Inversi Anomali Data Self-Potential Menggunakan Algoritma Micro-Differential Evolution

Ratnasari, Adhistya (2018) Inversi Anomali Data Self-Potential Menggunakan Algoritma Micro-Differential Evolution. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Interpretasi data self-potential (SP) dapat dilakukan melalui inversi yakni proses pencocokkan data pengukuran dengan data model yang dilakukan secara otomatis. Dengan demikian, diperlukan suatu metode untuk proses tersebut. Penelitian ini, dilakukan untuk mengetahui kemampuan algoritma Micro-Differential Evolution (MDE) dalam mengestimasi parameter model beserta ketidakpastiannya dari anomali SP. Algoritma ini telah di uji pada data sintetik dan data lapangan). Hasilnya ialah pada data sintetik, MDE akurat untuk proses inversi data SP dan mampu menyediakan PDM dengan cepat, sedangkan untuk data lapangan hasil inversinya sesuai dengan metode yang lainnya (Continuous Wavelet Transform dan Flower Polination Algorithm). ============== Interpretation of self-potential (SP) data can be done by inversion through automatically matching measurement data and model data. Thus, it requires a method to complete that process. The objective of this research is to identify Micro-Differential Evolution algorithm ability in estimating parameter model and its uncertainty of SP anomaly. This algorithm has been tested to synthetic and field data. The result is in synthetic data, MDE is accurate for SP data inversion process and able to provide PDM quickly, while for field inversion data results match to another methods such as Continuous Wavelet Transform and Flower Polination Algorithm.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: MDE, Self-Potential (SP); Posterior Distribution Model (PDM)
Subjects: Q Science > QA Mathematics > QA403.3 Wavelets (Mathematics)
Q Science > QC Physics
Divisions: Faculty of Mathematics and Science > Physics > (S1) Undergraduate Theses
Depositing User: Ratnasari Adhistya
Date Deposited: 09 Apr 2018 02:23
Last Modified: 09 Apr 2018 02:23
URI: http://repository.its.ac.id/id/eprint/50561

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