Today, a significant part of the oceans remains coarsely mapped. Some specific areas like the continental shelves will require high-resolution surveys. This represents a very long and expensive task that will keep scientist busy for several decades. A way to reduce costs and time could be to improve the efficiency of current surveys. We are trying to do this by sending more signals in the water at the same time.

The Multi-beam echo sounder is recognized as one of the most efficient and reliable tool for seabed mapping. Applications range from regional mapping of the seabed for geological surveys to the inspection of man-made objects such as pipelines or ship wrecks.

How does it work?  A Multi-beam is composed of two orthogonal arrays. The sounder transmits a short ultrasound pulse (or ping) inside a wide angular sector steered vertically under the ship (see the illustration in Fig. 1). On reception, beamforming technique is applied in order to process the seafloor echoes inside a high number of narrow beams, providing depths of sounding along numerous angular directions. Usually, we put a limit on the minimum delay between two pings, since a new echo might mask the final part of the previous reflection.

Fig. 1: Illustration of seabed mapping with a Multi-beam echo sounder
(Courtesy of Kongsberg Maritime)

This system has mapped seabeds all over the world since its introduction in the 1980’s.  The hardware has benefited from significant improvements in transducer technology and processing power. However, the fundamental design has not really changed for 40 years. Moreover, the simplest type of waveform, the unmodulated pulse, is still the one most used.

In my PhD project, we are exploring with our industrial partner Kongsberg Maritime, new sonar designs that could lead to improved performance in some particular applications. One possibility is to transmit advanced coded waveforms, inspired from modern techniques used in radar and wireless communication.

We show in Fig. 2, two examples of transmitted signals: The first one is the conventional “unmodulated” pulse, while the second has been “encoded” with a sequence of ones and zeros (the red line). In this example, additional information is added to the phase of the signal. It gives new properties to the pulse that may lead to various improvement, depending on the code and the desired application.

Fig. 2: (Top) Conventional unmodulated signal transmitted for seabed mapping.
(Bottom) Encoded signal, the red line corresponds to the code.

By carefully choosing specific codes, we are trying to mitigate interferences between signals transmitted at the same time. Such a property is called orthogonality and it is very interesting for seabed mapping since it will make it possible to map multiple parts of the seabed at the same time. It may also increase the sounding density, and make the survey more efficient.

Due to high cost at prototyping and testing new concepts, design investigations are performed with a simulator. It is based on FieldII (from DTU in Denmark) the reference in medical ultrasound simulation.

The main advantage is the ability to model the wave-field emitted by all kinds of array geometries with arbitrary pulse shape. It computes the wave-field backscattered from a defined seafloor model. Data are generated at all the different processing stages, from raw data until the final depth of soundings. It allows us to study the effects of new designs on Multi-beam performance. The simulation workflow is shown in Fig. 3.

Fig. 3: Simulation workflow

The last point that I would like to mention is the Doppler Effect: Unlike in medical imaging, the Doppler Effect is a source of trouble in seabed mapping. Rough ship motions induce frequency shifts in the transmitted pulse.  The consequence will be the same as encoding with a chaotic sequence which is a function of the ship motion. If this sequence is unknown, it will corrupt the coded signal and destroy its properties. Therefore, we are working on signal processing techniques that estimate and correct these shifts. Once corrected, it will allow us to extend the boundaries of orthogonality between signals.

Hopefully, this research project will open new possibilities for the next generation of Multi-beam echo sounders.

Blogger: Dr. Alan Hunter, University of Bath and Centre for Innovative Ultrasound Solutions (CIUS)

CIUS researchers are investigating whether a property of ultrasonic waves known as coherence can be used to detect microcalcifications in human tissue for cancer screening.  In marine sonar, it has already been used successfully for detecting and characterising objects and features on the seafloor.

The spirit of CIUS is for ideas to flow between the disciplines and there are several examples of concepts from medical ultrasound that have been adapted to marine sonar. However, this is a rare example of a technique which is being taken from sonar and applied to ultrasound.

Phase coherence

Wave-fields contain waves with different offsets, known as phase shifts or phases.  Coherence is a measure of the “uniformity” or “orderliness” of these phases.  An incoherent field has waves with random phases, whereas a coherent field has phases that are aligned in an orderly fashion. Good examples of incoherent and coherent electromagnetic wave fields are natural white light from the sun and laser light, respectively. Natural white light contains a wide range of frequencies with random phases, whereas laser light contains a narrow range of frequencies with tightly aligned phases.  These examples are illustrated in Fig. 1.

Fig. 1 – Illustrations of (a) incoherent natural white light and (b) coherent laser light.

Ultrasonic and acoustic imaging systems emit coherent waves. However, when these emitted fields are reflected and scattered, they can become incoherent due to the nature of the medium or object. Illustrations of coherent and incoherent reflection are shown in Fig 2.

Fig. 2 – An incident coherent ultrasonic wave field can be scattered (a) coherently (with aligned phase) or (b) incoherently (with random phase) leading to constructive and destructive interference.

Coherence-based image enhancement for marine sonar

In seafloor imaging sonar, measurements of phase coherence can be used to distinguish between objects or media that are smooth / simple versus rough / complicated. The former tends to result in coherent scattering and the latter in incoherent scattering. For low-frequency sediment and object-penetrating sonar, coherence measurements offer further potential to characterise material properties and internal structure.

