Automatic Detection of Buried Mn-crust Layers
Using a Sub-bottom Acoustic Probe from AUV
Based Surveys
Umesh Neettiyath1 , Blair Thornton2,1 , Harumi Sugimatsu1 , Takayuki Sunaga3 , Junya Sakamoto3 , Hikari Hino3
1

Institute of Industrial Science, The University of Tokyo, Komaba, 153-8505, Japan(E-mail: umesh@iis.u-tokyo.ac.jp)
2Southampton Marine and Maritime Institute, University of Southampton, Southampton SO16 7QF, UK
3Japan Oil, Gas and Metals National Corporation, Toranomon, 105-0001, Japan

Abstract—A method for estimating the thickness of sediments
covering buried layers of deep sea Cobalt-rich Manganese Crusts(Mn-crusts) from acoustic sub-bottom sonar data, recorded using
an Autonomous Underwater Vehicle (AUV), is described. The
acoustic data is analyzed with a combination of image and signal
processing techniques to identify the optimal reflections comingfrom the seafloor and the buried layer. The method is applied
to data collected from a field experiment and the results werevalidated using core samples from the same area; showing a highmatch. By including buried layers into volumetric estimation of
Mn-crust deposits, resource potential of seafloor areas can be
determined with better accuracy.

I. I NTRODUCTION
Cobalt-rich manganese crusts (Mn-crust) as a seafloor deposit is increasing in prominence recently as it has highcontents of valuable metals including Cobalt, Nickel, andrare earth elements [1]–[4]. These hydrogenetic deposits areformed mainly on the slopes and shoulders of seamounts
ranging from 800m to 2400m [5]. The International Seabed
Authority has assigned exploration slots to several countries for exploring these resources, and eventually exploiting
them [6].
Different methods of surveying have been attempted bystakeholders with exploration rights and other researchers
studying Mn-crusts. Physical sampling methods such as coredrills or sampling from an ROV is useful for detailed analysis
and confirmation of Mn-crusts [5], [7], [8]. Towed camera
surveys are used for visual inspection to identify various
seafloor types and the presence of exposed Mn-crust layers [9].
For large area resource estimation, ship based multibeam
bathymetry and backscatter data are used together with othersensor data such as core samples, sub-bottom sonars, etc. [7],[10]–[12]. While sampling gives high accuracy thickness measurements with very low spatial resolution, multibeam andcamera studies cannot measure thickness values. This became
further complicated when buried Mn-crust layers were foundbelow sediments, which cannot be seen in camera images.
Thickness and coverage are important results necessary toestimate the volume of Mn-crust. In order to accurately estimate the complete resource potential, an integrated approach isnecessary. Attempts to find the resource potential of seamount978-1-6654-1821-8/22/$31.00 ©2022 IEEE

scale areas by using multibeam backscatter data by verifying
them with towed video surveys and sub-bottom profiler data is
usually limited by the sparse thickness information collectedby core sampling. In order to get continuous thickness information, an acoustic probe for in-situ contactless thickness
measurement, and an underwater robot to conduct continuous
surveys using the acoustic probe were developed by theInstitute of Industrial Science of the University of Tokyo [13].
This system has been successfully deployed for Mn-crustvolumetric surveys [14] and is being actively used. Similar
acoustic probes were developed later by other researchers aswell [15], [16].

Fig. 1. Seafloor area mosaics from Mn-crust covered areas, mapped by AUV
Boss-A. (a) nodule (and sediment) covered (b) exposed Mn-crust deposits (c)Mn-crust partially covered by sediments. This paper attempts to improve crust
detection in mainly areas such as (c)The proposed paper intends to expand the scope of theprevious work [14] by considering buried crust layers. While
the previous works focused on identifying and estimating
exposed Mn-crust deposits, some sediment covered areas were
found to be having layers below. Later studies found buried
Mn-crust deposits in these areas. This research proposes analgorithm to calculate the thickness of these sediment deposits.
By including shallow buried, potentially Mn-crust, layers
along with the exposed Mn-crust deposits while estimatingThis is a DRAFT. As such it may not be cited in other works.
The citable Proceedings of the Conference will be published inIEEE Xplore shortly after the conclusion of the conference.

