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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Mar. Sci.</journal-id>
<journal-title>Frontiers in Marine Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Mar. Sci.</abbrev-journal-title>
<issn pub-type="epub">2296-7745</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fmars.2024.1370274</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Marine Science</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Sediment classification in the paleo-oceanic environment based on multi-acoustic reflectance characteristics in the Southern Tianshan Mountains</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Zhen</surname> <given-names>Huancheng</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2410042"/>
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</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Cao</surname> <given-names>Xinghui</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Qu</surname> <given-names>Zhiguo</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Zou</surname> <given-names>Dapeng</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/113303"/>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Xiong</surname> <given-names>Shuai</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2410509"/>
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<contrib contrib-type="author">
<name>
<surname>Song</surname> <given-names>Jiang</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
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<contrib contrib-type="author">
<name>
<surname>Guo</surname> <given-names>Hao</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
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<aff id="aff1">
<sup>1</sup>
<institution>Xinjiang Key Laboratory of New Energy and Energy Storage Technology, Xinjiang Institute of Technology</institution>, <addr-line>Aksu</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>College of Information and Communication Engineering, Harbin Engineering University</institution>, <addr-line>Harbin</addr-line>, <country>China</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, School of Electromechanical Engineering, Guangdong University of Technology</institution>, <addr-line>Guangzhou</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Guangming Kan, Ministry of Natural Resources, China</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Vahid Tavakoli, University of Tehran, Iran</p>
<p>Purna Sulastya Putra, National Research and Innovation Agency (BRIN), Indonesia</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Xinghui Cao, <email xlink:href="mailto:caoxinghui1982@163.com">caoxinghui1982@163.com</email>
</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>21</day>
<month>08</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>11</volume>
<elocation-id>1370274</elocation-id>
<history>
<date date-type="received">
<day>14</day>
<month>01</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>30</day>
<month>07</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2024 Zhen, Cao, Qu, Zou, Xiong, Song and Guo</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Zhen, Cao, Qu, Zou, Xiong, Song and Guo</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>The grain size of sediments is a crucial parameter in sedimentology, with significant implications for submarine engineering and water conservancy projects. In this study, we developed an acoustic reflection measurement system using a self-developed, high-precision, high-frequency shallow stratigraphic profiler. The system's accuracy was validated with standard acrylic samples. Results showed that within the sediment grain size range of 0.3 to 2.5 mm, the acoustic reflection amplitude increased with grain size. However, distinguishing grain sizes between 0.1 and 0.3 mm from those between 1.0 and 1.5 mm based solely on reflection amplitude proved challenging. Notably, the differences in wavefront flare shapes between these grain sizes were readily apparent. Therefore, combining reflection peak amplitude with time-domain waveform analysis enables more precise sediment grain size classification.</p>
</abstract>
<kwd-group>
<kwd>sandy sediments</kwd>
<kwd>fine measurement</kwd>
<kwd>waveform characteristics</kwd>
<kwd>wide-band transducer</kwd>
<kwd>pulse compression</kwd>
</kwd-group>
<contract-sponsor id="cn001">Natural Science Foundation of Xinjiang Uygur Autonomous Region<named-content content-type="fundref-id">10.13039/100009110</named-content>
</contract-sponsor>
<counts>
<fig-count count="8"/>
<table-count count="1"/>
<equation-count count="4"/>
<ref-count count="29"/>
<page-count count="10"/>
