Estimasi Parameter Sumber Anomali Self-Potential pada Sistem Hidrotermal Vulkanik Menggunakan Generalized Particle Swarm Optimization

Rachmawati, Dhea (2026) Estimasi Parameter Sumber Anomali Self-Potential pada Sistem Hidrotermal Vulkanik Menggunakan Generalized Particle Swarm Optimization. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Inversi data Self-Potential pada sistem hidrotermal vulkanik menghadapi dua kendala mendasar, yaitu permasalahan yang ill-posed dan solusi yang tidak unik (non-uniqueness), terutama ketika data mengandung noise. Banyak metode inversi yang ada menghasilkan solusi tidak stabil. Penelitian ini menerapkan algoritma Generalized Particle Swarm Optimization (GPSO) untuk menginversi data Self-Potential pada sistem hidrotermal vulkanik, dengan sumber anomali yang dimodelkan sebagai bola, silinder, dan inclined sheet. GPSO merupakan pengembangan dari PSO konvensional yang memodelkan partikel sebagai sistem massa pegas teredam stokastik, sehingga menghasilkan konvergensi yang lebih stabil dan keseimbangan eksplorasi-eksploitasi yang lebih efektif. Pengujian pada data sintetis dengan kontaminasi Gaussian noise 10% menunjukkan bahwa GPSO dengan parameter Variasi 3 (ω = 0.714, al dan ag = 1.714) mampu menghasilkan estimasi parameter yang akurat dengan ketidakpastian rendah. Penerapan pada data lapangan Gunung Api Kilauea, Haruna, Esan, dan Iwaki menghasilkan estimasi yang konsisten secara geologis dan penelitian sebelumnya. Hasil ini menunjukkan bahwa GPSO dapat direkomendasikan sebagai metode inversi data Self-Potential yang robust untuk karakterisasi sistem hidrotermal pada kawasan vulkanik.
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Inversion of Self-Potential data in volcanic hydrothermal systems faces two fundamental challenges, namely the ill-posed nature of the problem and solution non-uniqueness, particularly when the data contain noise. Many existing inversion methods produce unstable solutions. This study applies the Generalized Particle Swarm Optimization (GPSO) algorithm to invert Self-Potential data in volcanic hydrothermal systems, with anomaly sources modeled as sphere, cylinder, and inclined sheet. GPSO is a development of the conventional PSO that models particles as a stochastic damped mass-spring system, thereby producing more stable convergence and a more effective exploration-exploitation balance. Testing on synthetic data contaminated with 10% Gaussian noise shows that GPSO with Variation 3 parameters (ω = 0.714, al and ag = 1.714) is able to produce accurate parameter estimates with low uncertainty. Application to field data from Kilauea, Haruna, Esan, and Iwaki volcanoes yields estimates that are geologically consistent and in agreement with previous studies. These results indicate that GPSO can be recommended as a robust Self-Potential inversion method for characterizing hydrothermal systems in volcanic areas.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Generalized Particle Swarm Optimization, Inversi, Parameter, Self-Potential, Sistem Hidrotermal, Generalized Particle Swarm Optimization, Hydrothermal System, Inversion, Parameters, Self-Potential
Subjects:	G Geography. Anthropology. Recreation > GB Physical geography > GB1197.7 Groundwater flow. Reservoirs T Technology > T Technology (General) > T57.74 Linear programming T Technology > T Technology (General) > T57.8 Nonlinear programming. Support vector machine. Wavelets. Hidden Markov models. T Technology > T Technology (General) > T57.84 Heuristic algorithms.
Divisions:	Faculty of Civil, Planning, and Geo Engineering (CIVPLAN) > Geophysics Engineering > 33201-(S1) Undergraduate Thesis
Depositing User:	Dhea Rachmawati
Date Deposited:	20 Jul 2026 06:55
Last Modified:	20 Jul 2026 06:56
URI:	http://repository.its.ac.id/id/eprint/135683

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