Investigation of sound pressure waveforms helps the selection of appropriate metrics to evaluate their effects on marine life in relation to noise thresholds. As marine animals move farther away from a sound source, the temporal characteristics of sound pressure may be influenced by interactions with the sediment and the sea surface. Sound pressure kurtosis and root-mean-square (rms) sound pressure are quantitative characteristics that depend on the shape of a sound pulse, with kurtosis related to the qualitative characteristic “impulsiveness.” After verifying the propagation modeling approach using selected test cases from the JAM Workshop held in Cambridge, UK, in 2022, the time dispersion values of pressure signals produced by an individual airgun shot across various sediment types are analyzed. The results reveal that there is significant pulse dispersion when the seabed consists of predominantly sand-type sediments: i.e., the airgun signal duration increases considerably over long distances, thus decreasing the kurtosis of a sequence of pulses, whereas the pulse dispersion is more limited for clay and silt-type sediments. The range variations of frequency weighted kurtosis and rms sound pressure differ from those of the unweighted kurtosis, depending on the corresponding lower and upper roll-off frequencies corresponding to different marine animal groups.

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The national measures in several European countries during the COVID-19 pandemic also affected offshore human activities, including shipping. In this work, the temporal and spatial variations of shipping sound are calculated for the years before and during the pandemic in selected shallow water test areas from the Southern North Sea and the Adriatic Sea. First, the monthly sound pressure level maps of ships and wind between 2017 and 2020 are calculated for frequencies between 100 Hz to 10 kHz. Next, the monthly changes in these maps are compared. The asymptotic approximation of the hybrid flux-mode propagation model reduces the computational requirements for sound mapping simulations and facilitates the production of a large number of sound maps for different months, depths, frequencies, and ship categories. After the strictest COVID-19 measures were applied in April 2020, the largest decline was observed for the fishing, passenger and recreational ships. Although the changes in the number of fishing vessels are large, their contribution to the soundscape is minor due to their low source level. In both test areas, the spatial exceedance levels and acoustic energies were decreased in 2020 compared to the average of the previous three years.

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The sea surface acts as a very strong reflector because of the large impedance contrast between water and air. The reflection coefficient is -1 in a very good approximation. Apart from the surface multiples, the sea surface is also responsible for generating the source and receiver ghost wavefields. These cause the well-known ghost notches in the spectrum: areas where the signal-to-noise ratio is very low. To model the ghost wavefields, ghost operators can be computed and applied to ghost-free data. Modeling experiments indicate that in the case of a flat sea surface, the character of the notches in various gather types, e.g., receiver gather, common-offset gather, shot record, is largely determined by the complexity of the earth. In a simple earth, e.g., horizontally layered, the notches are always well-defined and deep, but in a complex earth, they become blurry in some of the gather types. Therefore, in the case of a complex subsurface, source deghosting is best carried out in the common-receiver domain and receiver deghosting is best carried out in the common-shot domain. In the case of a simple subsurface, deghosting can be carried out in all domains. An additional factor is that the sea surface may be rough and dynamic. This causes blurry ghost notches in all gather types, even in the case of a simple earth. To model the source ghost for this situation, an effective static rough sea surface suffices. This keeps the computations simple. The condition is that the source has an impulsive character. However, to model the receiver ghost (and the source ghost for a nonimpulsive source), the dynamics of the sea surface must be included. This can be done by composing the final result from the results computed for several "frozen" snapshots of the dynamic sea surface.@en

Computer models can be used to predict the sound field near an airgun or airgun array. The accuracy of such predictions can be assessed either by a process of verification (comparison with alternative theoretical solution to the same computational problem) or by validation (comparison with measurement). A set of verification test problems is described, as used originally by participants in the International Airgun Modeling Workshop held in Dublin, Ireland on July 16, 2016, and now by other authors in this special issue. The main inputs specified are the characteristics of the source (array geometry, airgun type and volume, and chamber pressure) and of the propagation medium (mainly water depth and bottom type). Also specified are source waveforms for individual airguns for authors wishing to focus only on the propagation aspects of the problem. The outputs are specified in terms of metrics derived from the sound pressure and sound particle acceleration.@en

The calculation of air gun source signatures gives insight into applications such as air gun array design, deghosting and the impact of sound on marine life. Single air gun source signatures were calculated from the numerical solution of a set of differential equations based on different branches of physics. Some characteristic parameters of air guns were obtained from the Svein Vaage broadband air gun data set (SVBAD) measurements to calibrate the model. The comparison between measured and modeled air gun signals helps to investigate the accuracy of air gun source models. The modeled air gun signatures compared well with measurements from the SVBAD for the case of a calm sea surface of sea state 2 or less. The source ghost signal modeled for a rough sea surface showed amplitude and phase changes, affecting the ghost notches, which may explain discrepancies between the SVBAD measurements and modeled air gun signals at frequencies above 150 Hz.

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Comparisons are made of sound pressure and particle acceleration predicted by two methods in the vicinity of two arrays of marine-seismic airguns. Data describing the array properties and the environmental conditions are taken from test cases designed to facilitate intermodel comparison. The two propagation approaches, one based on method of images and the other on wave number integration, are shown to be capable of giving line-on-line agreement when the latter method implements the full form of the Hankel transform; when the more approximate Fourier transform is used, predictions are shown to differ at ranges of a few meters from the source.@en

The strong sea surface reflectivity causes source- and receiver-ghost notches: areas in the wavenumber-frequency domain where the signal is very weak and consequently the SNR (signel-to-noise ratio) is low. If the sea surface is flat, these notches are very deep, but if it is rough and dynamic, they become blurred. The same strong sea-surface reflectivity also causes the surface-related multiples and its effect increases with each next multiple order. When modeling the ghost wavefield of an impulsive source in the case of a dynamic sea surface, an effective static sea surface suffices. The latter is composed of the surface areas that are ‘touched’ by the source wavefield at successive snapshots in time. However, such an effective static sea surface is not sufficient for the case of the detector ghost: here the dynamic character of the sea surface needs to be taken into account. We introduce a method for doing this by computing ghost operators for a number of snapshots in time and compose the final result from applying these. Our modeling method can be used to test algorithms such as deghosting. It can also be used to quantify the error that is introduced when the effects of the rough and dynamic sea surface are ignored, e.g., by replacing it by a flat sea surface in combination with an effective reflectivity that aims at incorporating the effects of a rough and dynamic sea surface.@en

The strong sea surface reflectivity causes source- and receiver-ghost notches: areas in the wavenumber-frequency domain where the signal is very weak. If the subsurface is complex, the source-ghost notch is most apparent in common detector gathers. It may even be absent in common source gathers, i.e., in shot records. For the detector-ghost notches it is the other way around: they are most apparent in shot records and may be absent in detector gathers. Both notch types show up in the common-offset and CMP domains albeit less clearly. In the case of a complex subsurface, the deghosting process should be carried out in the right domain: source deghosting in the common receiver domain, detector deghosting in the common shot domain. In the case of a simple subsurface, deghosting can be carried out in any domain.@en

The production of underwater sound is more and more considered to be an environmental risk. This has already been the case for military sonar for more than a decade, as sonar was identified as a possible cause of marine mammal strandings. The approach we adapted for military sonar is the following. The risk is characterized by computing the exposure (sound produced by the sonar) in an area around the source and by coupling that information to the effects it causes on a certain animal species. The risk is then quantified by taking into account the probability of the presence of that species in the area. If too large, the risk can be mitigated. We observe a trend of shifting the focus from individual disturbance to more general population consequences. A similar approach is advised to characterize the risks involved in the use of airguns in seismic acquisition.

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