<word-count count="3601"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Marine Biogeochemistry</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>Sediment classification is a prominent research topic in the fields of underwater acoustics and geology. Measuring the acoustic reflection characteristics of sediments serves as a crucial technical approach for investigating sediment classification. Establishing a correlation between the acoustic reflection characteristics of sediments and the types of sedimentation enables the inversion of sediment physical parameters. The process of deriving physical parameters from acoustic parameters to classify sediments holds significant scientific importance for the theory of geoacoustic inversion (<xref ref-type="bibr" rid="B12">Jackson and Richardson, 2007</xref>; <xref ref-type="bibr" rid="B14">Li et&#xa0;al., 2021a</xref>; <xref ref-type="bibr" rid="B23">Wang J. et&#xa0;al., 2023</xref>).</p>    <p>The correlation between the acoustic reflection characteristics and physical properties of substrate sand and gravel has mainly been established through <italic>in situ</italic> measurements and laboratory studies (<xref ref-type="bibr" rid="B10">Hamilton, 1980</xref>; <xref ref-type="bibr" rid="B16">Liu et&#xa0;al., 2013</xref>; <xref ref-type="bibr" rid="B29">Zhengyu et&#xa0;al., 2015</xref>; <xref ref-type="bibr" rid="B26">Zhang et&#xa0;al., 2017</xref>; <xref ref-type="bibr" rid="B14">Li et&#xa0;al., 2021a</xref>; <xref ref-type="bibr" rid="B15">Li et&#xa0;al., 2021b</xref>). One direct method for obtaining underwater acoustic reflection characteristics is <italic>in situ</italic> measurement. For example, <xref ref-type="bibr" rid="B28">Zheng et&#xa0;al (2013)</xref>. calculated seafloor reflection and attenuation coefficients based on seafloor profiles. Additionally, they quantitatively estimated the average grain size and corresponding sediment classification using the Biot model. However, this method can be costly and inefficient since the information about the seafloor only applies to discrete locations. Acoustic waves collected through sonar systems are a low-cost and effective means of detecting substrate structure and sediment type. For instance, the reflection coefficient (RC) estimated from acoustic echoes can be used to infer the mean grain size (<xref ref-type="bibr" rid="B8">Hamilton, 1970</xref>). <xref ref-type="bibr" rid="B13">Ji et&#xa0;al (2020)</xref> suggested that using acoustic remote sensing to classify seafloor siltation is an attractive method with a high coverage capacity and low cost compared to seafloor sampling. This research focuses on improving the accuracy of seafloor silt classification through backscattering intensity correction, sonar image quality enhancement, and classifier construction. The effectiveness and superiority of the selected optimal random forest (SORF) classifier were verified through comparison with the support vector machine (SVM) and random forest (RF) classifiers. The multi-beam echo sounding system records seafloor backscattering intensity data, which provide information about seafloor geological features. Acoustic inversion estimates the density of surface sediment layers, sediment sound velocity, and medium attenuation (<xref ref-type="bibr" rid="B15">Li et&#xa0;al., 2021b</xref>). Numerous studies have been conducted to identify various sediment types using acoustic echoes (<xref ref-type="bibr" rid="B17">Marsh and Brown, 2008</xref>; <xref ref-type="bibr" rid="B7">Fonseca et&#xa0;al., 2009</xref>). Moreover, there is a body of literature (<xref ref-type="bibr" rid="B4">Cui et&#xa0;al., 2021</xref>; <xref ref-type="bibr" rid="B24">Wang H. et&#xa0;al., 2023</xref>) utilizing deep learning methods for sediment classification. <xref ref-type="bibr" rid="B1">Anokye et&#xa0;al (2024)</xref>. proposed a novel method for seafloor sediment classification using a multibeam echo sounder system and a convolutional neural network (CNN), thereby improving classification accuracy. <xref ref-type="bibr" rid="B19">Qin et&#xa0;al (2021)</xref>. employed side-scanning sonar images in conjunction with different depths of a CNN. Pre-training the model using the greyscale CIFAR-10 dataset enables the transfer of parameters across a wide range of tasks, thereby improving the overall performance of the model and reducing the error rate of classification. However, in these studies (<xref ref-type="bibr" rid="B23">Wang J. et&#xa0;al., 2023</xref>; <xref ref-type="bibr" rid="B25">Wendelboe et&#xa0;al., 2023</xref>), the common practice is to first measure the acoustic properties of the sediments <italic>in situ</italic> and then sample them. The physical property parameters of the sediments are measured in the laboratory, and parameters such as the average grain size of the sediments are obtained. The extracted acoustic reflectance characteristics are based on the characteristic information of the mixed sediments. Fewer scholars have paid attention to the acoustic reflection characteristics of the fine distribution of particle sizes. Such research requires specific sediment grain sizes, which can be limited by sampling. Additionally, the use of sonar equipment with higher degrees of refinement is necessary for studying sediments with fine particle sizes. As a result, sediments with fine particle sizes have not been fully explored.</p>
