Automatic Extraction of Thickness Informationfrom Sub-Surface Acoustic Measurements ofManganese Crusts
Umesh Neettiyath1,2 , Blair Thornton3,1 , Mehul Sangekar1 , Kazuo Ishii2 ,Takumi Sato1 , Adrian Bodenmann1 , Tamaki Ura41

Institute of Industrial Science, The University of Tokyo, Komaba, 153-8505, Japan(E-mail: umesh@iis.u-tokyo.ac.jp)
2
Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, Japan
3
Southampton Marine and Maritime Institute, University of Southampton, Southampton SO16 7QF, UK
4
Center for Socio-Robotic Synthesis, Kyushu Institute of Technology, Kitakyushu, Japan

Abstract—Mapping and estimating the volumetric distributionof cobalt-rich manganese crusts (Mn-crust) is a challenging
task that lies at the centre of deep-sea mineral prospecting.
Acoustic methods are effective and capable of in-situ continuous
measurements of Mn-crust thickness, providing much higher
spatial resolutions compared to traditional methods involving
sampling. However, processing acoustic signal in order to estimate
thickness values is difficult due to low signal to noise ratios. This
paper proposes a combination of image processing techniques in
addition to acoustic signal processing in order to improve theaccuracy of measurements. The advantage is the possibility ofusing the physical properties of Mn-crust, such as local continuity
in order to recognize valid measurements. Testing the algorithm
on data collected from sea experiments demonstrate that thereflected signals from the crust can be identified, resulting in
spatially continuous thickness estimates.

I. I NTRODUCTION
Manganese crusts (Mn-crusts) are mineral deposits that cancover several thousands of square kilometres of the seafloor inregions that have been relatively free of tectonic activity andsedimentation for tens of millions of years [1], [2]. Mn-crusts
are seen as a potential source of minerals such as cobalt andtellurium by governments and industries internationally [3],[4]. This drives interest in the development of methods toquantitatively estimate their distribution and abundance.
Traditional methods of estimation, such as sampling from
Remotely Operated Vehicles (ROV) [4], dredges or coredrilling can obtain samples for detailed, pointwise measurements of thickness and composition. However, they fail tocapture the continuous local variations of the deposits. This
limitation can be overcome by in situ measurement techniques,
which can significantly improve the spatial resolution of measurements. The Institute of Industrial Science of the Universityof Tokyo developed a high power parametric acoustic probe
which uses a highly focused pulse to make measurementsof the sub-surface structure of Mn-crusts from a close rangeof 1 ∼ 2 m. The probe has a vertical spatial resolution of
about 1.4 mm and penetrates approximately 30 cm below
the top-surface of the crust, and is actively controlled by a

978-1-5090-5278-3/17/$31.00 ©2017 IEEE

double gimbal that ensures the acoustic waves always enter
the crusts normal to their surface [5]. This system is mounted
on the purpose built AUV, BOSS-A [6], together with a highresolution 3D visual mapping device.
Using the data acquired by the probe, the thickness of thecrust layer can be determined from the acoustic signals basedon the acoustic velocity. However, automated and reliable
extraction of this information in the presence of noise due
to scattering, multi-path reflections, local inclusions inside thecrust layer, fluctuating signal levels and signal attenuation is
not trivial. In order to constrain the outputs of algorithms
to extract the thickness of exposed crust layers, this work
leverages the fact that the thickness of the layers is typically
locally continuous. The proposed method uses a combinationof image processing and acoustic signal processing, and instead of considering signals individually, translates successive
measurements into a spatial frame and applies image processing techniques to find layers that are consistent.

Fig. 1. Picture of Mn-crust sample (thickness = 85mm)

The rest of the paper is organized as follows. The acoustic

The measurements begin with the firing of a short amplitude modulated 2 MHz burst, and recording its reflectionsfor 4.096 ms where measurements are repeated at a samplinginterval of 50 µs. The signal shown in Fig. 2 shows part ofa single pulse. An arbitrary number of pulses (denoted as N)
are stacked together to form a frame. N is decided by theuser depending on the number of pulses to be processed. The
frame is formed in such a way that each pixel is a signal
value, each vertical line from top to bottom is the recordingof a single pulse, and these are stacked consecutively fromleft to right. Each frame is treated as a 2-D grayscale image.
Since the axes are aligned with respect to time, it does notalways scale linearly to distance. In order to reconstruct thephysical scales, the data are plotted on a distance scaled axes
based on the vehicle’s navigation data and the angle of thegimbals in later processing stages. This is described in detailin section III.
III. A LGORITHM AND P ROCESSING W ORKFLOW
Fig. 2. Principle of thickness measurement

