Marine Geophysical Research (2018) 39:121–137 https://doi.org/10.1007/s11001-018-9341-z

ORIGINAL RESEARCH PAPER

Multibeam backscatter data processing

Alexandre C. G. Schimel1,2 · Jonathan Beaudoin3 · Iain M. Parnum4 · Tim Le Bas5 · Val Schmidt6 · Gordon Keith7 · Daniel Ierodiaconou2

Received: 14 June 2017 / Accepted: 12 January 2018 / Published online: 31 January 2018 © Springer Science+Business Media B.V., part of Springer Nature 2018

Abstract Multibeam sonar systems now routinely record seafloor backscatter data, which are processed into backscatter mosaics and angular responses, both of which can assist in identifying seafloor types and morphology. Those data products are obtained from the multibeam sonar raw data files through a sequence of data processing stages that follows a basic plan, but the imple- mentation of which varies greatly between sonar systems and software. In this article, we provide a comprehensive review of this backscatter data processing chain, with a focus on the variability in the possible implementation of each processing stage. Our objective for undertaking this task is twofold: (1) to provide an overview of backscatter data processing for the consideration of the general user and (2) to provide suggestions to multibeam sonar manufacturers, software providers and the operators of these systems and software for eventually reducing the lack of control, uncertainty and variability associ- ated with current data processing implementations and the resulting backscatter data products. One such suggestion is the adoption of a nomenclature for increasingly refined levels of processing, akin to the nomenclature adopted for satellite remote-sensing data deliverables.

Keywords Multi-beam · Echosounder · Echo-sounder · Backscatter mosaic · Angular response · Backscatter · Seafloor characterization

Introduction

Originally designed to measure seafloor , multi- beam sonar systems now routinely record seafloor backscat- ter data, which are commonly processed into a backscatter * Alexandre C. G. Schimel mosaic and less commonly into a set of angular responses [email protected] (Lurton and Lamarche 2015). A backscatter mosaic is the common term for a georeferenced image—usually repre- 1 National Institute of Water and Atmospheric Research (NIWA), Taihoro Nukurangi, 301 Evans Bay Parade, sented in gray scale—of the seafloor acoustic backscatter- Hataitai, Wellington 6021, New Zealand ing strength (or related variable) (Fig. 1a), while an angu- lar response 2 Deakin University, School of Life and Environmental is the common term describing the variation Sciences, Centre for Integrative Ecology (Warrnambool of the seafloor acoustic backscattering strength (or related Campus), Princes Hwy, Sherwood Park, PO Box 423, variable) with the angle of incidence of the acoustic signal Warrnambool, VIC 3280, Australia at the seafloor (Fig. 1b). Angular responses are typically 3 Quality Positioning Services Canada Inc, Fredericton, calculated for a single ping, a set of consecutive pings, or a Canada given geographical area, and are typically represented as a 4 Centre for Marine Science and Technology, Curtin mean value for each angle (“angular response curve”) as in University, Bentley, WA, Australia Fig. 1b, or a color-coded distribution of values for each angle 5 National Center Southampton, European Way, (“angular response 2D histogram”, see Le Gonidec et al. Southampton SO14 3ZH, UK (2003) for examples). The motivation for producing those 6 Center for Coastal and Ocean Mapping, University of New two outputs is the identification of different seafloor types Hampshire, 24 Colovos Road, Durham, NH, USA and morphology, which show as regions of different levels 7 CSIRO Oceans & Atmosphere, Hobart, TAS, Australia

Vol.:(0123456789)1 3 122 Marine Geophysical Research (2018) 39:121–137

Fig. 1 Example of a backscatter mosaic (a) and corresponding angu- backscatter level computed from the areas defined by polygons on the lar response curves (b). Data shown are a subset of a Kongsberg Mar- backscatter mosaic. The angular response curves in dashed lines are itime EM300 dataset (30 kHz) acquired in the Narrows Basin, Cook results from a geophysical model for some typical types. Fig- Strait, New Zealand, and processed using the IFREMER SonarScope ures modified from Lamarche et al. (2011) software. The coloured angular response curves represent the mean

Fig. 2 The backscatter data processing chain

and/or textures on a backscatter mosaic, and different shapes data processing stages (Fig. 2). However, the implementa- in angular responses curves or 2D histograms. tion of these stages tends to differ—whether in order, level Backscatter mosaics and angular responses are obtained of detail, or methodology—between multibeam sonar man- from the multibeam sonar raw data files through a series of ufacturers, between models from the same manufacturer,

1 3 Marine Geophysical Research (2018) 39:121–137 123 between operation modes of the same sonar system, “half-swath time-series” type, which consists in one uninter- between data processing software, and between software rupted time-series for each side of the swath, and is meant versions. This variability in approaches to implementation to emulate a sidescan sonar trace. Finally, backscatter data and the lack of control on some of these processing stages can also come as a “time-series of amplitudes and angles” has resulted in a general inconsistency in the backscatter format, which is the native output of phase-measuring bathy- mosaics and the angular responses being produced to date metric sidescan . Data in this format can be found in (Lucieer et al. 2017). full resolution and sometimes averaged/decimated. Note that This article provides an overview of backscatter data given the absence of accepted standard or accurate academic processing, including discussions on the possible levels of definition, the terminology for each of these data types vary detail, algorithms, and alternative approaches to implemen- between manufacturers; for example, the “single value per tation. Our objectives are (1) to provide the general user of beam” type is found as “Beam Intensity” in Kongsberg backscatter mosaics and angular responses with an under- Maritime systems, “Bathy Intensity” in R2Sonic systems, standing of the acoustic principles, processing techniques, or “Beam magnitude” in Teledyne RESON systems, while and levels of freedom that characterize the processing that the “beam time-series” type is found as “Seabed Image” generates those data products; and (2) to provide the manu- (Kongsberg Maritime) or “Snippets” (R2Sonic and Teledyne facturers of multibeam sonar systems (henceforth “manu- RESON). facturers”), the manufacturers of backscatter data process- For any given manufacturer, the representation of each ing software (henceforth “programmers”), and the users of data type may be dependent on the version of the sonar these systems and software (henceforth “operators”) with system and acquisition software. For example, backscatter an awareness of the sources of inconsistency in backscatter data in Kongsberg Maritime systems are found at a 0.1 dB data products, which tend to restrict the benefits that these resolution in newer models, but used to be recorded at a 0.5 products provide to their users. Based on our review of the dB resolution in older systems. It can also be expected that processing chain, we propose a possible common terminol- manufacturers upgrade the algorithm implemented by the ogy and nomenclature, as well as a set of recommendations firmware to calculate a given data type over time. It is there- to manufacturers, programmers and operators in order to fore important for manufacturers to always specify unam- assist reducing this inconsistency in the future. biguously the data types and versions that were recorded in the raw data files. This is necessary for programmers to use decoding adapted to each specific data type and version, Stage 1: Raw data decoding and for operators to select the data type appropriate to their needs and annotate output products with this information. The very first processing stage is the necessary decoding Knowledge of the exact source of the data at the origin of of the backscatter data values (and other required informa- final backscatter data products is essential for within-system tion) recorded in the raw data files. These data are encoded data normalization. in a binary format that is proprietary to each manufacturer and described in detail in manufacturer documentation. However, the lack of standard encoding format implies that Stage 2: Georeferencing backscatter data representation (i.e. its units and semantic meaning) typically varies across manufacturers. Moreover, The second processing stage is the calculation of the geo- this representation has continuously evolved with hardware graphical location of each sample in the backscatter dataset and software updates, resulting in variation for a same manu- (i.e. “georeferencing”). This is aided by the fact that data facturer across sonar models or firmware versions. files produced by multibeam sonar systems and their ancil- For most manufacturers, backscatter data now exist in lary sensors contain all the necessary information for such several types, with the most common ones being the “sin- calculation, including the position and attitude of the vessel gle value per beam” and the “beam time-series” types. In in the geographical frame, location and setup of the sonar the “single value per beam” type, the value recorded may head in the vessel frame, geometry of the beams, and sound be the backscatter amplitude of the sample corresponding velocity in the water column. Moreover, software for the to the bottom detection, or may be calculated from nearby processing of multibeam bathymetry data already implement samples (such as the average amplitude of the samples cor- detailed algorithms that use this information to calculate the responding to the beam footprint). The “beam time-series” precise geographical location of the sample in each beam type consists in a short sequence of backscatter samples for that corresponds to the seafloor. each beam, usually selected by the firmware as the range of Thus, if the backscatter data can be unambiguously asso- samples theoretically corresponding to the propagation of ciated with the bottom detect of each beam (i.e. the “single the pulse over the seafloor. Another common data type is the value per beam” data type), then the standard bathymetry