crust coverage, more accurate volumetric distribution maps
can be made. This method is particularly relevant for areas
intermittently covered with sediments, an example of which isshown in figure 1(c).
Rest of this paper is organized as follows. A descriptionof the AUV, the acoustic probe and other sensors used forsurvey are described in section II. Section III explains thealgorithm developed for identifying the buried layers in thesediment and calculating its thickness. Section IV analyzes
data collected during a field survey using the techniques
proposed in section III and validates the results using coresample data. Conclusions and future work are presented in
section V.
II. S YSTEM OVERVIEW
The data is collected by an AUV, called Boss-A, built by
the Institute of Industrial Science at the University of Tokyo
for surveying Mn-crusts [17]. The AUV surveys the seafloor
from an altitude of ∼ 1.5 m using acoustic and visual sensors
as shown in figure 2.
The acoustic system is based on a high power acoustic probe
tuned for measuring Mn-crust thickness [13]. A 200 kHz
signal, modulated on a carrier signal of 2 M Hz is directed
at the seafloor and the reflections are recorded. The recorded
signal typically consists of reflections from the top of theseafloor and the bottom of the Mn-crust layer in crust covered
areas. Whereas, in sediment covered areas, presence of asecond reflection indicates a buried layer. The thickness canthen be calculated by multiplying the time delay between thepeak reflections with the velocity of sound in Mn-crust or
sediment as appropriate.

Fig. 2. Boss-A AUV measurement subsystems and components.

Since acoustic measurements are sensitive to the angle ofincidence of the waves on the seafloor, the acoustic probe
is mounted on a double gimbal mechanism which orients it
normal to the seafloor [18]. This is done by calculating theorientation of the seafloor in real-time, using the data recordedby the visual system.

The visual system employs a light sectioning method togenerate a 3D colour map of the seafloor using a sheet laser
and a single camera and is based on [19]. The output of thesystem is a 3D colour point cloud of the seafloor with a swath∼ 1.5 m which is roughly equal to the survey altitude, and an
across-track resolution of ∼ 1.4 mm [14].
III. M ETHODS AND W ORKFLOW
The proposed algorithm builds upon the algorithm built
by the authors in [14], [20], and follows the same conceptof stacking adjacent recorded acoustic signals into an image
frame and performing image analysis techniques, in addition
to typical signal processing techniques to detect layers. The
complete flowchart of the algorithm is given in figure 3.
The signals recorded by the acoustic probe, in the ideal case,consists of two reflections - one reflection from the top of theseafloor, i.e. the top of the Mn-crust or sediment layer, and one
from the bottom of the layer. In deep sediment layers, beyond
the penetration depth of the probe, which is ∼ 30 cm as per
the specifications, no secondary reflections will be present.
However, in practice, the data is corrupted by noise arising
due to scattering, multi-path reflections, and seafloor features
such as local inclusions.
Initially, each received signal is analyzed individually tolocate the reflection arising from the seafloor. The seafloor
reflection is usually the strongest reflection. First, the envelopeof the signal is extracted using Hilbert transform. A peakdetection algorithm is then used to detect the local maxima
in the signal. Then, the first peak having a sufficient signal tonoise ratio (SNR) is selected as the seafloor reflection.
The major difference between detecting the bottom reflections of Mn-crust layers and the reflections from below
the sediment layers is the intensity, continuity and spread of
the signals. Mn-crust bottom reflections are sharper and canbe detected using the intensity alone. However, in order toensure the continuity of the thickness and remove noise from
inclusions, a layer detection algorithm was developed [14].
In contrast, reflections from below the sediment layers are
cluttered, spread over longer time periods and weaker. Also,
there is considerable variation in reflections at different areas.
Therefore, in order to detect buried layers, a different
analysis is performed. First, in order to remove shot noise,
the signal data is initially filtered using a median filter. In
order to obtain a high filtering performance, the resulting data
is processed with a deconvolution based filter, denoted as CSD
Filter in the flowchart shown in figure 3. Since deconvolution
is highly sensitive to noise and noise levels are very highin underwater measurements, an equivalent operation was
performed by calculating the cross spectral density (CSD)
between the transmitted pulse and the received signal. The
received signal is cropped using a moving rectangular window
and the filter is applied. The signal is then filtered with abandpass filter centred around the transmitted signal frequencyof 200 kHz. The 3 dB bandwidth of the result was calculated
to be between 70 kHz and 300 kHz and a filtered pulse isreconstructed by adding the filtered components, yielding ahigh signal to noise ratio.

Visual data​

Acoustic data​

Vres =

Hilberttransform​
CSD filter​

Peakdetection​
Seafloor

3D colour map
Reprojection​

Segmentation

Layer
Detection
Classification

Buried Layer

Seafloorclass?
Sediment/
Nodules

Sediment
Thickness

resolution depending the speed of the AUV during the survey;
in this case, the authors used a resolution of 0.01 m, whichis the approximate average physical distance between two
adjacent pulses when surveying at a speed of 0.2 kn. The
vertical resolution was calculated using equation 1.