probe and its principle of operation are described in Section
II. Section III details the proposed method. In Section IV,
implementation of the algorithm and results are presented. The
results are discussed in Section V.
II. S YSTEM OVERVIEW
The acoustic probe emits a 2 MHz amplitude modulated
signal, which generates a 200 kHz parametric wave that penetrates the seafloor. The beam is focused on the seafloor suchthat it projects a spot of 3 cm diameter at a distance of 1.5 m.
The probe is an improved version of the probe developedin [7], which used a 100 kHz wave modulated on a 1 MHz
carrier wave. The probe records the reflections of the wave at asampling rate of 2 MHz. Figure 2 shows a typical measurement
scenario on a Mn-crust sample. The recorded signal, in an ideal
scenario, will consist of two major reflections, one from the
top of the seafloor (i.e. top of the Mn-crust) and another fromthe interface between the Mn-crust and the substrate rock onwhich it is formed. The time delay between the reflections
is twice the thickness of the crust multiplied by the velocity
of sound in Mn-crust. Using the velocity measured in priorstudies [7], the thickness is calculated.
The reliability of the calculation is affected by noise in therecorded signal arising due to scattering, multi-path reflections, and seafloor features such as local inclusions inside thecrust [8]. In areas without Mn-crusts, there will not be a second
reflection. The signal, therefore, may not contain two uniquely
identifiable reflections. In such cases, misidentification of top
and bottom reflections may occur or the identification might
fail altogether. Since Mn-crusts are hydrogenetic deposits, thethickness can be assumed to not vary abruptly; this will beviolated in noisy measurements, resulting in an output which
is not locally continuous.

The process of finding thickness of Mn-crust from therecorded data consists of four steps - filtering individual
pulses, extracting signal boundaries, re-framing the signal intoa distance based grid, and identifying reflections to calculate
thickness. The complete algorithm is listed in Fig. 3.
The pulse transmitted from the acoustic probe is a parametric wave whose shape can be approximated into an exponentially decaying sine wave. By deconvolving this signal withthe reflected signal, the exact instants where the reflections
happened can be identified from the recorded signal. However
deconvolution is highly sensitive to noise and noise levels
are very high in underwater measurements. Therefore, anequivalent operation was performed by calculating the crossspectral density between the two signals. The recorded signal
was cropped using a moving rectangular window and thecross spectral densities are calculated at regular intervals. An
analysis of the resultant spectrum reveal that the strongest
components in the spectrum occur around 200 kHz, the transmitted signal frequency, as shown in Fig. 4. The spectral
components that fall within the 3 dB bandwidth - which was
identified to be between 70 kHz and 300 kHz - are selected anda filtered pulse is reconstructed by adding these components
to increase the signal to noise ratio.
The reconstructed signals are assembled into an image
frame using the methodology explained in section II. This
image is then filtered using a median image filter in orderto remove shot noise without blurring the image. In theframe, the reflections from the crust are continuous regions
having relatively high intensity values. They are selected bythresholding the image using an adaptive threshold value.
Otsu’s method is used for calculating the threshold value [9].
The adaptive threshold will ensure that variations in signal
levels across different datasets arising due to changes in theunderwater environment and changes in the system tuning does
not affect the results. The resulting binary image will haveseveral small disconnected peaks. In order to consider these
into the signal region, a morphological closing operation isFig. 3. Processing Steps

performed using a rectangular kernel of 6 × 11 pixels. The top
and bottom boundaries of the signal region is then extracted
as the first non-zero line with a minimum width (a value ofapproximately 7.5 mm was used) from the top and bottom of
the image respectively. Further processing focuses only in thisregion to find the reflections and thickness.
The physical nature of Mn-crusts dictate a dependence on
spatial scales and is independent of temporal aspects such as
the frequency of measurement or the speed of AUV. Thus, forfurther processing, the signals are transformed into a spatial
2-D frame. A point in 3-D space is identified for each of thepulses as the highest point of the top line. The coordinates ofthese points are calculated using the localization informationof the AUV and the pose of the acoustic probe using acoordinate transformation. A line is then traced through these