1 3 124 Marine Geophysical Research (2018) 39:121–137 data processing directly provides georeferencing for the where SL is the source level in the acoustic axis (in dB re backscatter data as well. For other backscatter data types, 1 µPa at 1 m), TL is the one-way transmission loss (in dB), additional processing is required to complete this georefer- TS is the target strength of the seafloor that contributed to encing stage, often drawing from “geometric corrections” the return signal (in dB re 1 m2) (MacLennan et al. 2002), methodologies originally designed for data from sidescan SH is the sensitivity of the receive array (in dB re 1 V/µPa), sonar systems (Beaudoin et al. 2002; Le Bas and Huvenne and PG is the gain introduced by the electronics from signal 2008). reception to recording in data files (in dB) [adapted from Backscatter data in the “half-swath time-series” format Augustin and Lurton (2005)]. can be georeferenced using the same type of slant-range cor- Backscattering strength is derived from Target Strength rections designed for sidescan sonar imagery, using a flat but this task is not straightforward as the Target Strength seafloor assumption. However, the bathymetry data provided is the total contribution over the total insonified area A of in each beam makes it possible to improve significantly on the backscattering cross-section bsf (in natural values, with that basic procedure by precisely locating the trace on the BSf = 10log10bsf ) occurring over area element dA and mod- ground along the swath (Beaudoin et al. 2002). ulated by the directivity patterns in transmission and recep- Backscatter data in the “beam time-series” format can tion bpT and bpR , which both depend on the transmission be georeferenced using methodologies derived from the angles θ [adapted from Hellequin et al. (1997)]: approaches discussed above. One method is to use the bathymetry information of the bottom detect in the beam to TS = 10log bs ( ) ⋅ bp () ⋅ bp ()dA 10 f T R (2) georeference the corresponding sample in the time series— A as if it were in the “single-value-per-beam” format—and then to georeference the other samples by interpolating the In practice, the common approximation is to consider bathymetry between neighboring beams. Another common instead: method is to transform these beam time-series data into TS = BS ( ) + 10log A half-swath time-series and apply the georeferencing meth- f 10 (3) odology specific to that format. Such format transformation And add the directivity patterns to the full equation for is particularly relevant for beam time-series that have been the recorded level, that is (Augustin and Lurton 2005): formed specifically as to create a continuous trace along the BL = SL + BP ( ) − 2TL + BS () + 10log A seafloor when concatenated. For beam time-series that have 0 T f 10 (4) been formed specifically to cover the beam footprint, one + SH + BPR( ) + PG can average samples between beams with overlapping foot- print so as to eventually form a continuous half-swath trace where BPT and BPR are the directivity modulations (i.e. (Augustin et al. 1994; Beaudoin et al. 2002). beam patterns) in transmission and reception, respectively (in dB). This latest equation allows retrieving BSf ( ) from the Stage 3: Radiometric corrections recorded level BL0 and the process of doing so is often termed “radiometric corrections”. In practice, most software The third processing stage consists of adjustments to the implementations consist in one single operation account- recorded backscatter level to produce a level free of unde- ing for all necessary corrections. Here we will distinguish sirable dependencies. In effect, the signal level recorded in these corrections in three themes: a Correction for the Gains the raw data files is usually not directly exploitable, as the applied in Reception (CorGR), a Correction for the effects of original transmitted sound pulse was considerably modi- propagation through the Water-column and interaction with fied by its interaction with the water-column, the seafloor, the Seafloor (CorWS), and a Correction for the Mechanical and the sonar system receive hardware and software. The Properties of the transducers (CorMP). most desirable backscatter level is the physically meaningful 2 2 backscattering strength BSf ( ) (in dB re 1 m per m­ ), which Correction for the gains applied in reception (CorGR) is only dependent on the system frequency f, the angle of incidence on the seafloor , and the morphology and compo- CorGR is the compensation of the backscatter level for all sition of the seafloor that scattered the signal (Lurton 2010). analog and digital modifications that were applied to the In practice however, the backscatter level recorded in the received signal by the hardware between the reception of files is (in dB re 1 V): the signal and its recording in the raw data files. Written PG BL0 = SL − 2TL + TS + SH + PG (1) collectively as in Eqs. (1) and (4), these modifications include analog and/or digital, static and/or time-varying

