PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

An effective method for measuring the thickness of Cobalt-rich
Manganese Crust based on the neighborhood information and dualchannel information
Feng HONG1; Haihong FENG1; Minyan HUANG1; Binxian WANG1;
1

Shanghai acoustic lab Chinese Academy of Sciences, China

ABSTRACTThe thickness of Cobalt-rich Manganese Crusts (CRC) is one of the important information to map andestimate the volumetric distribution of deep-sea mineral. In this paper, we propose an improved method formeasuring the thickness of Cobalt-rich Manganese Crust based on the neighborhood information and dualchannel information. The high-frequency signal and low-frequency parametric signal is obtained by feeding
the signal received by the transducers centered at the array to two designed filters, respectively. The reflection
instant of the top surface is determined by the high-frequency signal, while that of the bottom surface isdetermined by rule based on the neighborhood information assuming that the CRC has the local thickness
invariability. Finally, the demonstration experiment is conducted in the tank to validate the effectiveness ofthe method.
Keywords: Thickness, Cobalt-rich Manganese Crust, Acoustic

1. INTRODUCTION
Cobalt-Rich manganese crusts(Mn-CRC) are mineral deposits containing valuable metals and canbe found on the shoulder of sea-mounts[1]. Mapping and quantitatively estimating the volumetric
distribution of these deposits is, therefore, of interest to geologists, oceanographers, and industry[2].
As a key step of the assessment, the measuring of the thickness can be performed by various methods
such as bathymetric multibeam, sidescan sonar imaging, and acoustic sub-bottom mapping. However,
because Mn-CRC have extremely uneven distribution, thin geometrical features, and being c overed
with sediment, it still remains a challenging task to collect necessary information to measure thethickness by using traditional acoustic instruments which have low resolution. To complete the taskof in-situ thickness measurements of Mn-CRC at depths up to 3000 m, another kind of device, i.e.,
the acoustic probe developed by the Institute of Industrial Science of the University of Tokyo, cansignificantly improve the spatial resolution[3,4]. The acoustic probe employs a high-power acoustic
pulse, whose sub-surface reflections are recorded [2]. By extracting the reflections from theinterfaces above and below the crust layer, the thickness can be determined if the speed of soundthrough the deposit is known [2]. The thickness measurement has been pr oven feasible after thelaboratory test and sea trial.
Motivated by their device, we built the first generation of the acoustic probe, i.e. Programmable
Phased Parametric Array Acoustic Probe 2017 (PPPAAP17), to collect and process the data both for
thickness measurement and recognition. Compared with the acoustic probe mentioned above, ourdevice consists of dual-channels, with one channel for the primary signal and the other for thedifference signal. The device has a good penetration ability which should be better than 30cm.
In this paper, we mainly focus on the thickness extraction algorithm. Typically, the thickness
information is obtained by processing the received signal which is a one-dimensional signal in timedomain that contains complicated context, with the top-bottom peak detection. The reliability of thecalculation is affected by noise in the recorded signal arising due to scattering, multi-path reflection,and seafloor features such as local inclusions inside the crust[5]. Considering that the signal quality
degraded by the system interference and ambient noise, the inner connection between the large
amounts of the recorded data should be exploited. To further improve the robustness of the thickness
extraction, we take some improvements into consideration. The processing of the dual-channel signal,i.e., the high-frequency primary signal and the low-frequency parametric signal, are used in different
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hongfeng@mail.ioa.ac.cn

way. Specifically, the reflection instant of the top surface is determined by the high-frequency signal
in one-dimension field, while that of the bottom surface is determined by the rule based on theneighborhood information in the image field assuming that the CRC has the local thickness
invariability.

2. RECEIVER SYSTEM DESCRIPTIONAs shown in Fig.1, the receiver system of PPPAAP17 mainly consists of two receiving transducers,
a filtering and compensation board, a data collection and controlling board, and the on-line processing
unit.