A new algorithm was developed recently to extract coherence information from synthetic aperture sonar (SAS) images. The algorithm can separate an image into its coherent and incoherent parts.  Examples are shown in Fig. 3 for images collected by the NATO Science and Technology Organisation (STO) Centre for Maritime Research and Experimentation (CMRE) using their MUSCLE autonomous underwater vehicle (AUV). Here, the coherent parts of the scene are overlaid in red, highlighting rigid objects that are resting on the seafloor. Another example is shown in Fig. 4 for an image beneath the seafloor collected by TNO (Netherlands Organisation for Applied Science) using their MUD sediment-penetrating sonar. Here, buried objects can be detected more easily once the incoherent seafloor has been removed.

Fig. 3 – Sonar images of the seafloor from CMRE’s MUSCLE AUV. The grayscale images have been enhanced with overlays that highlight the coherent regions in red: (a) shows a benign and mostly featureless seafloor and (b) shows a seafloor that is cluttered with natural debris. This figure has been reproduced from [Hunter et al., 1st International Conference on Underwater Acoustics, Corfu, Greece, 2013] with the permission of Dr Samantha Dugelay at NATO STO CMRE.

Fig. 4 – A sonar image of buried objects from TNO’s MUD sonar. The original image is shown in (a) and the enhanced image with the incoherent background removed is shown in (b). A buried object can be observed in the zoomed region. This figure has been reproduced from [Hunter et al., 1st International Conference on Underwater Acoustics, Corfu, Greece, 2013] with the permission of Dr Guus Beckers at TNO.

Application to medical ultrasound imaging of human tissue

This year, PhD student Stine Hverven at the Department of Informatics, UiO, has started to investigate whether the sonar coherence algorithm can be adapted for detecting microcalcifications in human tissue using medical ultrasound. Microcalcifications in the breast can be a precursor to cancer. However, they are very difficult to detect in traditional ultrasound imagery. Our hypothesis is that microcalcifications scatter ultrasound more coherently than the surrounding fibrous tissue.  Therefore, by exploiting the coherence information it might be possible to provide a better detection capability and this could lead to early detection and ultimately prevention of some cancers.

Preliminary results based on simulations and medical phantoms have shown promise. A simulated example is given in Fig 5.  Stine is now starting to test the algorithm on images from real breast tissue containing microcalcifications and we are eager to see the results.

Fig. 5 – Simulated medical ultrasound imagery of three microcalcifications in human tissue generated using the Field-II simulator: (a) is the original ultrasound image and (b) is the coherence-enhanced image. The locations of the microcalcifications are indicated by the arrows and the potential improvement to detectability is clearly illustrated.

Blogger: Marcus Wild, PhD-candidate, Centre for Innovative Ultrasound Solutions (CIUS) and the University College of Southeast Norway.

Within the maritime industry, ships use SONAR (Sound Navigation and Ranging) systems in order to make maps of the seabed. The problem with SONARs, is that they have a tendency to overheat. Our aim is to predict the temperature rise in the materials used in SONARs to prevent this from happening.

SONARs emit sound waves, which then bounce off the seabed and return to the ship, in the same way that a bat or a dolphin uses sound to find its prey. SONARs are made of many components, but one of the most important components is the piezoelectric (piezo means squeeze) material. When pressure is applied to this type of material, an electric current is produced and reversely, when an electric current is applied to this material, it generates a sound wave (like a microphone and an amplifier). This behaviour is very useful and this material is the driving mechanism behind the sound wave generated by SONAR. However, some of this energy is also converted into heat, and too much heat will damage the SONAR.

In order to predict the heating in the piezoelectric material, the material properties such as the stiffness, electric properties (specifically, electric permittivity) and the electromechanical coupling factor (how easily the material converts mechanical energy like sound to electrical energy and vice versa) need to be characterised accurately.

First, we developed a fast and accurate method to characterize the piezoelectric material. This method uses a 1D analytical model of a piezoelectric material, which predicts the material’s electrical admittance as a function of frequency for a given set of material parameters. The electric admittance is the ease that a material has to allow an electrical current to flow through it for a given voltage. This property is frequency dependent for a piezoelectric material. The shape of the electrical admittance as a function of frequency can give us a lot of information about the material properties such as the resonance frequency or the energy loss mechanisms. The electrical admittance can also be measured quite easily in our case.

Inevitably, there will be a difference between the actual and modelled electrical admittance. To reduce the difference between theory and practice, we use an optimisation or minimisation algorithm. This algorithm will feed the 1D model with a series of changing material properties, which produces a different admittance curve for each change. It will do this until it finds a set of material parameters where the model mirrors reality. At that point, we have characterised the material in question accurately.

The efficacy of the method is illustrated in figure 1 and 2. Figure 1 shows that there is a clear difference between the two curves before optimisation. This suggests that the initial material parameters input into the 1D model are not accurate. After the optimisation however, there is no discernible difference between the two curves as shown in figure 2, and the material parameter set is the accurate set for this material.

Figure 1: Comparison between measured and modelled admittance curve as a function of frequency before optimisation.

Figure 2: Comparison between measured and modelled admittance curve as a function of frequency after optimisation.

Now that we have developed a method to characterise the piezoelectric material, we will apply it to the piezoelectric material at various temperatures. Indeed, the material parameters tend to change with temperature, and this is important for us to capture if we are to predict the heat rise within this material. Once we have these material parameters, we will be able to predict the amount of energy lost by this material depending on time and volume. This will enable us to be a step closer to understanding how and where the heat is generated in these materials.