1 3 Marine Geophysical Research (2018) 39:121–137 125 gains, as well as the effects on the signal level from the piecewise TVG to correct for transmission losses, area of analog-to-digital conversion, beamforming, array shading insonification and even the seafloor angular dependence and other signal processing (Beaudoin et al. 2002; Lurton (Hammerstad 2000). Such a complex gain depends on many 2010; Parnum and Gavrilov 2011a). parameters that vary between modes of operation (pulse Analog gains are usually designed to increase with time length), pings, transmit sectors (frequency) and receive of reception in order to overcome the decaying of signal beams (beam width), and relies on accurate parameters strength with time/range and keep the signal at the input of of the environment (absorption coefficient). Systems that the analog/digital converter within its dynamic range. Since implement such complex TVG have the advantage of pro- this decay is due to phenomena represented in the sonar posing in their data files a level BL0 that approximates the equation (most importantly, transmission losses TL), such desired BS. However, their main inconvenience is that the time-varying gain (TVG) can be viewed as a desirable radio- complexity of such TVG makes its compensation more dif- metric correction. Likewise, the flexibility of digital gains ficult. Given that it is almost always possible to implement after analog/digital conversion makes them an attractive more accurate radiometric corrections in post-processing, solution for manufacturers to finalize the radiometric cor- such compensation is often desirable (Fig. 3). rection initiated by the analog TVG. This potential of using The signal processing in reception introduces other gains gains for radiometric corrections and the lack of guidelines than the TVG discussed above. For example, the modern or standards in implementation resulted in a wide variability use of frequency-modulated (FM) and amplitude-shaded in the gain designs among sonar manufacturers, sonar mod- pulses to increase signal-to-noise ratio requires a matched- els and even individual sonar systems. filter processing (“pulse compression”), which introduces a At one end of the spectrum, the simplest gain implemen- shift in the amplitude of the signal, that is, a gain in recep- tation will take the general form: tion. This gain often goes unreported and it is often unsure ⋅ whether the firmware of the various systems automati- PG(t) = K1log10(t) + K2 t + K3 (5) cally compensates for it and, in case they do, if it is done t K K K where is the time of reception and 1, and 2, 3 are con- appropriately. stant terms that are either permanently set by the hardware or Thus, it is almost always better to remove the gains from modifiable by operators during the survey through the data the recorded data, whatever their level of complexity. Such acquisition software. compensation should theoretically be unambiguous since all At the other end of the spectrum, the most complex these processes are analogically defined in the hardware, gain implementations operate a full radiometric correction digitally hard-coded in the firmware, or set by the opera- including—for example in Kongsberg Maritime systems—a tors in the acquisition software. However, the information

Fig. 3 Example of backscatter mosaic artefact induced by maladapted artefact required the complete removal of the gain, followed by a gains, and compensated in post-processing. Here, the dynamic gain more appropriate compensation of insonified area in post-processing implemented in a Kongsberg Maritime system was designed to cor- [from Augustin et al. (2013). Data courtesy of Shallow Survey 2012, rect for insonified area but fails to account for the complex bathym- processed with SonarScope] etry induced by a shipwreck lying on a flat seafloor. Correcting the

1 3 126 Marine Geophysical Research (2018) 39:121–137 is not always made available to the operator in the data files Where c is the velocity of sound (in m/s), t is the time of or clearly described in metadata or documentation. Even in reception (in s), and α is the absorption coefficient (in dB/m, the best-case scenario of properly documented gain designs, although typically reported in dB/km). their analog nature makes them prone to difference between The velocity of sound and the absorption coefficient design and hardware implementation, and change over time depend on temperature, salinity, pressure and pH, and thus due to the ageing of components. tend to vary with geographical location (e.g. proximity of A potential solution is to empirically measure the gain a zone of fresh water), time (e.g. seasons) and depth. With under all possible combinations of modes of operations and accurate (location and time-wise) measurement of sound parameter settings [see for example Rice et al. (2012)]. In velocity through depth, realistic estimates of range and cor- this context, it would make sense that in the future manu- responding depth can be obtained for each time sample. facturers would seek to minimize the complexity of gains Then, with accurate estimates of absorption through depth, to help reducing errors in their subsequent compensation (a cumulative absorption may be calculated through the path possible development aided by the increasing dynamic range of the signal (Carvalho and Hughes Clarke 2012). Measure- of modern analog/digital converters), include all necessary ments of sound velocity as a function of depth in several design details in documentation, implement a method of locations and times are often available because the frequent empirical measurement at all gain stages (with a test signal capture of sound velocity profiles is part of every multibeam perhaps), and either record those measurements in the data sonar survey procedure. However, equivalent measurement files or offer an accommodation in processing software for is often missing for the absorption coefficient, despite its these measurements. important influence on the backscatter level (Hughes Clarke If these gains are removed successfully, the resulting cor- 2012). Depth-dependent absorption coefficients could be rected backscatter level is the level received at the transducer estimated from profiles of conductivity, salinity and tem- face prior to application of gain, commonly referred to as perature taken during survey (and temperature profile are Received Level (Lurton 2010) and noted here BL1 (in dB often an additional output of sound velocity profiles) or re 1 V): oceanography databases, using the equations in Francois and Garrison (1982). However, the approximation of absorption BL BL PG SL BP TL BS  1 = 0 − = + T ( ) − 2 + f ( ) losses through the use of a single constant coefficient (usu- + 10log10A + SH + BPR( ) (6) ally calculated for an average depth) is still commonplace to date. Correction for the effects of propagation More so than depth, absorption coefficients are also through the water‑column and interaction strongly dependent on the signal frequency. This is espe- with the seafloor (CorWS) cially relevant since modern multibeam sonar systems are increasingly designed to implement variable operating fre- CorWS is the compensation of the backscatter level for the quencies between different files, pings or transmit sectors. predictable effects of the physical interaction of the sound The TVG implemented in modern Kongsberg Maritime sys- pulse with its environment, namely: the transmission losses tems includes a modelling of absorption losses that take into in the water column, and the area insonified by the pulse account both variations in frequency and cumulative absorp- within the beam width on the seafloor at the moment of tion through a depth-dependent coefficient. This method- interaction. Backscatter data processing software suites ology, described by Carvalho and Hughes Clarke (2012), implement these two compensations using different algo- should be adopted in post-processing for optimal correction rithms whose level of detail operates a trade-off between of absorption losses. realism/accuracy and speed/simplicity. The levels of detail The compensation for insonified area only requires the that are implemented to date are often following common estimation of the total area of seafloor insonified by the pulse practices that might have been devised at times where cor- for each time sample t in the return signal. Theoretically, this rection complexity was limited by computer processing area is defined by the pulse footprint on the seafloor at time power. The recent increase in computer processing power t/2, which is an annulus, modulated in space by the transmit has resulted in increasingly more realistic corrections. and receive beam patterns. There is considerable variation Transmission loss is most often modeled as a loss through in the level of realism of such a calculation (Schimel et al. geometrical spreading and a loss through absorption in the 2015). water medium, both dependent on the range R (in m): The simplest approximation is to consider a flat seafloor R = ct∕2 and an area being the product of the instantaneous foot- (7) prints in the across-track and along-track directions, which TL = 20log10(R) + R (8) are limited by the beam width in the along-track direction and in the across-track direction for beams with near-normal

