of borehole measurements. Meanwhile, in the profile 4 data, the basement depth of the GNDO inversion results showed better agreement with the borehole conditions than the DE inversion results. Thus, it can be seen that the GNDO algorithm has a good performance in determining basement relief through gravity data inversion.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Basement Relief, Data Gravitasi, GNDO, PCA, PDM, Gravity Data
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:	Fany Faradila
Date Deposited:	26 Oct 2023 07:38
Last Modified:	26 Oct 2023 07:38
URI:	http://repository.its.ac.id/id/eprint/102516

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