Penerapan Metode Ensemble Kalman Inversion Untuk Inversi Data Vertical Electrical Sounding Pada Kasus Evaluasi Kestabilan Tanggul LUSI

Dongalemba, Josh Michael Jeferson (2026) Penerapan Metode Ensemble Kalman Inversion Untuk Inversi Data Vertical Electrical Sounding Pada Kasus Evaluasi Kestabilan Tanggul LUSI. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Pemodelan struktur tanggul dengan pendekatan quasi-2D menggunakan data Vertical Electrical Sounding (VES) merupakan salah satu metode non-destruktif yang digunakan untuk mengevaluasi kestabilan tanggul. Metode inversi yang umum digunakan, seperti optimasi lokal masih rentan terjebak pada minimum lokal dan bergantung pada model awal, sementara algoritma metaheuristik sangat bergantung pada tuning parameter. Penelitian ini mengimplementasikan Ensemble Kalman Inversion (EKI) yang memiliki keunggulan dalam sifatnya yang derivative-free, efisiensi komputasi dari Kalman Filter, dan kuantifikasi ketidakpastian yang efisien melalui statistik ensemble. Implementasi EKI dilakukan melalui serangkaian tahapan, meliputi tahapan uji inversi data sintetis, inversi data lapangan hingga menggabungkan hasil inversi dari data VES sepanjang profil tanggul untuk membuat model quasi-2D. Hasil inversi data sintetis pada kurva tipe A (resistivitas meningkat terhadap kedalaman), kurva tipe HK (resistivitas tinggi di antara resistivitas rendah) dan kurva tipe KH (resistivitas rendah di antara resistivitas tinggi) dengan penggunaan ukuran ensemble 200 dan iterasi 500 mampu mengoptimalkan antara stabilitas konvergensi dan resolusi model. Selanjutnya, EKI diimplementasikan pada inversi data lapangan (tanggul LUSI P.76-77 & P.78-79) dan hasilnya dibandingkan dengan perangkat lunak interpretasi IPI2WIN. Hasil perbandingan menunjukkan bahwa EKI unggul dalam memberikan estimasi dan mampu menyajikan kuantifikasi ketidakpastian dengan baik sehingga menghasilkan penampang model quasi-2D yang stabil dan konsisten dengan penelitian terdahulu. Hasil ini menunjukkan bahwa EKI merupakan pendekatan yang robust dan efisien dalam mengevaluasi kestabilan tanggul
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Embankment structure modeling using quasi-2D approach based on Vertical Electrical Sounding (VES) is one of the non-destructive methods used to evaluate embankment stability. Commonly used inversion methods, such as local optimization, are still prone to getting stuck at local minima and dependent to initial model, while metaheuristic algorithms are highly dependent on tuning parameter. This study implements Ensemble Kalman Inversion (EKI), which offers several advantages including its derivative-free nature, the computational efficiency of the Kalman filter and efficient uncertainty quantification through ensemble statistics. The implementation of EKI was carried out through a series of stages, including synthetic data inversion testing, field data inversion, and the integration of inversion results from VES data along survey profiles to construct a quasi-2D model. The synthetic data inversion results for curve type A (resistivity increased with depth), type HK (high resistivity between low resistivity), and type KH (low resistivity between high resistivity) using an ensemble size of 200 and 500 iterations were able to achieve an optimal balance between convergence stability and model resolution. Furthermore, EKI was applied to the inversion of field data (LUSI embankment P.76-77 and P.78-79), and the results were compared with those from IPI2WIN interpretation software. The comparison results showed that EKI outperformed in providing estimated and was able to quantify uncertainty, thereby producing a stable quasi-2D model cross-section consistent with previous research. These findings indicates that EKI is a robust and efficient method for evaluating embankment stability.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Inversi, Vertical Electrical Sounding, Ensemble Kalman Inversion, Stabilitas Tanggul
Subjects:	T Technology > T Technology (General) 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. T Technology > T Technology (General) > T58.62 Decision support systems
Divisions:	Faculty of Civil Engineering and Planning > Geophysics Engineering > 33201-(S1) Undergraduate Thesis
Depositing User:	Josh Michael Jeferson Dongalemba
Date Deposited:	20 Jul 2026 02:31
Last Modified:	20 Jul 2026 02:31
URI:	http://repository.its.ac.id/id/eprint/135582

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