Estimasi Kedalaman Basement Melalui Inversi Data Gravitasi Menggunakan Algoritma Hybrid Rao

Pangastuti, Niken Rizka (2024) Estimasi Kedalaman Basement Melalui Inversi Data Gravitasi Menggunakan Algoritma Hybrid Rao. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Dalam geofisika, data pengukuran lapangan diolah untuk mendapatkan informasi karakter atau struktur batuan di bawah permukaan bumi melalui proses inversi data. Dalam melakukan proses inversi, algoritma yang efektif dan robust diperlukan agar proses inversi dilakukan secara akurat dan cepat. Dalam penelitian ini, inversi data menggunakan algoritma hybrid Rao diterapkan pada data gravitasi untuk mengestimasi kedalaman basement. Melalui penelitian ini, algoritma hybrid Rao diuji performanya dalam inversi data gravitasi sintetik (noise-free dan yang ditambah Gaussian noise 10%) dan observasi untuk mengestimasi kedalaman basement. Karena solusi dari inversi data geofisika umumnya non-unique dan keberadaan noise dalam data lapangan, penilaian ketidakpastian (uncertainty) dilakukan menggunakan posterior distribution model (PDM) dan principal component analysis (PCA). Berdasarkan kedua penilaian ketidakpastian parameter model yang dihasilkan, algoritma hybrid Rao akurat untuk menentukan kedalaman basement dari data gravitasi sintetik. Selanjutnya, inversi data gravitasi observasi dilakukan pada gravitasi residual dari daerah Aegean Graben, Anatolia Barat, Turki. Hasil inversi menggunakan algoritma hybrid Rao dibandingkan dengan hasil penelitian sebelumnya yang menggunakan algoritma differential evolution (DE) dan data sumur bor. Hasil estimasi kedalaman basement dari inversi data Profil 3 di lokasi sumur bor bernilai 0,7546± 0,0082 km. Adapun hasil inversi dengan algoritma DE bernilai 0,7418 km. Hasil inversi kedua algoritma tidak berdekatan dengan data kedalaman basement dari sumur bor, yaitu 0,8800 km. Adapun pada Profil 4, inversi dengan algoritma hybrid Rao menghasilkan estimasi nilai kedalaman basement sebesar 0,7038 ± 0,0177 km. Nilai ini sangat mendekati data kedalaman basement dari informasi sumur bor yang bernilai 0,7300 km. Adapun hasil inversi dengan algoritma DE bernilai 0,6088 km. Dari hasil ini, dapat disimpulkan bahwa inversi data gravitasi menggunakan algoritma hybrid Rao akurat untuk estimasi kedalaman basement.
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In geophysics, field measurement data is processed to obtain information on the character or structure of rocks below the earth's surface through a data inversion process. In carrying out the inversion process, an effective and robust algorithm is needed so that the inversion process is carried out accurately and quickly. In this research, data inversion using the hybrid Rao algorithm is applied to gravity data to estimate basement depth. Through this research, the performance of the hybrid Rao algorithm was tested in the inversion of synthetic gravity data (noise-free and with 10% Gaussian noise) and observations to estimate basement depth. Because the solution from geophysical data inversion is generally non-unique and the presence of noise in field data, uncertainty assessment is carried out using posterior distribution models (PDM) and principal component analysis (PCA). Based on both uncertainty assessments of the resulting model parameters, the hybrid Rao algorithm is accurate for determining basement depth from synthetic gravity data. Next, inversion of observational gravity data was carried out on residual gravity from the Aegean Graben area, West Anatolia, Türkiye. The inversion results using the hybrid Rao algorithm were compared with the results of previous research using the differential evolution (DE) algorithm and drilled well data. The estimated basement depth from Profile 3 data inversion at the drilled well location is 0.7546 ± 0.0082 km. The inversion results with the DE algorithm are worth 0.7418 km. The inversion results of the two algorithms are not close to the basement depth data from the drilled well, which is 0.8800 km. As for Profile 4, inversion with the hybrid Rao algorithm produces an estimated basement depth value of 0.7038 ± 0.0177 km. This value is very close to the basement depth data from drilled well information which is 0.7300 km. The inversion results with the DE algorithm are worth 0.6088 km. From these results, it can be concluded that gravity data inversion using the hybrid Rao algorithm is accurate for estimating basement depth.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Algoritma GNDO, Anomali Gravitasi, Basement Relief, Cekungan Sedimen, Posterior Distribution Model, Basement Relief, GNDO Algorithm, Gravity Anomaly, Sedimentary Basin
Subjects:	Q Science > QA Mathematics > QA9.58 Algorithms Q Science > QC Physics > QC111 Density and specific gravity Q Science > QE Geology > QE471 Sedimentary rocks. Sedimentology
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Physics > 45201-(S1) Undergraduate Thesis
Depositing User:	Niken Rizka Pangastuti
Date Deposited:	20 Aug 2024 08:35
Last Modified:	20 Aug 2024 08:35
URI:	http://repository.its.ac.id/id/eprint/112297

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