1 3 Marine Geophysical Research (2018) 39:121–137 127 incidence, and by the pulse width in the across-track direc- software at an early stage of radiometric corrections, tion for beams with grazing incidence (Lurton 2010). A usually along with the gains in reception. Manufactur- more realistic approximation is to also consider the seafloor ers often list a nominal source level for different sonar slope, either estimated from the soundings in nearby beams models or modes of operation [see for example Hammer- and pings, or estimated from a model of bathymetry (Par- stad (2005) for Kongsberg Maritime systems], and the num 2007). Finally, the most realistic approach is to consider data acquisition software sometimes allow the operator the full 3D vector at transmission, modulated by the vessel to select the source level (or transmit power) and record movement, its full refraction in the water-column, and the the information in the raw data files. Information is much 3D normal vector to the seafloor at the location of interac- scarcer for the other terms. A combined transmit/receive tion (Beaudoin et al. 2004). beam pattern obtained from the testing of a prototype in a Irrespective of the level of realism, these corrections are tank is sometimes provided (usually upon request). As for strongly dependent on the effective pulse length (or equiva- receive array sensitivity, this information is almost never lent bandwidth) and the transmit/receive beam widths. available or requested. While nominal values are available from the manufacturer However, the accuracy of this information is usually and usually stored in the raw data files, the estimates nec- limited. Discrepancies between the design (and reporting) essary for an accurate correction may be very different in of a source level and its implementation are common and practice (Hughes Clarke 2012). Particularly, the pulse length necessitate measurement and correction (Rice et al. 2012). is strongly affected by pulse tapering and sampling, as well Likewise, the combined beam patterns obtained from a pro- as the transfer function of the transducer, and it is often totype are usually different from that of individual systems unknown whether the pulse length value reported in the as their construction, housing, and electronic components data is a theoretical value or an effective one considering all may differ from the model design (Hughes Clarke et al. these effects. The use of the theoretical pulse length instead 2008). In any case, system components experience degra- of the effective one introduces a bias depending on the mag- dation through time, which can affect all of these terms sig- nitude of the difference; for instance,10log 10(0.5)∼−3dB if nificantly (Hughes Clarke et al. 2008). Ideally, these terms the effective length is half of the theoretical one. Transmit would therefore be measured for each system and each mode and receive beam widths may also be very different than of operation, before its first use and through its life cycle. the nominal values. Particularly, beam width typically var- The operation of measuring the combined effect of SL (or its ies with frequency, which is very relevant for multi-sector residual after compensation of the nominal value), BPT(θ), systems. SH and BPR(θ) is often called “calibration” and its result a Ideally, if the gains in reception were accurately removed “beam pattern”. from the recorded level and the transmission losses and There are standard calibration protocols for sonar sys- insonified area were appropriately compensated, the result- tems, usually using a solid sphere of known target strength ing level, noted here BL2 (in dB re 1 V), should only be (Demer et al. 2015). These protocols are widely used for dependent on the seafloor backscattering strength and the fisheries single-beam and split-beam echosounders for mechanical characteristics of the transducer in transmission example, but are much more difficult to apply to multi- and reception: beam echosounders because of their large angular swath, small individual beams and usually large number of modes BL BL TL A SL BP 2 = 1 − [−2 + 10log10 ]= + T ( ) of operation. Nonetheless, procedures for the calibration + BSf () + SH + BPR( ) (9) of multibeam echosounders in a tank have been proposed using standard spheres (Melvin et al. 2003; Foote et al. Correction for the mechanical properties 2005; Lanzoni and Weber 2011) or extended surface targets of the transducers (CorMP) (Heaton et al. 2017). Such procedures can readily provide the initial calibration of a system before its delivery to a CorMP is the compensation of the backscatter level for the client. At present however, manufacturers do not systemati- mechanical characteristics of the transducer, that is, at trans- cally perform such initial calibration for each system they mission, the transmit power in the acoustic axis modulated produce. by the transmit beam pattern, and at reception, the efficiency Several techniques have been devised to calibrate multi- of the acoustic-electric conversion of the receive array, beam sonar systems in the field, with the main problem to modulated by the receive beam pattern. These are respec- be tackled being that the variation of seafloor backscattering tively represented by the terms SL, BPT(θ), SH and BPR(θ) strength with angle of incidence confounds the measure- in Eqs. (4, 6 and 9). ment of beam patterns, which vary with the transmit/receive Information about source level is usually available. angle. The most common solution consists of acquiring data Because of this, it is often compensated in data processing over a seafloor of homogeneous type, fitting the data with

1 3 128 Marine Geophysical Research (2018) 39:121–137 an appropriate BS model, and extracting the residual as the has been recently devised, using a dataset containing sig- desired “beam pattern” (Augustin and Lurton 2005; Fon- nificant roll movement (Tamsett and Hogarth 2016; Hiroji seca et al. 2006; Hughes Clarke 2015). Figure 4 illustrates et al. 2016; Hiroji and Hughes Clarke 2017). Under such the process devised by Augustin and Lurton (2005) for a conditions, a given small area of homogenous seafloor multi-sector system. The result is typically checked for would be insonified from the same grazing angle at seafloor consistency between several different seafloor types. Beam but different angles at transmission/reception, thus result- patterns have also been extracted from large amounts of ing in a small dataset in which the signal only varies with data spanning several seabed types and seabed morphol- transmit/receive angle, giving away the beam pattern. Such ogy, without a need for a prior correction for a model BS beam pattern would only be derived over the small angular (de Moustier and Kraft 2013). A different methodology range provided by the roll movement, but it could then be

Fig. 4 Example of a methodology to measure on the field the com- sion angle (c). An appropriate physical model of BS is fitted to the bined effect of beam patterns in transmission and reception, for indi- data (d), leaving only the beam patterns. Those patterns are modelled vidual transmit sectors. From a Kongsberg Maritime EM300 multi- by individual transmit sectors (e) to be used in further radiometric beam sonar backscatter dataset showing the effects of uncorrected corrections. Applying those beam patterns as part of the radiometric beam patterns (a) and the corresponding transmit sector extent (b), corrections remove the artefacts seen previously (f). From Augustin the average backscatter level is calculated as a function of transmis- and Lurton (2005). Data processed with SonarScope