55
−
60
−

Power (dB)
−
−
75
70
65
80

−
85

−

0

0

0

70

80

90

00

0
60

10

0
50

0
30

0

0
20

40

0
10

0

−

Require: For the selected area, identify the pulses to beprocessed
Require: Calculate pose information for the robot and acoustic probe
1: for each pulse in the frame do
2:
Calculate 3D location of each point
3:
Pass the pulse through cross spectral density function
and split the components
4:
Discard values in noisier frequencies
5:
Using selected components, reconstruct a filtered pulse
6: end for
7: Assemble N pulses into a frame ⊲ N decided by the user
8: Run a median filter on the frame and remove noise
9: Threshold the image into binary image ⊲ Otsu’s method
10: Using morphological closing, fill gaps in the binary image
11: Identify the top and bottom limits of the signal region
12: Trace a virtual line in 3D space along the top reflection
13: Integrate along the line to find distance traveled
14: Segment the trace distance into uniform intervals
15: Vertically offset each pulse based on depth
16: Interpolate pulses from step.5 into the new x-z space
Ensure: A signal frame with time axes is converted to framewith distance axes; where signals can be spatially understood intuitively
17: Run a median filter on the frame
18: Compensate for attenuation in the region of interest
19: Run a canny edge detector to locate signal transitions
20: Discard edges outside the boundaries identified in step.11
21: On the edge image, run a progressive probabilistic houghtransform to detect continuous lines
22: Discard steep lines and repeated lines
23: Discard pulses over non-crust areas ⊲ if the classification
information is available
24: Calculate strength of reflection for each line
25: Identify bottom reflection as the strongest line
26: Wherever reflections are available, calculate thickness
return An array of thickness estimates

Frequency (kHz)Fig. 4. Cross correlation spectrum. The 3 dB bandwidth is highlighted

points in 3-D space, which is considered as the top surface ofthe Mn-crust. The distance along the this line is calculated andis used as the horizontal coordinate for the frame. The trace
distance is sampled at uniform intervals of defined horizontal
resolution (the authors used a resolution of 0.01 m, whichis the approximate average physical distance between two
adjacent pulses). The vertical resolution was calculated using
equation 1.
Vres =

xshif t · νsound2 · fs(1)where νsound = 2700 m /s is the velocity of sound in
Mn-crust [7], xshif t is the width by which the window isshifted while calculating the cross-correlation spectrum, and
fs = 2 MHz is the rate of sampling of the measured signal.
Although this vertical resolution does not accurately describe
all parts of the image due to the change in speed of sound, it isvalid throughout the region of interest of the signal calculated
in the previous step and thus can accurately describe thereflections inside the crust.
The signals are plotted similar to the previous frame, left
to right and top to bottom, with the vertical axis of the frame
compensated for each pulse separately based on probe’s depth
for each pulse. The horizontal location of each pulse is interpolated using a one dimensional nearest neighbor interpolation
using k-D trees. The result is an image frame consisting of
signal reflection intensities with physical locations to scale,from which the reflections and therefore the thickness can be
determined.
The image is then filtered using a 2D median filter for
removing noise using a square kernel of approximately 5 mmsize. The filtered image is then corrected for attenuation,
between the top and bottom boundaries of the signal’s regionof interest. Equation 2 shows the calculation performed.

(

γ i [z] × 102αf (z−ztop )/20
γ i [z]if ztop ≤ z ≤ zbot
elsewhere(2)
where γ i [z] denotes the pixel at vertical coordinate z andhorizontal coordinate i. The signal’s region of interest, as identified in step 11 is between ztop and zbot . αf = 1.266dB /cm
is the attenuation coefficient of Mn-crust at the measuring
frequency, calculated using the value measured in [7].
The final steps of calculation consists of identifying thelines corresponding to the secondary reflections within theregion of interest. The primary reflection is identified as the
top of the signal region. Since each reflection indicates atransition of signal levels, a canny edge detector is used to
extract edges in the frame. A progressive probabilistic houghtransform then detects lines having a minimum length andcontinuity [10]. The probabilistic progressive hough transformis chosen over other line detection methods because it is able
to detect line segments along with their start and end points
with a specified minimum length. Among all lines detected,
overlapping lines and steep lines are discarded (the authors
used a threshold value of slope of 10◦ with respect to the
top reflection). The secondary reflection, originating from the
Mn-crust - base rock interface, is assumed to be the strongestof all reflections present in the signal. In order to determine
the stronger reflections, the remaining lines are analyzed to
calculate a strength of reflection, which was defined as thesum of intensities of all points lying on the line. The imageis then scanned horizontally to find the secondary reflections.
Wherever lines overlap, the line having highest intensity is
selected as the secondary reflection. Finally, the thickness iscalculated as the difference between the top and bottom lines.
γαi [z] =IV. I MPLEMENTATION AND R ESULTS
The proposed method is verified using the data collectedduring field experiments conducted at No.5 Takuyo seamountlocated in the northwest Pacific Ocean. Results from two
research cruises, namely KR16-01 and NT13-13 are used.
NT13-13 cruise was conducted in 2013 and performed surveyof the Mn-crusts using the ROV HyperDolphin using theacoustic probe, at depths ranging between 1400 and 1700
metres. The ROV also collected a number of samples witha mean thickness of 60 mm, one of which with a measured
thickness of 85 mm is shown in Fig. 1. KR16-01 researchcruise used the research vessel Kairei and surveyed Mn-crusts
using the AUV Boss-A in 2016. Visual 3D mapping systems
on board the AUV and ROV collected data which is used forreconstructing the 3D colour maps of the seafloor.
The proposed method is verified on the data collected overa patch of seafloor of approximate length of 8.1 m, a visual
3D reconstruction of which is shown in Fig. 5. This data
was collected during the BSA-032 dive using Boss-A. The
initial processing steps are shown in Fig. 6. The acoustic
data collected is shown in Fig. 6a as a colour-coded image
frame. The values are scaled along a logarithmic axis with