Reflected
signalReflected
signalReceivingtransducers

BPF forPrimary
channel A

Gain
Compensation

BPF for
Difference
channel B

Gain
CompensationFiltering and compensation
Board

Samplingθstorageθandtransfering

Thickness
calculating
algorithm

Data collection andcontrolling
Board

Thickness
information

On-line processing
softwareFigure 1 –Receiver System description
When the system works, the reflected signal is received by the two separated transducers, with one
fed into the primary channel and the other fed into the difference channel. The high-frequency signal
and low-frequency parametric signal is obtained by feeding the signal received by the transducer
centered at the array to two designed filters. The two designed filters are used to improve the Signal
to Noise Ratio (SNR) of the signal. The gain compensation part can be adjusted by the da ta collection
and controlling board to obtain suitable signal amplitude. The filter and sampling parameters are listed
in Tab.1.The sampled data are transferred and stored in real-time way and processed by using the online processing software.

Table 1 – Filter and sampling parameters
Parameter

SymbolValueCentroid frequency of the primary channel

f 0H

1 MHz

Quality factor of the primary channel

QH

5

Sampling frequency of the primary channel

f sH

5 MHz

Centroid frequency of the difference channel

f 0L

100 KHz

Quality factor of the difference channel

QL

5

Sampling frequency of the primary channel

f sL

500 KHz

System transmitting period

Twork

20 ms

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3. ALGORITHM
Primary signal processing for
top arrival time calculation
Primary
SignalEnvelope
calculation

Peak
Searching

Movingaverage

Top arrivaltime
determination
Sound velocity

Difference signal processing
EnergyDifference
Envelope
compensSignal
calculation
ation
framing

Image
Forming

Sobel
Edge
Detection

Line
ConnectionNon-local
filteringThicknessdetermininati
onThickness

Bottomarrival time
determination

Figure 2 – System composition
The aim of the algorithm is to automatically calculate the thickness of the Cobalt-Rich manganesecrusts with the large amounts of the recorded data. Theoretically, the thickness can be simply
determined by the time delay between the top and bottom reflection if the sound velocity with theferro-manganese crusts is assumed to be known. Therefore, the algorithm needs to determine threeparameters, i.e., the sound velocity, the top arrival time, and the bottom arrival time.
Generally, the sound velocity of CRCs is not a constant. A feasible way is to measure the valuesof several samples that are growing a specific area and take the averaged values as the reference
velocity. The top arrival time is calculated only using the primary signal. Although the primary signal
has a poor penetrating capability for the CRCs, the reflected signal of the top boundary has relatively
higher SNR compared with the low frequency signal. Therefore, the top arrival time can be precisely
determined by searching the peak of its envelope, which is also a good reference to assist calculating
the bottom arrival time. The envelop extraction can be realized by different method, the approximation
parameter should be set relatively small to get more accurate peak. After obtaining the peak vector, amoving average filter can be applied to filter the noise, whose window size can be set according to
the transmitting period and the speed of the ROV. The filtered peak vector will be simultaneously used
to get the top arrival time and serve as the reference to assist the determination of the bottom arrival
time.
While the signal of the primary channel is processed in the one-dimension signal field, the dataprocessing of the difference channel is performed in the image field. Because of the travelling
attenuation after travelling through the CRCs and affected by the noise, the difference signal isdegraded. Fortunately, the results of neighboring measuring can be fully employed to improve theperformance considering that during the continuous scan there is some redundant information todenoise the data. The method is listed as follows. Firstly, each difference signal which has filtered by
the BPF needs to be corrected for attenuation between the top and coarse bottom boundaries in
exponential form[5]. The envelope of each difference signal should be extracted with a relatively large
approximation parameter. Afterward, all the envelope signal is assembled as an noisy image whichneeds to be denoised. Here, we choose the filter of the nonlocal means (NLM) filter[6]. The idea ofpatch-wise methods, which weights the relative importance of pixels by comparing their patches,offers a new way to reconcile the contradiction between image denoising and texture preservation[7].
Furthermore, a Sobel detector is applied to the denoised image to obtain the possible edges. Broken
line connection is also necessary since the edge detection step uses morphological operations. The
next step is to determine which line is the bottom arrival time. By the assist of the top arrival time
vector, we only keep the edges that have the instant larger than the vector and have the instant smaller
than the time of travelling the thickest CRCs (about 35cm). If there only exist unique detected lines,
the bottom arrival time vector can be uniquely determined; Otherwise, the progressive probabilistic
Hough Transform is necessary to determine the most possible one.