<p>The study of specific sediments requires a sonar instrument capable of supporting refined measurements under laboratory conditions. Our laboratory has developed an in-house sonar that meets these requirements. This sonar instrument emits a very narrow beam, and in the mid- and high-frequency bands, the measurements are free from side-lobe interference. In contrast, general sonar equipment typically generates multipath interference from side lobes, which prevents clear echo distinction and complicates fine measurements in these frequency bands. To address this, we developed a unique transducer to support laboratory fine measurements.</p>
<p>Furthermore, the southern foothills of the Tianshan Mountain, where our study is located, were once a paleo-marine depositional environment (<xref ref-type="bibr" rid="B20">Song et&#xa0;al., 2016</xref>; <xref ref-type="bibr" rid="B21">Tao et&#xa0;al., 2023</xref>). Some studies suggest this region belongs to the Late Ediacaran-Early Cambrian stratigraphy (<xref ref-type="bibr" rid="B3">Chang et&#xa0;al., 2021</xref>), while others propose an Ordovician period (<xref ref-type="bibr" rid="B27">Zhang and Munnecke, 2016</xref>). The sandy sediments in this area exhibit good homogeneity of grain size due to natural sorting processes. Based on this, we screened six sandy sediment samples with grain sizes ranging from 0.1 to 2.5 mm using a standard sieve. Under laboratory conditions, we then used a high-frequency submersible sub-bottom profiler (HF-SSBP) to investigate the relationship between the sediments and acoustic reflection signals.</p>
<p>The remainder of this paper is organized as follows: Section 2 outlines the measurement principle and method. The experimental setup and sample preparation are detailed in Section 3. Section 4 presents the analysis and discussion of the data results. Finally, a concise summary is provided in Section 5.</p>
</sec>
<sec id="s2">
<label>2</label>
<title>Measurement methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>Measuring device</title>
<p>An iron water tank measuring 4 m &#xd7; 2.5 m &#xd7; 1.7 m was utilized in the experiment. The bottom of the tank was lined with a 100-mm-thick acrylic plate, which held the sediment samples in acrylic buckets. A small aerial crane was employed to lift the various test samples. The transducer and polyformaldehyde (POM) plate were connected and suspended from the iron frame at the top of the water tank. The POM plate was attached to the center of a fixed axis, allowing the transducer to move vertically and adjust its distance from the sediment surface. During testing, the transducer remained parallel to the sediment surface and was kept suspended directly above it. <xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref> illustrates the schematic diagram of the measurement setup.</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Schematic diagram of the measuring device.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1370274-g001.tif"/>
</fig>
<p>The experimental study is a mechanistic investigation of the relationship between sediment grain size and acoustic reflection. However, field sediments are typically mixtures of multiple grain sizes, complicating mechanistic studies. Therefore, sieving naturally sorted sediment grain sizes in the laboratory to study acoustic reflection at different grain sizes is a fundamental aspect of understanding mixed grain sizes under field conditions.</p>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Acoustic reflection signal processing</title>
<p>The HF-SSBP device utilizes a linear frequency modulation (LFM) signal as the transmit signal and receives an echo signal that is a superposition of multiple reflection signals in the time domain. This can make it difficult to distinguish between the interfaces of the sediments, acrylic, and water. By processing the raw data obtained using the shallow stratigraphic profiler with a pulse compression algorithm, it is possible to extract the reflection information at the interface between the water and sediment. First, the time-domain signal undergoes orthogonal demodulation. Next, the demodulated result is filtered using a low-pass filter to remove the signal&#x2019;s carrier frequency. Finally, the signal obtained from the low-pass filter is processed using the LFM signal as a matched filter, resulting in the computation of the interface reflection intensity. The pulse compression algorithm comprises several main steps. For more detailed information on the pulse compression algorithm, please refer to the literature (<xref ref-type="bibr" rid="B5">Curlander and Mcdonough, 1992</xref>).</p>
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</mml:mrow>
</mml:msub>
<mml:mo stretchy="false">(</mml:mo>
<mml:mi>t</mml:mi>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> and <inline-formula>
<mml:math display="inline" id="im5">