Mn-crust
Mn-crust
Thickness
Estimation
Mn-crust
Thickness

Fig. 3. Flowchart showing the analysis of AUV collected data to estimate
the thickness of sediment deposits. The results at each stage of the acoustic
data analysis is shown in figures 5 and 6

In the reprojection step, the CSD filtered acoustic data is
projected into a distance-time axes using the seafloor location.
The recorded data is represented on a time-time axes graphinitially, with x-axis being the time when the signal was transmitted by the acoustic probe and y-axis being the time when
the reflections were received by the probe. After reprojection,
the x-axis is retained, but the y-axis is converted into a distancescale by multiplying the time with the speed of sound in
sediment. This allows for easy visualization and analysis of thedata with the goal of sediment thickness measurement. Also,
only the relevant portion of the signal having strong reflections
is retained and the rest are discarded. This is achieved by
selecting the CSD filtered data around the seafloor location
detected in the previous step and copying them into a newimage frame.
The acoustic image reconstructed above has a horizontal

xshif t · νsound2 · fs(1)where νsound is the velocity of sound in sediments, xshif t
is the width by which the window is shifted while calculating
the cross-correlation spectrum, and fs = 2 MHz is the rateof sampling of the measured signal. Although this verticalresolution does not accurately describe all parts of the image
due to the change in speed of sound, it is valid throughout
the sediment region of interest of the signal calculated in theprevious step and thus can accurately describe the reflections
inside the crust. In the example used in this paper, the verticalresolution was found to be 2.1 mm.
Since the areas of interest in the signal are continuous
regions having relatively high intensity values, the signal isgrouped into segments of equal width. Then, detection of thebottom layer is done using a signal intensity weighted integral
function. For each segment and each potential thickness value,
a cumulative signal level is calculated and the strongest
cumulative sum is selected as the bottom layer. In sediment
filled areas, this can be assumed to be the reflection originatingfrom the buried layer.
A filtering is then performed to isolate areas covered withsediment or nodules and discard Mn-crust covered areas using
seafloor classification data. 3D colour reconstruction of theseafloor, generated from the camera images recorded by the
robot; is classified into Mn-crust, sediment or nodules by
a machine learning algorithm using the method describedin [14]; and is used for this purpose. Finally, a thickness
estimate is made by multiplying the time difference withthe speed of sound in sediment. A sound speed value ofνsound = 1640 m /s is used, which is calculated using theformula given by [21] for grain size values measured from the
Mn-crust covered seamounts. The thickness of the Mn-crust
is calculated separately using the previous method.
IV. R ESULTS FROM F IELD S URVEYS
The proposed method is used to analyze a portion of thedata collected by AUV Boss-A, during field deployments
at an undisclosed location. Since core samples were alsocollected from this area by Japan Oil, Gas and Metals National
Corporation (JOGMEC) using the Benthic Multi-coring System (BMS); this data was borrowed to validate the proposed
method.
The seafloor patch which was analyzed is about ∼ 18 m
in length and ∼ 1.7 m in width and lies at a depth of ∼1337 m. Figure 4 shows the 3D reconstruction of the surveyed
area as top view and bathymetric map. Most of the seafloor
is covered in sediments with rocky crusts projecting out inplaces, particularly in the middle and extreme right of the
figure.

Fig. 4. (a) Top view of 3-D reconstruction (b) bathymetric map withhillshading of a seafloor patch ∼ 18 m in length from the area surveyed
by the AUV. This section contains mostly sediments with occasional rocks
projecting out. Four of the samples from this area, collected using the BMS
system, are used for validating the proposed algorithm. Their locations areshown as red triangles marked 1 to 4.

Fig. 6. Analysis steps leading up to the thickness of the sediment layer. (a)CSD filtered acoustic data aligned to the seafloor reflection. The seafloor isdenoted as 0 mm and distance is calculated w.r.to it (b) integrated intensity
value for a segment width of ∼ 50 cm (c) best thickness candidate for eachsegment, shown as a green line (d) estimated thickness of the sediment layer.

Fig. 5. Steps in analyzing the acoustic data corresponding to the seafloor
patch shown in figure 4. (a) acoustic data recorded by the probe plottedon a logarithmic scale (b) processed signal after CSD filtering, showing theburied layer more clearly (c) the detected seafloor reflection, plotted over the
Hilbert transformed acoustic data (d) seafloor classification, made from the
3D reconstruction, matched with the acoustic data.

