The prediction of underwater noise emissions from impact pile driving during near-shore and offshore construction activities and its potential effect on the marine environment has been a major field of research for several years. A number of different modeling approaches have been suggested recently to predict the radiated sound pressure at different distances and depths from a driven pile. As there are no closed-form analytical solutions for this complex class of problems and for a lack of publicly available measurement data, the need for a benchmark case arises to compare the different approaches. Such a benchmark case was set up by the Institute of Modelling and Computation, Hamburg University of Technology (Hamburg, Germany) and the Organisation for Applied Scientific Research (TNO, The Hague, The Netherlands). Research groups from all over the world, who are involved in modeling sound emissions from offshore pile driving, were invited to contribute to the first so-called COMPILE (a portmanteau combining computation, comparison, and pile) workshop in Hamburg in June 2014. In this paper, the benchmark case is presented, alongside an overview of the seven models and the associated results contributed by the research groups from six different countries. The modeling results from the workshop are discussed, exhibiting a remarkable consistency in the provided levels out to several tens of kilometers. Additionally, possible future benchmark case extensions are proposed.Index Terms-Benchmark case, impact pile driving, underwater acoustics.

The task of detecting and classifying highly maneuverable and unidentified underwater targets in complex environments is significant in active sonar systems. Previous studies have applied many detection schemes to this task using signals above a preset threshold to separate targets from clutter; this is because a high signal-to-noise ratio (SNR) target has sufficient feature vector components to be separated out. However, in real environments, the received target return’s SNR is not always above the threshold. Therefore, a target detection algorithm is needed for varied target SNR conditions. When the clutter energy is too strong, false detection can occur, and the probability of detection is reduced due to the weak target signature. Furthermore, since a long pulse repetition interval is used for long-range detection and ambient noise tends to be high, classification processing for each ping is needed. This paper proposes a multilayer classification algorithm applicable to all signals in real underwater environments above the noise level without thresholding and verifies the algorithm’s classification performance. We obtained a variety of experimental data by using a real underwater target and a hull-mounted active sonar system operated on Korean naval ships in the East Sea, Korea. The detection performance of the proposed algorithm was evaluated in terms of the classification rate and false alarm rate as a function of the SNR. Since experimental environment data, including the sea state, target maneuvering patterns, and sound speed, were available, we selected 1123 instances of ping data from the target over all experiments and randomly selected 1000 clutters based on the distribution of clutters for each ping. A support vector machine was employed as the classifier, and 80% of the data were selected for training, leaving the remaining data for testing. This process was carried out 1000 times. For the performance analysis and discussions, samples of scatter diagrams and feature characteristics are shown and classification tables and receiver operation characteristic (ROC) curves are presented. The results show that the proposed algorithm is effective under a variety of target strengths and ambient noise levels.

The relation between high-frequency broadband acoustic signal variability and two types of internal waves (short-period internal solitary waves; ISWs, and semidiurnal internal tides; ITs) is investigated using data collected during the shallow-water acoustic variability experiment 2015 in the northeastern East China Sea. In this flat (∼100 m depth) region, an underwater sound channel with sound speed profile (SSP) variability observed during the experiment significantly affects the acoustic variability induced by the ISW, and the arrival structure of the channel impulse response (CIR) modeled by ray tracing. To model the range-dependent SSP due to ISW, the location and characteristics of the mode-1 ISW of wavelength (0.5–1 km) are estimated and verified based on the two-layer Korteweq–de Vries theory and by analyzing the observed temperature fluctuations. It is found from comparison between the measured and modeled CIRs that the ISW scatters the arrival structures of refracted rays. Meanwhile, semidiurnal ITs change the channel size modeled as range-independent considering the wavelengths (15–40 km) longer than the model range (3 km). Higher centroid of acoustic arrival time is found with lower isotherm depressions owing to the multimode ITs, indicative of acoustic energy focusing at the lower channel region.

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An offshore wind farm will be constructed in the Yellow Sea, west of Korean Peninsula, where there are extensive fishing activity and numerous fishery farms. To study the effect of underwater piling noise on fishing and marine lives, we model the pile driving noise propagation using coupled FE and PE model. The near-field noise is computed by FE model, considering detailed specifications of the pile driving system. We apply 2D axis-symmetric geometry and utilize acoustic structure interaction analysis in the frequency domain. The FE results are used to compose the starting field for PE model, where appropriate range selection is an important factor to cover most of the contributing ray paths. Extrapolation technique to compensate the lack of FE data and the numerical filtering method to smooth the FE result are discussed. In the far-field, the noise propagation is modeled by the split step Pade PE algorithm. The improved PE starting field seems to give refined result than previous coupled model.