William F. Jenkins

• Doctor of Philosophy
• Postdoctoral Scholar at University of California, San Diego

Geoacoustic inversion can be a computationally expensive task in high-dimensional parameter spaces, typically requiring thousands of forward model evaluations to estimate the geoacoustic environment. We demonstrate Bayesian optimization (BO), an efficient global optimization method capable of estimating geoacoustic parameters in seven-dimensional s...

Geoacoustic inversion of high-dimensional parameter spaces is a computationally intensive procedure, often necessitating thousands of forward model evaluations to accurately estimate the geoacoustic environment, such as Markov chain Monte Carlo sampling. This study introduces Bayesian optimization (BO), an efficient global optimization technique, t...

Distributed acoustic systems (DAS) offer a unique opportunity to sense seismic and acoustic waves at high sampling rates and spatial resolutions using fiber optic cables. The prevalence of fiber optic cables crossing seabeds around the world make them particularly attractive sensors of opportunity for acoustical and geophysical applications. Howeve...

Source localization with a geoacoustic model requires optimizing the model over a parameter space of range and depth with the objective of matching a predicted sound field to a field measured on an array. We propose a sample-efficient sequential Bayesian optimization strategy that models the objective function as a Gaussian process (GP) surrogate m...

This study probes the association between fluid injection in enhanced geothermal systems and certain kinds of seismicity that may result from hydraulic fracturing occurring at depth using unsupervised machine learning. In April and May 2019, a distributed acoustic sensing borehole array at the Frontier Observatory for Research in Geothermal Energy...

Matched field processing is a computationally expensive approach to estimate acoustic source location, since it relies on a grid search through range and depth. We present an efficient alternative that samples the parameter space with a Bayesian approach using a Gaussian process as a surrogate model of the objective function. The objective function...

Under the “curse of dimensionality,” distance-based algorithms, such as k-means or Gaussian mixture model clustering, can lose meaning and interpretability in high-dimensional space. Acoustic data, specifically spectrograms, are subject to such limitations due to their high dimensionality: for example, a spectrogram with 100 time- and 100 frequency...

No PDF available ABSTRACT The Hamiltonian Monte Carlo (HMC) algorithm is a variant Markov Chain Monte Carlo (MCMC) excelling at sampling high dimensional target distributions. The HMC explores a distribution as a particle trajectory in a Hamiltonian system by augmenting distribution parameters with auxiliary momentum variables. A particle trajector...

No PDF available ABSTRACT In this study, we present a method that samples geoacoustic parameter space with a Bayesian approach that uses a Gaussian process as a surrogate model of the objective function. The objective function is defined as a Bartlett processor whose output measures the match between a received and replica pressure field on a verti...

No PDF available ABSTRACT Gaussian processes (GP) have been used to predict acoustic fields by interpolating under-sampled field observations. Using GP interpolation to predict fields is advantageous due to its ability to denoise measurements, and for its prediction of likely field outcomes given a certain field coherence, or in GP terminology, a k...

Acoustical observations are presented from the Useful Arctic Knowledge (UAK) 2021 cruise, an early-career training program which took place in sea ice north of Fram Strait in June 2021 on board the Norwegian Coast Guard icebreaker KV Svalbard. Through oceanographic sampling and three acoustics-related tasks, participants were introduced to practica...

No PDF available ABSTRACT Geoacoustic model optimization and inversion are computationally expensive endeavors. In cases where a parameter grid search is prohibitively expensive, optimization produces an approximated solution through sampling techniques such as Markov chain Monte Carlo, simulated annealing, and genetic algorithms. More recent work...

No PDF available ABSTRACT The dynamic nature of the ocean environment presents numerous challenges to parameter estimation in ocean acoustics. We present a variational Bayesian method which selects an optimal model for parameter estimation in ocean acoustics using Gaussian process (GP) regression. GP model selection is based on Bayesian model selec...

Advances in machine learning (ML) techniques and computational capacity have yielded state‐of‐the‐art methodologies for processing, sorting, and analyzing large seismic data sets. In this study, we consider an application of ML for automatically identifying dominant types of impulsive seismicity contained in observations from a 34‐station broadband...