Fig. 5. A snapshot of the seafloor showing the region from where acoustic
reflections were obtained

lighter colours indicating a higher signal amplitude level. Afterfiltering the individual pulses and the signals are arranged into
an image frame as shown in Fig. 6b.
The image is thresholded into a binary image followed bya morphological closing operation resulting in Fig. 6c. The
limits of the signal region are identified and are shown in
Fig. 6d overlaid on Fig. 6b. The top boundary is plotted asa blue line and the bottom boundary is plotted as a green
line. Tracing the top line in 3D space result in Fig. 7. The
traced line has a length of about 22 m on a patch of about
8 m distance; this behavior arises due to the gimbals whichcauses the acoustic beam to move in a nonlinear fashion on
the seafloor. The signals are then interpolated into a distancebased axes resulting in Fig. 8a; the horizontal resolution of theaxes is chosen to be 10 mm and vertical resolution is calculated
to be about 1.9 mm.
After filtering and attenuation correction, a canny edge
detector is applied on the signal region of the frame resulting
in the black edges shown in Fig. 8b. On this edge image,within the limits identified in Fig. 6d, the potential secondaryreflections are identified using progressive probabilistic houghtransform. The calculation used a minimum line length of
60 cm and a maximum gap of 10 cm as parameters. These
parameters ensured that long lines are detected despite smallergaps, while small inclusions that result in pulses with ideal
reflections are excluded. The resulting lines are plotted as
red lines in Fig. 8b. Optimal candidates for the secondaryreflections are identified based on the intensity of reflections
and thickness values are calculated. The estimated thickness
values are shown in Fig. 8c.
The mean thickness of the crust in the region was calculated
to be approximately 65 mm, which is consistent with theobservations made during the cruise. The thickness values
are plotted as a colour coded bar graph above the seafloor
reconstruction in Fig. 9. The figure shows that the detection
is consistent with the coverage of crust, with no reflections
identified over sand section in the middle of the patch.
V. C ONCLUSION
This paper proposed a method to estimate the thickness ofmanganese crust deposits from sub-surface acoustic reflections
using techniques from image and signal processing. By assembling subsequent pulses into an image frame and interpolating

2500

12
:0
3:
24

12
:0
2:
54

12
:0
2:
24

3000
12
:0
1:
54

Pulse travel time (µS)

2000

Pulse transmit time
2000

2500

3:
24
12
:0

2:
54
12
:0

12
:0

12
:0

2:
24

3000
1:
54

Pulse travel time (µS)(a) Image frame consisting of 1800 recorded signals cropped to the region containing signals

Pulse transmit time
2000

2500

:0
3:
24
12

:0
2:
54
12

12

12

:0
2:
24

3000
:0
1:
54

Pulse travel time (µS)(b) Image after CSD and filtering

Pulse transmit time
2000

2500

Pulse transmit time(d) Detected top and bottom reflections on signal data
Fig. 6. Steps leading up to detection of the signal boundaries

