Theses and Dissertations
Date of Award
Master of Science (MS)
Dr. Soumya Mohanty
Dr. Soma Mukherjee
Dr. Teviet Creighton
The Earth’s Vertical Gravity Gradient (VGG) can be used to map seafloor topography but presents a challenging inverse problem. A promising approach is forward modeling, in which one searches over a set of candidate topographies and selects the one whose predicted VGG best fits the observed one. The main bottleneck here is solving the associated high-dimensional and non-linear optimization problem. Yang et al (2018) demonstrated a method in which the topography is parametrized by heights of mass elements on a rectangular grid and the ≈ 104 dimensional optimization problem is tackled with simulated annealing (SA). We propose a computationally much cheaper method, using a stochastic optimization method known as Particle Swarm Optimization (PSO) and representing the topography as a linear combination of Radial Basis Functions (RBFs). First results, obtained without any tuning, show that the MATLAB code achieves an RMS error of 650 m with 500 RBFs (1500 parameters) and a 30 min run time. This is comparable to the error of 300 m from the much more expensive SA method that takes hours on a super-computer. Improvements to our method are likely to result in state of the art performance levels.
Kazhykarim, Yelbir, "Topography Estimation Using Particle Swarm Optimization" (2020). Theses and Dissertations. 488.
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