Uji Coba Algoritma Covariance Based Moth-Flame Optimization with Cauchy Mutation Untuk Inversi Data Self-Potential

Ramadhan, Agung Nugroho (2022) Uji Coba Algoritma Covariance Based Moth-Flame Optimization with Cauchy Mutation Untuk Inversi Data Self-Potential. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Self-potential (SP) memiliki banyak aplikasi di dalam kehidupan sehari-hari. Intepretasi data self-potential dapat dilakukan dengan cara kualitatif maupun kuantitatif. Inversi data SP dapat digunakan untuk intepretasi kuantitatif. Global optimization method (GOM) dapat digunakan untuk inversi data SP dengan menyediakan ketidakpastin parameter model. Covariance based moth-flame optimization with Cauchy mutation (CCMFO) merupakan salah satu global optimization method yang dapat bersaing dengan algoritma-algoritma lain untuk menyelesaikan masalah rekayasa teknik. CCMFO diuji untuk menyelesaikan permasalahan inversi data self-potential yang memiliki anomali tunggal maupun memiliki anomali lebih dari satu. Hasil yang diperoleh menunjukkan algoritma CCMFO memperoleh hasil yang relatif bagus jika dibandingkan dengan algoritma MFO, GMFO, CMFO, LMFO, FPA, dan PSO. CCMFO juga diuji dengan data lapangan dan menunjukkan bahwa hasil inversi cocok dengan kondisi geologi setempat
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Self-potential (SP) has many applications in everyday life. Self-potential data interpretation can be done in a qualitative or quantitative way. SP data inversion can be used for quantitative interpretation. The global optimization method can be used for inversion of SP data by providing the uncertainty of model parameters. Covariance based moth-flame optimization with Cauchy mutation (CCMFO) is a global optimization method (GOM) that can compete with other algorithms to solve engineering problems. CCMFO is tested to solve the problem of inversion of self-potential data that has a single anomaly or has more than one anomaly. The results obtained show that the CCMFO algorithm obtains relatively good results when compared to the MFO, GMFO, CMFO, LMFO, FPA, and PSO algorithms. CCMFO was also tested with field data and showed that the inversion results matched the local geological conditions

Item Type:	Thesis (Other)
Uncontrolled Keywords:	CCMFO, GOM, inversi, self-potential CCMFO, GOM, inversion, self-potential
Subjects:	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:	Agung Nugroho Ramadhan
Date Deposited:	15 Feb 2023 04:21
Last Modified:	15 Feb 2023 04:21
URI:	http://repository.its.ac.id/id/eprint/97258

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