The acoustic data and the results at various stages of processing are shown in figures 5 and 6 . The raw acoustic datacollected is shown in figure 5(a). Although it is plotted on a log
scale, the buried layer reflections are not clearly visible. The
resultant data after CSD filtering is shown in figure 5(b), whichclearly shows the below sediment reflections. Figure 5(c)shows the Hilbert transformed signal and the detected seafloor
reflections. Figure 5(d)shows the classification of seafloor
made from the 3D reconstructions as a colour overlay overthe corresponding acoustic signal.
The reprojected signals are shown in figure 6(a). The signal
is segmented at a width of 50, which is approximately 50 cm
and the cumulative signal values are estimated as shown in andthe final thickness values estimated are shown in figure 6(b).
The segments having the strongest cumulative intensity isshown in figure 6(c), plotted as green lines over the reprojectedsignals. The final thickness estimate for the sediment deposit,
filtered with the classification data, is plotted in figure 6(d).
A validation of the results was performed using core sam-

ples collected by the BMS from near the survey transect.
Figure 7(a) shows the locations of the samples on the transect
using red triangles. The locations of acoustic measurements
made by Boss-A are shown using red dots in the same image.
A total of 4 samples were used, with sediment cover varying
from 6 mm to 124 mm in thickness. The Mn-crust layer onthese samples was of the order of several tens of millimeters
on Calcareous conglomerate substrate. The acoustic data isshown in figure 7(b); and the detected seafloor and buried
layer reflections, along with the classification data are shownin figure 7(c). The calculated thickness values are plotted infigure 7(d) in black. The BMS measurements, mapped to thenearest thickness measurement are shown using red bars andthe exact values are denoted above the figure.
It can be seen that samples 2,3 and 4 matches the thickness
estimates closely, indicating the validity of the proposed
method. Since the estimate is in steps, continuity of the
thickness information is resolved into step size, which in turndepends on the segment width. A longer segment skips overcontinuous thickness variations, but is robust to noise such as
local inclusions and signal level changes. The authors foundthat a value of 50 cm provides a good balance.
There is a large difference between BMS1 (6 mm) and thenearest thickness estimate (70 mm). On close inspection, it
can be seen that these two points are about 50 cm away fromeach other. BMS1 is positioned between several exposed Mncrust pieces, indicating that the sediment between them could
be thin. Since the measurement point is further away, thesediment layer could be thicker. Also, the stronger filtering andamplification are performed to locate the below seafloor layers,
makes it harder to identify the transition points where theexposed crust goes under the sediment deposits and becomes
the buried layer. However, visual data which is collected alongwith the acoustic data can be used to detect these regions, but
is out of scope of the present work.
While the proposed algorithm estimates the thickness of thesediment layer, it cannot identify the type of the buried layer
or determine the thickness of the Mn-crust layer, if present.
However, the sediment thickness in itself is valuable information and an estimate of the thickness can be interpolated
from neighboring exposed Mn-crust areas, albeit less accuracy.
Further verification of the proposed algorithm is planned using
a testbench setup. Also, while the present study only analyzed
a short section of the seafloor, the algorithm must be scaled toanalyze the large volumes of data collected using the robot.
V. C ONCLUSION
The authors proposed a method for detecting shallow buried
layers of potential Mn-crust deposits in sub-bottom acoustic
data collected by an AUV. This was achieved by stacking subsequent acoustic pulses into a frame and performing image andsignal processing techniques to detect an optimal buried layer.
The results were validated using core samples collected fromnear the surveyed area. Further validation is being planned
by desk experiments. By incorporating potential Mn-crustlayers below shallow sediments into the volumetric distribution

estimates, in addition to exposed Mn-crust deposits, enhanced
estimates of Mn-crust distribution can be made for large areasusing autonomous robotic surveys, including past surveys.
ACKNOWLEDGMENT
The data in this paper have been collected as a part ofsponsored projects by Agency for Natural Resources andEnergy, Ministry of Economy, Trade and Industry (METI),
Japan, and Japan Oil, Gas and Metals National Corporation(JOGMEC). Part of this work was supported by the Japanese
Ministry of Education under the Program for the Developmentof Fundamental Tools for the Utilization of Marine Resources.
The authors also thank Dr. Kazunori Mizuno from the University of Tokyo for his help with sound velocity calculation.
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Fig. 7. Results and validation of the thickness estimates using samples collected by the BMS (a) 3D map (top view) of the area surveyed by AUV Boss-A
showing the location of the acoustic measurements and BMS sampling locations (b) processed acoustic data from the area clearly showing buried layers(c)seafloor and buried layer detection plotted over the acoustic data and the seafloor classification data, made using the visual data (d) sediment thickness
values estimated by the algorithm (black) compared with the thickness values measured by the BMS system (red). The exact BMS thickness values are noted
above the figure. Mn-crust thickness values are not shown.