Bayesian optimization with Gaussian process surrogate model for geoacoustic inversion and parameter estimation
This repository contains code used to perform acoustic parameter estimation using Bayesian optimization with a Gaussian process surrogate model. The following papers use this code:
William Jenkins, Peter Gerstoft, and Yongsung Park, “Bayesian optimization with Gaussian process surrogate model for source localization,” J Acoust. Soc. Am., vol. 154, no. 3, pp. 1459–1470, Sep. 2023, doi: 10.1121/10.0020839.
William Jenkins and Peter Gerstoft. "Bayesian optimization with Gaussian processes for robust localization," submitted to IEEE Int. Conf. Acoust., Speech, Signal Process., Sep. 2023.
In your desired target directory, run the following command:
git clone git@github.com:NeptuneProjects/BOGP.git
Once cloned, build the Conda environment.
This may take a few minutes.
Two dependencies, TritonOA and OAOptimization, are automatically installed via pip
from their respective GitHub repositories.
conda env create -f gp_dev.yml
Activate the environment:
conda activate gp310
This workflow applies to multiple projects and data sets. Specific instructions for running the workflow on a particular data set are provided in the corresponding README.md
files:
Application | Data | Instructions |
---|---|---|
Acoustic source localization | SWellEx-96 | README.md |
Source localization robust to array tilt | SWellEx-96 | README.md |