<mml:mrow>
<mml:msub>
<mml:mi>s</mml:mi>
<mml:mrow>
<mml:mi>c</mml:mi>
<mml:mi>o</mml:mi>
<mml:mi>s</mml:mi>
</mml:mrow>
</mml:msub>
<mml:mo stretchy="false">(</mml:mo>
<mml:mi>t</mml:mi>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> of the time-domain signal <inline-formula>
<mml:math display="inline" id="im6">
<mml:mrow>
<mml:mi>s</mml:mi>
<mml:mo stretchy="false">(</mml:mo>
<mml:mi>t</mml:mi>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula>. <xref ref-type="disp-formula" rid="eq3">Equation 3</xref> represents the computational expression for the complex signal. <inline-formula>
<mml:math display="inline" id="im7">
<mml:mrow>
<mml:mi>P</mml:mi>
<mml:mo stretchy="false">(</mml:mo>
<mml:mi>t</mml:mi>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> is the low-pass filter; <inline-formula>
<mml:math display="inline" id="im8">
<mml:mrow>
<mml:mi>S</mml:mi>
<mml:mo stretchy="false">(</mml:mo>
<mml:mi>t</mml:mi>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> is the complex signal; and <italic>M(t)</italic> is the matched filter; <italic>V(t)</italic> represents the strength of reflection for a multilayer signal. <xref ref-type="disp-formula" rid="eq4">Equation 4</xref> represents the mathematical model for calculating the reflected intensity of the multilayer.</p>
</sec>
</sec>
<sec id="s3">
<label>3</label>
<title>Experiments</title>
<sec id="s3_1">
<label>3.1</label>
<title>Testing equipment</title>
<p>To investigate the acoustic echo signals of specific sediment grain sizes, we utilized an HF-SSBP (<xref ref-type="bibr" rid="B2">Cao et&#xa0;al., 2022</xref>). The HF-SSBP uses LFM signals with an operating carrier frequency of 110 kHz, a bandwidth of 30 kHz, and a coherent signal for echo reception. The transducer structure is a transceiver combination that employs a novel broadband design. It also incorporates very low side-lobe technology, resulting in a side-lobe to main-lobe ratio of &#x2212;17.1 dB. To ensure that the transducer received reflective signals from all sediment surfaces, we selected an acrylic bucket with an appropriate diameter for the sediment samples. During the experiment, it was found that an acrylic bucket with a diameter of 30 cm could fully capture the acoustic signal, whereas buckets with diameters of 20 cm and 40 cm were less effective. Consequently, the experimental tests were conducted using a 30-cm-diameter bucket to ensure that all reflected signals originated from the sediment and not from other spatial reflections. This setup allowed for precise measurements even in confined spaces. <xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref> presents the device parameters.</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>HF-SSBP parameter configuration.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="center">Technical parameter</th>
<th valign="top" align="center">Index</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="center">Signal waveform</td>
<td valign="top" align="center">LFM</td>
</tr>
<tr>
<td valign="top" align="center">Bandwidth</td>
<td valign="top" align="center">30KHz</td>
</tr>
<tr>
<td valign="top" align="center">Carrier frequency</td>
<td valign="top" align="center">110KHz</td>
</tr>
<tr>
<td valign="top" align="center">3dB Beam width</td>
<td valign="top" align="center">5.8&#xb0;</td>
</tr>
<tr>
<td valign="top" align="center">Wave beam sidelobe</td>
<td valign="top" align="center">-17dB</td>
</tr>
<tr>
<td valign="top" align="center">Transducer</td>
<td valign="top" align="center">Uniform wide band transmitter&#x2013;receiver</td>
</tr>
<tr>
<td valign="top" align="center">Signal receiving form</td>
<td valign="top" align="center">Coherent</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>HF-SSBP, high-frequency submersible sub-bottom profiler; LFM, linear frequency modulation.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Sample preparation</title>
<p>In order to conduct this experiment, samples of sandy sediment were collected from the Kumarik River basin. The sandy sediments in this area are transported by rivers. Due to the natural sorting effects of wind and other environmental factors, it was deemed appropriate to select naturally sorted sandy sediments, which are more representative of those formed in natural environments than man-made sand. However, the grain size of the naturally sorted sediments is not very uniform. Therefore, the sediments were further sieved into six grain sizes: 0.1-0.3 mm, 0.3-0.5 mm, 0.5-1.0 mm, 1.0-1.5 mm, 1.5-2.0 mm, and 2.0-2.5 mm, using a standard sieve in the laboratory. To remove very fine sand particles and clay attachments from the sediments, the sieved sediments were placed in a bucket and mixed with clean water. By repeatedly stirring and decanting the supernatant, most of the clay attached to the sediments was washed away after several repetitions. Finally, the grit was loaded into an acrylic bucket and stirred to settle. The resulting saturated sandy sediments, with a thickness of 8 cm, were placed in an acrylic bucket with a diameter and height of 300 mm. The thickness should not be too thin to avoid an unstable sound field and should not be too thick to prevent internal inhomogeneities and ensure strong echo information from the bottom of the sediment. These considerations help verify and calibrate the measurement results. The sediment samples were prepared in June 2023 and tested in October 2023 after a deposition time of approximately 90 days. <xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref> shows the surface map of the sediment samples, illustrating the gradual increase in particle size and surface roughness across the six sediments.