1 3 Marine Geophysical Research (2018) 39:121–137 129 combined with other pieces of the beam pattern obtained by of parameters in most physical models of backscattering applying the methodology at the same time for all patches strength is too large to invert these models efficiently and of seafloor across the swath, thus allowing the extraction unambiguously, and that no model exists that reliably char- of a full beam pattern without a need to compensate for the acterize a heterogeneous seafloor, of which most of the seafloor backscattering strength. seafloor consists. Note that roll/pitch/yaw stabilization in modern multi- Because of these issues, angular response analyses more beam sonar systems affect beam patterns. For systems often consist in fitting simpler empirical models to the angu- without stabilization, the beam patterns may be measured lar response (e.g. Hughes Clarke 1994; Hughes Clarke et al. at angles relative to the sonar acoustic axis. For systems with 1997; De Falco et al. 2010; Lamarche et al. 2011; Huang stabilization, the beam patterns may be measured at angles et al. 2013), or using directly the angular response values relative to the vertical, within the limits of the stabilization at set angles as the parameters to be fed into a seafloor- capabilities of the system. type supervised-classification algorithms (e.g. Che Hasan Most processing software can implement a correction et al. 2012). The advantage of these approaches is that they for beam patterns, using calibration functionalities that don’t rely explicitly on physical meaningful variables, and implement one of the methods discussed above to retrieve are therefore unaffected by systematic bias in the data: as empirical estimates of the combined effect of SL, SH and long as the relative level is consistent and the variation with the beam patterns (Augustin and Lurton 2005; Leblanc and incidence angle is only dependent on seafloor character- Foster 2015; QPS 2017). These procedures have the addi- istics, the results will be the same. These approaches are tional advantage of compensating for the constant terms in therefore applicable even in the very common event that the the gains in reception that may have not been properly com- systematic terms of some radiometric corrections needed for pensated for at the first step of radiometric corrections. accurate reduction to BS are missing, such as static gains, The level resulting from an efficient implementation of all source level and receive array sensitivity. In other words, if radiometric corrections including this last calibration step is the purpose of the backscatter data processing is to produce the seafloor backscattering strengthBS : angular responses for an empirical analysis, then the radio- metric corrections in the processing chain can be simplified. BL3 = BL2 −[SL + BPT ( ) + SH + BPR( )]=BSf () (10) However, if these systematic terms change (for instance with data acquisition settings or hardware components ageing), Notes on angular response analysis the parameters determined from empirical models must be re-estimated. Thus, the interest of these methodologies is BSf ( ) is characteristic of the seafloor type and geomorphol- usually limited to the identification of differences between ogy, but also dependent on the frequency f and on the angle seafloor types within a single survey dataset. of incidence . Since the frequency of the system is known Note that the dependence of BSf ( ) with frequency and the prior radiometric corrections involved the estimation implies the possibility to characterize or discriminate of the angle of incidence, the seafloor-characteristic angular between seafloor types also on the basis of potentially differ- response can thus be directly obtained at that stage of the ent angular responses at different frequencies. The increas- processing. Ideally, data samples would be compiled over ing availability of systems operating at different frequencies, areas that are both (1) of assumed homogenous seafloor- and the trend towards new broadband systems will likely type and (2) large enough to obtain a number of samples result in backscattering strength being analyzed in the future that is sufficient to overcome statistic fluctuations and covers both as a function of frequency as well as incidence angle the entire angular range. The distribution of backscattering (Hughes Clarke 2015). strength over the angular range—or more commonly the mean backscattering strength per angle—can then be ana- lyzed to characterize the seafloor at the origin of this dataset. Stage 4: Angle dependence removal A large body of research has been dedicated to identify- ing, understanding, quantifying and modelling the several Stage 4 is the compensation of the backscattering strength physical phenomena that are responsible for the seafloor re- for its dependence with angle of incidence at the seafloor. emitting portions of an incident acoustic signal—specular Indeed, if one were to create a mosaic with the backscat- reflection, surface scattering and volume scattering (Urick tering strength retrieved after radiometric corrections, its 1983; APL 1994, 2000). In theory, fitting such physical dependence with angle would show as a strong striping models to angular response data would allow estimating oriented along the vessel track, which would hinder both some key physical parameters of the seafloor, thus allow- visual interpretation and image processing algorithms ing its identification (e.g. Fonseca and Mayer 2007). The (Fig. 5). Thus, BS is typically mosaicked only after its two main issues with this approach are that the number angular dependence has been compensated. However, such

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angle-dependent backscatter values are replaced by an angle- independent value that captures the instantaneous variation in backscatter level from the mean, scaled to the reference angle (Hughes Clarke 2012). There are two main implemen- tation choices for this approach that dramatically affect the aspect of the subsequent mosaic: (i) the methodology used to create the “expected” curve, and (ii) the choice of the angle or angular interval used as a reference. Generic angular responses measured for typical sea- floor types, or generated from canonical physical models, such as Jackson’s (APL 1994, 2000), would be possible approaches to create the expected curve. However, in most field data, both the seafloor type and its spatial variability are a-priori unknown, making this approach inapplicable. Therefore, expected curves are most often created from the data themselves, usually by computing the average of the backscatter level over a subset of data. In this case, a trade- off controls the choice in the definition of the data subset used to create the expected curve (Hughes Clarke 2012). The larger the subset, the more likely it is to overcome the statistical variation in the data and thus smooth out across- track variations, but the more likely it is to overlap several different seafloor types and to mix up their typical angular responses, creating along-track artefacts, particularly at transition between seafloor types. Conversely, a very small subset will minimize along-track artefacts at the expense of a failure to smooth out all across-track variations. A variety of approaches have been explored in that respect. Some Fig. 5 Example of georeferenced and mosaicked backscatter level without (a) and with (b) correction for angular dependence. The adopted for the creation of a single expected curve for the variation with angle of incidence in the first panel shows as a strong entire dataset, whether built from the entire dataset itself striping along the vessel track (oriented East–West in this case). Note (Preston 2009) or from a carefully selected line (Beaudoin how the width of the striping artefact differs between strongly reflec- et al. 2002). More often, several curves are created based tive and weakly reflective seabed types (respectively in light and dark tones), illustrating that the angular dependence itself depends on sea- on data subsets selected according to the data to be cor- bed type. In this example, the angular dependence was corrected by rected. At the most basic, one can use the data from each taking the beam angle as incidence angle (i.e. an assumption of a flat track line to form the curve to be used to correct the data seafloor and neglecting vessel movements and refraction in the water in that same line (Schimel et al. 2010). A more common column), removing the average value per angle calculated on each file, and not introducing an average level afterwards (i.e. no reference option is to form a curve from a series of consecutive pings angle) Figure adapted from Schimel et al. (2010) encasing the ping to correct, resulting in a “sliding win- dow” of corrective curves (e.g. Fonseca and Calder 2005; Gavrilov et al. 2005; Hughes Clarke et al. 2008; Kloser correction is not trivial because the fact that this angular et al. 2010; Parnum and Gavrilov 2011b). This approach variation is itself dependent on seafloor type and morphol- presents the advantage of allowing the operators to adjust ogy implies that it cannot be ideally corrected without the size of the “window” for better results. Going one prior knowledge of the distribution of the different seafloor step further, the early Geocoder offered a version of this types and morphology over the dataset (Hughes Clarke approach where separate curves were created for each side 2012). of the swath independently. The standard technique to correct for angular depend- The choice of the reference angle is also an important one ence consists in subtracting from the backscatter level an and there is no standard in that respect either. Fonseca et al. “expected” level for the corresponding angle of each sample (2009) used the relatively wide angular interval 20°–60° and (which normalizes the level across angles) and adding the that choice is still used in QPS FMGT and presumably other expected level for a “reference” angle or the average level subsequent versions of Geocoder. The new backscatter data within a “reference” angular interval (which reintroduces processing in Caris HIPS & SIPS uses 30°–60° (Leblanc the difference between seafloor types). This way, the local and Foster 2015). Lamarche et al. (2011) suggested using

