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The motivation for this study is to create a scaled laboratory model of a steel construction pile being driven by an impact hammer, which can provide controlled data to aid understanding and development of a structural acoustics numerical model simulating full-scale impact pile driving. The scaled model is approximately thirty times shorter than a typical 30-meter long Cast-in-Shell-Steel (CISS) pile. The relationship between the impact force, structural vibrations, and radiated sound field is analyzed. The time-domain acoustic intensity in the radial direction is found to be predominately negative immediately following excitation by the impact force. Analysis of the radial intensity shows that during the hammer strike, there is a net flow of energy from the structure into the water; however, because the structure and water are acoustically coupled a significant portion of the energy immediately flows back into the cylinder following hammer impact. This fluid-structure interaction results in a highly damped acoustic pulse in the water that propagates to the far field. In addition, the frequency spectra of the impact force, model pile wall acceleration in the radial direction in air and water, and underwater acoustic pressure are analyzed to find transfer functions between these variables. The transfer function between impact force and sound pressure is of particular interest because it can be used to calculate the system response for any other applied hammer force. This transfer function analysis has potential applications in mitigating noise generated by impact pile driving.

Impact pile driving can produce extremely high underwater sound levels, which are of increasing environmental concern due to their deleterious effects on marine wildlife. Prediction of underwater sound levels is important to the assessment and mitigation of the environmental impacts caused by pile driving. Current prediction methods are limited and do not account for the dynamic pile driving source, inhomogeneities in bathymetry and sediment, or physics-based sound wave propagation. In this thesis, a computational model is presented that analyzes and predicts the underwater noise radiated by pile driving and is suitable for shallow, inhomogeneous environments and long propagation ranges. The computational model uses dynamic source models from recent developments in the technical literature. Pile source models are coupled to a broadband application of the range-dependent acoustic model (RAMPE), a standard parabolic equation (PE) propagation code capable of modeling wave propagation through complex, range dependent environments. Simulation results are shown to be in good agreement with several observations of pile driving operations in the Columbia River between Portland, Oregon and Vancouver, Washington. The model is further applied to extend sound level predictions over the entire river and study the effects of sediment and bathymetry on the underwater sound levels present in the environment.

Understanding what psycho-physiological and behavioural factors influence aversiveness of sound in marine mammals is important for conservation and practical applications. The aim of this study was to determine predictors for impact of anthropogenic noise and to develop a target-specific predator deterrence system for use on fish farms. Three classes of stimuli were tested: 1.) grey seal underwater communication calls expected to be used in territorial defence, 2.) high duty-cycle moderately loud artificial sounds (some of which were based on models of unpleasantness for humans), 3.) brief, intense pulses designed to elicit the acoustic startle reflex. Communication calls had no deterrence effect but instead caused attraction responses. Tests with high duty-cycle artificial sounds showed that food-motivated animals habituate quickly, although sound exposure caused subtle changes in diving patterns over a longer time. Field trials using the same stimuli were used to determine avoidance thresholds but also indicated that sound features like ‘roughness’ play a role. The startle eliciting stimuli, however, had the most dramatic effects. To this stimulus most seals exhibited rapid flight responses, hauled out, sensitised and showed signs of fear conditioning. Startle thresholds were found to be 80-85 dB above the assumed hearing threshold. The data showed that startle thresholds are a crucial predictor for the occurrence of strong avoidance behaviour and suggests that the startle response evolved to increase an animal’s propensity for flight. Finally, a prototype predator deterrence system based on the startle sounds was developed to repel seals whilst not affecting toothed whales. In fish farm trials, seals were deterred at close ranges but local abundance of cetaceans did not change showing that it is possible to cause differential responses between species based on differences in their audiograms. The results are used to develop noise exposure criteria and to elucidate acoustic parameters that can be used to predict responses to anthropogenic noise.

<p>This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification.</p><p>In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information.</p><p>In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data.</p><p>Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear.</p><p>We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale vocalization data set. The word error rate of the DCTNet feature is similar to the MFSC in speech recognition tasks, suggesting that the convolutional network is able to reveal acoustic content of speech signals.</p> / Dissertation

Wavelets have been used in numerous geophysical studies but few have examined their applicability to underwater acoustic signals. Wavelet transforms can remove noise from a given time series and allow data analysis at multiple levels of resolution. This unique ability is exercised as a feasible application to the signals in this thesis: a reflected scattered signal from a swimbladder-bearing fish, alewife (Alosa pseudoharengus), and several Odontocetes vocalizations. Both studies reveal that wavelet-based techniques show potential in providing viable information for these acoustic signals despite the lack of statistical analysis. The alewife portion shows a reasonable first order approximation to the absolute target strength and to the time delay correlation caused by the spatial separation of scattering features in the fish. The marine mammal application shows a possible real time method to estimate the mammal's range using the root mean square (RMS) energy of the decomposed signal. Because of wavelet function mismatch, both studies conclude that more extensive research is necessary to develop these techniques into systematic processes.

