Open AccessMultivariable teaching-learning-based optimization (MTLBO) algorithm for estimating the structural parameters of the buried mass by magnetic data
Author(s) -
Ata Eshaghzadeh,
Sanaz Seyedi Sahebari
Publication year - 2020
Publication title -
geofizika
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.243
H-Index - 16
eISSN - 1846-6346
pISSN - 0352-3659
DOI - 10.15233/gfz.2020.37.6
Subject(s) - multivariable calculus , algorithm , noise (video) , amplitude , cylinder , anomaly (physics) , computer science , phase (matter) , magnetic anomaly , simple (philosophy) , process (computing) , mathematics , artificial intelligence , image (mathematics) , physics , geophysics , geometry , optics , engineering , philosophy , epistemology , quantum mechanics , control engineering , condensed matter physics , operating system
This paper presents a nature-based algorithm, titled multivariable teaching-learning-based optimization (MTLBO) algorithm. MTLBO algorithm during an iterative process can estimates the best values of the buried structure (model) parameters in a multi-objective problem. The algorithm works in two computational phases: the teacher phase and the learner phase. The major purpose of the MTLBO algorithm is to modify the value of the learners and thus, improving the value of the model parameters which leads to the optimal solution. The variables of each learner (model) are the depth (z), amplitude coefficient (k), shape factor (q), angle of effective magnetization (θ) and axis location (x0) parameters. We employ MTLBO method for the magnetic anomalies caused by the buried structures with a simple geometric shape such as sphere and horizontal cylinder. The efficiency of the MTLBO is also studied by noise corruption synthetic data, as the acceptable results were obtained. We have applied the MTLBO for the interpretation of the four magnetic anomaly profiles from Iran, Brazil and India.