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4. EXPERIMENTS
The experiments are carried out on the China Ocean 51th voyage on Aug 5, 2018. The parametricacoustic system is mounted on the hydraulic system of the HAIMA Remotely Operated Vehicle(ROV).

Transmitter
Transducer
Array
&
Receiving

Electronic
Processing
Unit

PPPAAP17
(a)(b)
Figure 3 – The Experimental configuration. (a) The PPPAAP17 mounted on the hydraulic system of the
HAIMA ROV (b) The Transmitter transducer array and receiving transducer (Uppper) and the electronic
processing unit (Lower)
The parametric acoustic system, PPPAAP17, transmits the signal for a fixed spot every 20ms andit samples and keeps the first 2ms data for each pulse. The Experimental configuration and the array
together with the electronic processing unit are shown in Fig.3. The transmitter transducer array andreceiving transducer is about 0.3 m away from the seabed at the vertical direction. It can be seen thatthe primary transducer is slightly higher than the difference transducer, which causes a fixed timedelay when receiving acoustic echo signal. The delay value can be calibrated in advance.
The primary data and difference data contain 500 records, which means the measuring time is 10
second. As shown in Fig.4(a), the envelope of the 40th primary signal is calculated with good SNR.
The arrival time corresponds with the practical distance of about 0.3m. The averaged peak vector oflength of 500 has been presented in Fig.4(b). The smooth filter is designed with the window size of
10.

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(a)
(b)
Figure 4 – Primary channel signal and the top arrival time vector. (a) Sampled primary signal (blue)andits envelope(orange) (b) Estimated top surface arrival time before smoothing(blue star) and aftersmoothing (blue line).
The data processing of the difference channel is performed in the image field. After the energy
compensation and envelope calculation operation, the energy of the difference channel forms an imageof the size of 500h1000, as shown in Fig.5(a). The NLM filter with the weight block size of 3, thesearch block size of 7, and the decay factor of 10 is applied to the energy image, whose results are
given in Fig.5(b). Comparing these two images, it can be observed that some noise is filtered with thetextures kept well.

(a)(b)
Figure 5– Energy image. (a)The energy image of the difference channel after compensation andenvelope extraction (b) The energy image after applying the NLM filter

As shown in Fig.6(a) and Fig.6(b), the Sobel edge detection operator is applied to the energy image
without and with NLM, respectively. It can be noticed that after NLM filtering, the inference signal
is largely compressed. The averaged top arrival time vector is marked as a red line in Fig6(b). Theline at its right side is the difference peak at the top surface, since the setup difference between the
primary transducer and the difference transducer. The next step is broken line connection with themorphological operations. Finally, the detected bottom arrival time line is marked with blue.

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(a)

(b)
Figure 6 –The detected lines.(a)The image with Sobel edge detection operator with no NLM filtering (b)The image with Sobel edge detection operator with NLM filtering(red: estimated top arrival time, blue:
estimated bottom arrival time)
As shown in Fig.7, the estimated thickness is calculated with the mean value of 12.81 cm and thevariance of 1.49cm, if the sound velocity within CRCs is assumed to be 2700m/s. The thickness
information corresponds with the sample obtained with the shallow drilling system.

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Figure 7– Estimated thickness values

CONCLUSION
In this paper, we briefly introduced the composition of the device and proposed an algorithm tomeasure the thickness of CRCs using dual-channel information and the context information.
Afterward, we applied the algorithm to the real data acquired by the sea trial. The results has
demonstrated that the method for measuring the thickness of CRC based on neighborhood informationand dual-channel information is effective.

ACKNOWLEDGEMENTS
This work reported herein was funded jointly by National Key R&D Program of China (Grant No.
2016YFC0302000) and the National Natural Science Funds for Young Scholar (Grant No. 618014 71).
The authors must than and our team members, Zhenya Yang, Weijie Xu, Weicai Zhang, Xiangyang
Zhu, Jianhua Fu, Huijun Wang, and Yunfeng Qin.

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