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45° (effectively, the narrow interval 43°–47°) as a standard dimension in the across-track direction as the data density reference angle, given that it is at this angle that angular is often much higher in that dimension. responses tend to differ the most between common seafloor types. This reference is implemented in the IFREMER soft- ware Sonarscope. Stage 6: Mosaicking It is worth nothing that there are variations on the com- mon methodology described above. In the same manner as The sixth and final processing stage consists in mapping the average level is replaced with a reference average level, the angular-dependence-corrected (after stage 4) and pos- Parnum et al. (2006) and Preston (2009) have explored sibly filtered (after stage 5) backscatter level into the geo- replacing the standard deviation of the signal by a refer- referenced image that is the backscatter mosaic. “Back- ence standard deviation, thus completing a full signal nor- scatter mosaic” is a terminology introduced in sidescan malization. More commonly used but less understood are sonar data processing, at a time when single files were the three different algorithms termed “Flat”, “Trend” and processed into separate images to be combined (“mosai- “Adaptive” of Geocoder and its subsequent versions such as cked”) into one. With multibeam sonar backscatter data, QPS FMGT. While the default “Flat” is likely the standard one rarely produces individual images for each file; the method described previously, it is unsure what “Trend” and process of creating the final image more often consists in “Adaptive” do. gridding all data at once. However, the terminology has Software suites usually permit the adjustment of some become entrenched into everyday use. Mosaicking can be parameters of their angular dependence correction algo- broken down in a sequence of four processing steps. rithm, thus allowing operators to select the parameters that The first step is the gridding itself. It is defined by the minimize the angular-dependent residual artefacts in their two important decisions of choosing an appropriate grid backscatter mosaics. However, information on the details of resolution and an appropriate strategy for filling this grid the algorithm and the parameters selected are often lacking given the overlap of data from a variety of sources (dif- from the data products themselves (or their metadata), even ferent beams, pings, and files) with various quality. The though these operational choices strongly influence the final grid resolution is more or less constrained by the overall aspect of the backscatter mosaic. horizontal distance between data points, which is depend- ent on a number of parameters of the data acquisition, survey design, but most importantly on depth, with higher Stage 5: Pre‑mosaicking corrections data density in shallower waters. In contrast, the gridding strategy is largely a matter of choice. The fifth processing stage consists in operations applied to A suitable grid resolution is usually the smallest grid the angular-dependence-corrected backscatter level aimed at cell size such that the final grid contains little to no gaps enhancing the visual quality of the final image, mostly de- (that is, grid cells without data samples contributing to it). speckling and anti-aliasing. Although many operators may The two important factors in that decision are the spatial prefer to apply any visual enhancement on the final product proximity of samples, and the spatial extent of seafloor instead (that is, the backscatter mosaic), there are advantages that contributed to the signal of any given sample and can to apply at least some visual enhancement operations before thus be attributed the value of that sample (i.e. footprint). mosaicking due to the dataset still being at its full resolution The main issue is that proximity and footprint can differ at this stage. widely between data types, across the swathe and across One important operation is down-sampling the variable as dimensions. In the across-track dimension, “beam time- data at this stage often have a much higher spatial resolution series” data samples are much closer to each other than than the mosaic grid that they will be contributing to, which data samples in the form of “single value per beam”, with tends to result in aliasing. For example, data after angular proximity between samples being dependent on sampling dependence removal may be available at 0.05 m resolution, frequency in the first case and the angular interval between while the desired mosaic is to be produced at a resolution beams in the second case. In that latter case, a constant of 0.50 m. Data can thus be subsampled or averaged in bins angular interval between beams results in data samples approximating the resolution of the desired mosaic. being much closer to each other for beams intersecting Another possible operation is filtering the high-frequency the seafloor at near normal incidence (typically, near the noise that is common in acoustic data (speckle). This “de- nadir) than for beams intersecting the seafloor at grazing speckling” operation usually consists in the application of angles (typically, near the edges of the swath). However, a noise-reduction low-pass filter such as a median filter. the limiting factor is often in the along-track dimension, Applications in two dimensions (across-track/along-track) where proximity between samples depends on vessel speed are possible, but this operation is more often applied in one and ping rate, while footprint depends on transmit beam

1 3 132 Marine Geophysical Research (2018) 39:121–137 width and range, irrespective of the data type. All these conundrum is to use some weighting algorithm to operate a estimates of distance between samples will be strongly trade-off between the “blending” and “seaming” strategies, modulated by the movements of the vessel for unstabilized perhaps preferentially weighting those beam angles which systems, with uncompensated pitch and yaw leading to provide the best discrimination and least noise (de Oliveira gaps in places in the along-track direction (Hughes Clarke Junior and Hughes Clarke 2007). 2012). Finally, one must consider the increase in data den- The second step of mosaicking is the application of image sity due to the use of dual head systems, or overlapping enhancements algorithms (de-speckling, anti-aliasing, low- adjacent lines. In the case where data samples are much pass filtering, etc.) after the grid is complete. The possible closer to each other in the across-track dimension than techniques are the same as discussed at the stage of pre- in the along-track dimension (typically, using beam time- mosaicking corrections. They are indeed more often applied series on deep water systems), it is common to choose a at this stage in the two dimensions northing/easting since grid resolution that is small enough to retain the resolution the grid is the final product, with the most common opera- of the across-track data and interpolate in the along-track tion being the application of a noise-reduction low-pass filter direction (Augustin et al. 1994). such as a median filter. Gridding strategy is governed by (1) the mathematical The third step of mosaicking is the colour-scale map- operation used to compute the final value for a grid cell ping. It is the decision of how to map backscatter values in when several data points contribute to it, and (2) the man- their current unit (dB or others) to the standard 8-bit scale agement of overlapping lines. The most common gridding (pixel values ranging between 0 and 255) or 16-bit scale (0 implementations consists in taking the mean of the backscat- to 65,535) that greyscale images are usually coded in. This ter values, in linear or logarithmic (dB) units. Averaging in decision is often made subjectively by the operator for opti- linear units is the mathematically correct choice but it is mal visual interpretation. However, it is a critical choice that biased towards higher values, while averaging in logarithmic strongly impacts the aspect of the final mosaic, and a critical units will hurt the sensibility of the more mathematically- piece of information that is necessary to retrieve a dB level oriented, but produce a result that is less sensitive to the from a mosaic file, but that is very often completely over- higher outliers. For instance, considering a set composed looked. This choice includes the backscatter level bounds to of one sample showing a strong return of − 20 dB and four “crop” the data to, and the data-unit-to-pixel-value mapping samples at the very low return of − 70 dB, an average in function (linear or otherwise). No standard exists for these logarithmic units would be − 60 dB, which highlights the choices although some values appear more obvious, such overall trend of more low returns in the set, while an average as not cropping the data (i.e. keeping minimum and maxi- in linear units would be a still relatively strong − 33.9 dB, mum backscatter level), cropping at a factor of the standard highlighting the bias towards the one strong sample. Other deviation around the mean (the QPS FMGT software has mathematical operations besides the mean are also possible: the option to automatically crop to three times the standard median value or mode, for instance, which are less sensitive deviation), or using set percentiles (i.e. 5 and 95% would to outliers than the mean, or minimum or maximum values probably be suitable in most cases). It is also common of (“brightest return”), which specifically seek the outliers and operators to choose values to subjectively maximize contrast can thus be useful for detecting targets. over the range of measured values, for a feature or area of Likewise, the management of overlapping lines is an interest. The mapping method of choice is to linearly map operating choice with several options available and equally the value in dB to the pixel value, although the logarithmic suitable. A common choice is to consider each sample con- nature of the dB scale implies that it would be just as valid tributing to a grid cell independently from its line of origin, an option to use a logarithmic mapping method instead. All which “blends” the lines together but has the perhaps unde- this critical information can be summarized simply as a short sirable consequence of averaging together data from differ- annotation on backscatter maps or as part of its caption, for ent incidence angles or azimuth angles (orientation relative instance: “Data in dB units were [all kept/cropped at 5–95% to North) or inhomogeneous levels of quality (e.g. in terms percentiles/cropped at ±3 /cropped at –X to –Y dB], and of signal-to-noise ratio). Another common method is to mapped [linearly/logarithmically] to an [8/16]-bit scale”. actively avoid blending adjacent lines by limiting cell calcu- The fourth and final step of mosaicking is the choice lations that involve data from more than one line to the sam- of the color scale used to represent the data. The common ples belonging to one line only. The choice of which line to practice is to use a grayscale color bar (i.e. shades of grey keep can then be made on the basis of which line is closest in ranging from black to white), although it is not always horizontal distance from that specific cell, or which has the the case (e.g. Hill et al. 2014). It is debatable whether most appropriate angle of incidence, or signal-to-noise ratio. low backscatter should be represented in black and high In this case, the drawback is that the final mosaic shows a backscatter in white, as in Fig. 1a, or the other way around. visible “seam” between overlapping lines. A solution to the The decision to represent increasing backscatter level in