:2
4
12

:0
3

:5
4
12

:0
2

:2
4
:0
2
12

:0
1

:5
4

3000

12

Pulse travel time (µS)(c) Image after thresholding and closing

[6] Yuya Nishida, Kenji Nagahashi, Takumi Sato, Adrian Bodenmann, Blair
Thornton, Akira Asada and T. Ura, “Autonomous Underwater Vehicle
BOSS-A for Acoustic and Visual Survey of Manganese Crusts,” Journalof Robotics and Mechatronics, vol. 28, no. 1, pp. 91–94, 2016.
[7] B. Thornton, T. Ura, M. Sangekar, and K. Ohira, “A case study todemonstrate remote acoustic measurement of ferro-manganese crust
thickness,” Ocean Engineering Symposium, 2009.
[8] U. Neettiyath, T. Sato, M. Sangekar, A. Bodenmann, B. Thornton,
T. Ura, and A. Asada, “Identification of Manganese Crusts in 3D
Visual Reconstructions to Filter Geo-registered Acoustic Sub-surface
Measurements,” in Oceans 2015, 2015, pp. 1–6.
[9] N. Otsu, “A threshold selection method from gray-level histograms,”
IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp.
62–66, 1979.
[10] J. Matas, C. Galambos, and J. Kittler, “Progressive Probabilistic Hough
Transform,” Transform, vol. 24, no. 4, pp. 303–316, 1998.

Ground trace distance (m)

20

15

10

5

03
:2
4
12
:

02
:5
4
12
:

02
:2
4
12
:

12
:

01
:5
4

0

Pulse transmit time
Fig. 7. Integrated trace distance of the signal

them into a distance scaled image, the authors could identify
reflections using probabilistic line detection algorithms. This
allowed the locally continuous nature of Mn-crust deposits
to be leveraged in detecting the sub-surface crust-substrateboundary. Improved thickness estimates can lead to higher
reliability in estimating the volumetric distributions of Mncrust.
ACKNOWLEDGEMENT
The authors would like to thank the Hyper-Dolphin team,
R/V Natsushima crew, and R/V Kairei crew, Japan Agency
for Marine-Earth Science and Technology (JAMSTEC) during
the NT13-13 and KR16-01 cruises. This work was supportedby the Japanese Ministry of Education under the Program forthe Development of Fundamental Tools for the Utilization ofMarine Resources.
R EFERENCES[1] A. Usui, I. J. Graham, R. G. Ditchburn, A. Zondervan, H. Shibasaki, and
H. Hishida, “Growth history and formation environments of ferromanganese deposits on the Philippine Sea Plate, northwest Pacific Ocean,”Island Arc, vol. 16, no. 3, pp. 420–430, 2007.
[2] A. Usui, K. Nishi, H. Sato, Y. Nakasato, B. Thornton, T. Kashiwabara, A. Tokumaru, A. Sakaguchi, K. Yamaoka, S. Kato, S. Nitahara,
K. Suzuki, K. Iijima, and T. Urabe, “Continuous growth of hydrogenetic
ferromanganese crusts since 17Myr ago on Takuyo-Daigo Seamount,
NW Pacific, at water depths of 800-5500m,” Ore Geology Reviews, 2016.
[3] M. Clark, R. Heydon, J. Hein, S. Smith, C. Smith, S. Petersen, E. Baker,
and Y. Beaudoin, Deep Sea Minerals: Cobalt-rich Ferromanganese
Crusts, a physical, biological, environmental, and technical review,
E. Baker and Y. Beaudoin, Eds. SPC - EU Deep Sea Minerals Project,
2013, no. July.
[4] J. R. Hein, K. Mizell, A. Koschinsky, and T. A. Conrad, “Deep-oceanmineral deposits as a source of critical metals for high- and greentechnology applications: Comparison with land-based resources,” Ore
Geology Reviews, vol. 51, pp. 1–14, 2013.
[5] B. Thornton, A. Asada, A. Bodenmann, M. Sangekar, and T. Ura,“Instruments and methods for acoustic and visual survey of manganesecrusts,” IEEE Journal of Oceanic Engineering, vol. 38, no. 1, pp. 186–
203, jan 2013.

z (m)

1413

1414

1415

1416
0.0

2.5

5.0

7.5

10.0

12.5

15.0

17.5

20.0

Trace Distance [m]Thickness (mm)(a) Image frame after interpolation into distance axes
20
70
120
170
0

5

10

15

20

Distance (m)(b) Output of canny edge detector and lines detected

Thickness (mm)

120

80

40

0
0

5

10
Distance (m)(c) Estimated thickness values
Fig. 8. Edge detection and thickness estimation

Fig. 9. Colour coded output of thickness plotted over the 3D map

1520