Waterborne acoustic signals carry information about the ocean environment. Ocean geoacoustic inversion is the task of estimating environmental parameters from received acoustic signals by matching the measured sound with the predictions of a physics-based model. A lower bound on the uncertainty associated with environmental parameter estimates, the Cramér-Rao bound, can be calculated from the Fisher information, which is dependent on derivatives of a physics-based model. Physics-based preconditioners circumvent the need for variable step sizes when computing numerical derivatives. This work explores the feasibility of using a neural network to perform geoacoustic inversion for environmental parameters and their associated uncertainties from ship noise spectrogram data. To train neural networks, a synthetic dataset is generated and tested for generalizability against 31 measurements taken during the SBCEX2017 study of the New England Mud Patch.
College and Department
Physical and Mathematical Sciences; Physics and Astronomy
BYU ScholarsArchive Citation
Mortenson, Michael Craig, "Physics-Guided Machine Learning in Ocean Acoustics Using Fisher Information" (2022). Theses and Dissertations. 9488.
underwater acoustics, geoacoustic inversion, Fisher information, Cramér-Rao bound, machine learning, deep learning, uncertainty analysis