1 3 Marine Geophysical Research (2018) 39:121–137 133 increasingly bright tones is supported by the fact that approach adopted for satellite remote-sensing data (EOSDIS higher reflectivity indicates higher energy, which trans- 1986, 2017), and following the processing sequence of this lates for example in low-energy acoustic shadows in the article lee of a significant feature on the seafloor to be shown dark as in our visual perception of the shadow of objects in • BL0 : The recorded level as it is currently recorded in data the sunlight. The opposite decision to represent increasing files. Most levels currently provided by existing systems backscatter level in increasingly dark tones is supported can be considered BL0 , which mostly indicates that the by the fact that (1) this follows the original convention of provided level is not controlled at this stage and that the analogue sidescan sonars (which would print images on users can expect levels from different systems to widely a thermal paper that darkens when exposed to heat), and differ from one another. that (2) rocks are more reflective than soft sediments and • BL1 : The level obtained after applying CorGR to BL0 . would therefore appear darker than sand on a sonar image, Without the gains in reception, BL1 should be the level as on a regular photography, and help for interpretation. of the received acoustic power directly after conversion The choice is of little consequence and therefore left to from acoustic pressure by the (in dB re. the preference of the operator, but it must be clearly docu- 1 V). The term “ BL1 ” would indicate to its user that this mented in a legend or metadata. “backscatter level” requires no more compensation for gains introduced in reception by the firmware, although other types of correction are still required for this level to be exploitable. Conclusions and recommendations • BL2 : The level obtained after applying the CorWS to for backscatter data processing BL1 . Because the gains applied in reception are often rough corrections for Transmission Losses and insoni- In this article, we reviewed the backscatter data processing fied area, many current software consider the gains a chain while highlighting the varied and complex ways in “bad” CorWS, estimate a “better” one instead, compute which backscatter data processing implementation can differ. the residuals between the two and compensate BL0 for While data decoding (stage 1), georeferencing (stage 2), and them. This one-step “CorGR + CorWS” correction read- the radiometric corrections related to the hardware (CorGR ily brings the level from BL0 to BL2 , but we do encourage and CorMP in stage 3) are constrained by the system and the the use of the intermediate processing step BL1 so as to sonar equation, the information necessary to operate these stress the need for accurate documentation about gains processing stages comes with considerably varied levels of from manufacturers. The term “ BL2 ” would indicate to availability and detail between sonar systems, sonar models, its users that this “backscatter level” is free of gains and or modes of operation. In comparison, CorWS (stage 3), cor- dependence on range, and only dependent on the seafloor rection for angular dependence (stage 4), the pre-mosaicking characteristics and the angle of transmission/reception, corrections (stage 5) and the mosaicking methodology (stage so that it is suitable for the extraction of beam patterns. 6) require little to no information, but present considerable • BL3 : The level obtained after applying CorMP to BL2 . freedom in software implementation. Reducing inconsist- With an ideal correction, BL3 should be the actual sea- ency in the future will therefore require agreeing on (1) the floor backscattering strength (in dB re 1 m2 per m­ 2)—free quantity and accuracy of the information that is necessary of hardware gains, calibration measurements, transmis- for the earlier processing stages, as well as (2) standard, sion losses and area of insonification, and only dependent documented procedures for the later stages. on frequency, angle of incidence and seafloor type—and These needs may be fulfilled by some form of standard realistically the only term in the list deserving the initials coding of the various processing stages. Lamarche and Lur- “ BS ”. However, since the use of “ BS ” for uncalibrated ton (2017) suggested a possible coding, in the form of a level is still widespread, we do recommend the use of nomenclature that categorizes the alternative approaches “ BL3 ”. If acquired at a single frequency and segregated at each processing stage, thereby providing a framework by angle of incidence , BL3( ) , is an angular response metadata format for backscatter mosaics. While we fully with de facto “absolute” level, that is, readily useable for support this nomenclature as such format would provide angular response geophysical analyses. much-needed information to the user about their backscatter • BL4 : The level obtained after compensating BL3 (that is, data products, it does not entice manufacturers and program- BS ) for its dependence with angle of incidence, ready to mers to evolve towards more control in the backscatter data be mosaicked. It can be debated whether BL4 could be that sonar systems and software provide. A possible solu- instead written with mentions to the reference angle in tion is presented here, namely a sequential terminology for order to indicate that the level displayed is not the back- increasingly refined levels of processing, modelled after the scattering strength, but an “angle-corrected” version of