This manuscript describes a methodology to combine environmental models, acoustic signal predictions, statistical detection models and operations research to form a framework for calculating and communicating performance. This methodology has been applied to undersea target detection systems and has come to be known as Performance Surface modeling. The term Performance Surface refers to a geo-spatial representation of the predicted performance of one or more sensors constrained by all-source forecasts for a geophysical area of operations. Recent improvements in ocean, atmospheric and underwater acoustic models, along with advances in parallel computing provide an opportunity to forecast the effects of a complex and dynamic acoustic environment on undersea target detection system performance. This manuscript describes a new process that calculates performance in a straight-forward "sonar-equation" manner utilizing spatially complex and temporally dynamic environmental models. This performance model is constructed by joining environmental acoustic signal predictions with a detection model to form a probabilistic prediction which is then combined with probabilities of target location to produce conditional, joint and marginal probabilities. These joint and marginal probabilities become the scalar estimates of system performance. This manuscript contains two invited articles recently accepted for publication. The first article describes the Performance Surface model development with sections on current applications and future extensions to a more stochastic model. The second article is written from the operational perspective of a Naval commanding officer with co-authors from the active force. Performance Surface tools have been demonstrated at the Naval Oceanographic Office (NAVOCEANO) and the Naval Oceanographic Anti-Submarine Warfare (ASW) Center (NOAC) in support of recent naval exercises. The model also has recently been a major representation for the "performance" layer of the Naval Meteorological and Oceanographic Command (NAVMETOCCOM) in its Battlespace on Demand strategy for supporting the Fleet with oceanographic products.

This thesis investigates inversion techniques used to determine the geoacoustic properties of a shallow-water waveguide. The data used were obtained in the Shallow Water '06 Modal Mapping Experiment in which four buoys drifted over a system of subbottom channels. The method used was perturbative inversion using modal eigenvalues as input parameters, which were found using an autoregressive spectral estimator. This work investigates the differences between a "channel" region and a "no channel" region based on an inferred stratigraphic model. Inversions were performed on data from a single buoy both at individual frequencies and multiple frequencies simultaneously. Since the use of multiple frequencies and a certain set of constraints proved to be an effective method of inversion, the method was applied to data from the other three buoys as well. It is shown that the "channel" and "no channel" regions have significantly different sound speed profiles. / by Rebecca Weeks. / Thesis (M.S.C.S.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.

High-resolution sonar systems are primarily used for ocean floor surveys and port security operations but produce images of limited resolution. In turn, a sonar-specific methodology is required to detect and classify underwater unexploded ordnance (UXO) using the low-resolution sonar data. After researching and reviewing numerous approaches the Multiple Aspect-Fixed Range Template Matching (MAFR-TM) algorithm was developed. The MAFR-TM algorithm is specifically designed to detect and classify a target of high characteristic impedance in an environment that contains similar shaped objects of low characteristic impedance. MAFR-TM is tested against a tank and field data set collected by the Sound Metrics Corp. DIDSON US300. This thesis document proves the MAFR-TM can detect, classify, orient, and locate a target in the sector-scan sonar images. This paper focuses on the MAFR-TM algorithm and its results. / by Lisa Nicole Brisson. / Thesis (M.S.C.S.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.

This thesis proposes to estimate the speed of a moving acoustic source by either linear or non linear processing of the resulting Doppler shift present in a high-frequency pilot tone. The source is an acoustic modem (Hermes) which currently uses moving average to estimate and compensate for Doppler shift. A new auto regressive approach to Doppler estimation (labeled IIR method in the text) promises to give a better estimate. The results for a simulated peak velocity of 2 m/s in the presence of additive noise showed an RMSE of 0.23 m/s using moving average vs. 0.00018 m/s for the auto regressive approach. The SNR was 75 dB. The next objective was to compare the estimated Doppler velocity obtained using the two algorithms with the experimental values recorded in real time. The setup consisted of a receiver hydrophone attached to a towing carriage that moved with a known velocity with respect to a stationary acoustic source. The source transmitted 375 kHz pilot tone. The received pilot tone data were preprocessed using the two algorithms to estimate both Doppler shift and Doppler velocity. The accuracy of the algorithms was compared against the true velocity values of the carriage. The RMSE for a message from experiments conducted indoor for constant velocity of 0.4 m/s was 0.6055 m/s using moving average, 0.0780 m/s using auto regressive approach. The SNIR was 6.3 dB. / by Poorani Kathiroli. / Thesis (M.S.C.S.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.