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it. For example, one could write instead BS45◦ for a level for the corresponding model would be welcome. These val- referenced to an incident angle of 45°—as previously ues could be output in the data themselves for unambiguous suggested by Lamarche et al. (2011)—or BS20−60◦ for a retrieval (compared to a document, or personal communica- level referenced to the average level in the 20–60° inter- tion, which rapidly gets outdated), perhaps in a dedicated val. Given that the value mapped is also dependent on “radiometric corrections” or “calibration” datagram. Meth- frequency, this concept could be extended by mentioning ods could be created to allow operators to input new values the relevant frequency, for example: BS45◦, 300 kHz for an (with new dates) in the acquisition software so that updated angle-corrected level referenced at 45° from a system that values are recorded along with new data files (even if they operated at 300 kHz. are not applied to the backscatter level BL1 recorded in the raw data files). Note that if manufacturers were to provide the neces- Recommendations to sonar manufacturers sary parameters to implement CorWS and CorMP, they would very well be able to implement their own versions Some manufacturers such as Kongsberg Maritime and Tel- of these corrections and therefore compute the subsequent edyne Reson make good attempts at providing corrected levels BL2, BL3 or BL4 “on the fly” and record them in the backscatter data that follow most of what is called for in data files. This would provide programmers and operators this document. For instance, the “Normalized Backscat- with a more refined product directly available from the raw ter” feature introduced recently by Teledyne Reson (2015) data files, ready or almost ready to be mosaicked. In many was found to give very close results to what QPS FMGT respects, this is what Kongsberg Maritime does with its produces from the raw beam time-series data. Similarly, complex TVG, and Teledyne Reson with “Normalized Back- Kongsberg Maritime refined its gains over time to reflect scatter”. However, manufacturers should ensure to make BL1 the advancements made in post-processing over the years. the default backscatter level provided so that programmers These recorded levels may very well be considered refined can always implement the superior algorithms that are often BL3 or BL4 . However, this does not mean that these recorded only possible in post processing thanks to the availability of, levels are ideal. As we saw, in many cases, the manufacturer for instance, cleaned bathymetry maps (for CorWS), more corrections need to be removed to implement better pro- recent calibration measurements (for CorMP), or new angu- cessing algorithms and these “refined levels” then become lar dependence correction algorithms. a major obstacle. Therefore, a case is to be made for manufacturers to Recommendations to software programmers provide backscatter data at earlier stages of processing, to allow flexibility in the subsequent data processing. A stand- If manufacturers implement all, or some of, the recom- ard default backscatter level to output would ideally be BL1 . mendations above, programmers should liaise with them In effect, by offering programmers a level free of all known to ensure where in the data files to find BL1 as well as the gains in reception (constant and time-varying ones), manu- parameters (with clear definitions of their units) necessary facturers would eliminate a major part of the uncertainty for CorMP and CorWS. Programmers would then be able to in their radiometric corrections. Doing so would also have design software that implement CorWS on BL1 to retrieve the additional interest for manufacturers to allow hiding any BL2 , CorMP on BL2 to retrieve BL3 and an angular depend- potentially commercially-sensitive approaches in hardware ence correction algorithm to produce some version of BL4. (e.g. time-varying gain), while providing a more controlled Each of these levels should be exportable. BL2 could then product. Until a manufacturer has sufficient knowledge and be used in input to software designed to extract new beam control of its gains to allow their own removal “on the fly”, patterns, using calibration procedures established in part- they may instead output BL0 and strive to provide informa- nership with manufacturers and researchers. BL3 , exported tion as accurate as possible for operators to remove these along with the corresponding angle of incidence, would be gains. angular response datasets, readily useable by researchers for In addition to outputting BL1 by default, manufacturers angular response analysis. would ideally provide the parameters necessary for all sub- Ideally, in the earlier stages of processing, software would sequent corrections. The parameters necessary for CorWS allow the operator to override parameter values with new (depth- and frequency-dependent absorption coefficients, ones. This is particularly important for the CorMP parame- effective pulse length or bandwidth, frequency-dependent ters that may have been re-calculated through a new calibra- beam widths) are often already provided. Ideally, this would tion, but also critical parameters of CorWS for which better be completed by parameters necessary for CorMP, that is, values may have become available, such as a measurement results of a calibration of each individual system along with of the effective pulse length or a more suitable coefficient the date of calibration. Failing so, at least generic values of absorption. For the later stages of processing, the more

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Fig. 6 Example of metadata information to be provided for each of the processing stages

1 3 136 Marine Geophysical Research (2018) 39:121–137 parameters available, the better. Particularly, the angle (or backscatter derived from Reson 8101 systems. Canadian Hydro- angular interval) of reference in the angular dependence cor- graphic Conference 2002 Proceedings, Toronto, ON Beaudoin JD, Hughes Clarke JE, Bartlett JE (2004) Application of rection should be selectable. surface sound speed measurements in post-processing for multi- For operators to fully understand their data products and sector multibeam echosounders. Int Hydrogr Rev 5:26–31 make good use of them, it is imperative for them to have Carvalho RdC, Hughes Clarke JE (2012) Proper environmental reduc- detailed information about the algorithms employed in their tion for attenuation in multi-sector sonars. In: Proceedings of the Canadian hydrographic conference, Niagara Falls, Ontario, processing. To this end, programmers are encouraged to Canada. pp 1–15 document fully (or as fully as commercial confidentiality Che Hasan R, Ierodiaconou D, Laurenson L (2012) Combining angu- allows) the steps taken in those backscatter data processing lar response classification and backscatter imagery segmentation operations. In any case, the software should always output for benthic biological habitat mapping. Estuar Coastal Shelf Sci 97:1–9. https​://doi.org/10.1016/j.ecss.2011.10.004 all processing parameters along with their output as a form de Oliveira Junior AM, Hughes Clarke J (2007) Recovering wide angu- of metadata, from the coefficient of absorption all the way to lar sector multibeam backscatter to facilitate seafloor classifica- the color scale mapping utilized. Figure 6 presents the detail tion. U.S. Hydrographic Conference. Norfolk, VA, p 22 that could be included. De Moustier C, Kraft BJ (2013) In situ beam pattern estimation from seafloor acoustic backscatter measured with swath mapping sonars. In: Proceedings of Meetings on Acoustics ICA2013, Recommendations to sonar operators vol 19, No. 1, p 070013 De Falco G, Tonielli R, Di Martino G et al (2010) Relationships between multibeam backscatter, sediment grain size and Posido- Ideally, operators would calibrate their systems regularly nia oceanica seagrass distribution. Cont Shelf Res 30:1941–1950. to produce the necessary parameters for accurate CorMP. https​://doi.org/10.1016/j.csr.2010.09.006 Demer DA, Berger L, Bernasconi M et al (2015) Calibration of acoustic However, it will be necessary to agree on feasible standard instruments. ICES Cooperative Research Report No. 326. p 133 calibration procedures first, and to have software that allow EOSDIS (1986) Report of the EOS data panel, vol IIa, NASA Techni- input for these parameters. cal Memo 87777 Beyond this, it is incumbent on operators to ensure their EOSDIS (2017) EOSDIS Handbook—March 2017. https​://cdn.earth​ data.nasa.gov/conduit/uploa​ d/6321/EOSDI​ S_Handb​ ook_1.3.pdf​ . data products have the required metadata regarding the data Accessed 15 June 2017 acquisition and subsequent processing to inform interpreta- Fonseca L, Calder B (2005) Geocoder: an efficient backscatter map tion of the data products. This metadata shall be provided as constructor. U.S. Hydrographic Conference, San Diego, CA a report, article or appendix to a dataset. 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