</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Six grain size sediment samples: <bold>(A)</bold> 0.1-0.3 mm; <bold>(B)</bold> 0.3-0.5 mm; <bold>(C)</bold> 0.5-1.0 mm; <bold>(D)</bold> 1.0-1.5 mm; <bold>(E)</bold> 1.5-2.0 mm; <bold>(F)</bold> 2.0-2.5 mm).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1370274-g002.tif"/>
</fig>
</sec>
</sec>
<sec id="s4" sec-type="results">
<label>4</label>
<title>Result and analysis</title>
<sec id="s4_1">
<label>4.1</label>
<title>Test results and data processing</title>
<p>To verify the accuracy and stability of the measurement process, we placed an acrylic bottom with a depth of 10 cm at the bottom of the tank. We then measured the reflection signal from the transducer at intervals of 80 cm, 85 cm, 90 cm, 95 cm, 100 cm, and 105 cm from the surface of the acrylic. The data were processed using the pulse compression algorithm to extract the characteristic peaks. Two reflection peaks were observed: one at the interface between the water and acrylic, and the other at the interface between the acrylic and the bottom of the water tank. However, the amplitude of the reflection peak at the interface between the acrylic and the bottom of the water tank was stronger than that at the interface between the water and acrylic. This may be due to the strong reflection at the bottom of the water tank, resulting in a signal amplitude that exceeded that of the reflection between the water and acrylic interface. The reflected signal amplitude decreased linearly as the transducer moved away from the acrylic surface, indicating good accuracy and stability of the experimental process.</p>
</sec>
<sec id="s4_2">
<label>4.2</label>
<title>Time domain analysis</title>
<p>To analyze the effect of sediment grain size on the shape of the acoustic reflection signal echo, we examined the time-domain diagrams of the echo signals at the sediment surface 90 cm from the transducer. The time-domain diagrams of the six grain sizes are shown in <xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>. The purple area in <xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref> represents the initial 80 &#x3bc;s segment of the received signal following reflection by the sediment. Variations in the waveform within this region indicate sediment grain size differences. The reflection peak amplitude is relatively large for grain sizes A and F, and it gradually increases with increasing grain size for B, C, D, and E. For particle sizes A and B, the three echoes of the reflected signal are clearer. As particle size increases, the superposition trend of the three interfaces of the reflected echoes becomes more pronounced. The reflected echoes exhibit different wavefront flare shapes, with the angle of the wavefront flare being larger for several grain sizes, except for grain size E. This may be related to the porosity of the sediment and other factors.</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Time domain visualization of six particle sizes. <bold>(A)</bold> 0.1-0.3 mm; <bold>(B)</bold> 0.3-0.5 mm; <bold>(C)</bold> 0.5-1.0 mm; <bold>(D)</bold> 1.0-1.5 mm; <bold>(E)</bold> 1.5-2.0 mm; <bold>(F)</bold> 2.0-2.5 mm).</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1370274-g003.tif"/>
</fig>
</sec>
</sec>
<sec id="s5" sec-type="discussion">
<label>5</label>
<title>Discussion</title>
<sec id="s5_1">
<label>5.1</label>
<title>Six sediment reflection peaks</title>
<p>The characteristic reflection peaks of each grain size were extracted from the reflection signals of six sandy sediments using the pulse compression algorithm. The results are presented in <xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4</bold>
</xref>. The black line represents the first antipodal amplitude, the reflection peak at the water-sediment interface. The blue line represents the second antipodal amplitude, the reflection peak at the interface between the bottom of the sediment and the acrylic bucket. The red line represents the third antipodal amplitude, the reflection peak at the interface between the bottom of the tank and the acrylic bucket. These results are consistent with the pulse compression results shown in <xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5</bold>
</xref>, which illustrate the clear layering of the three interfaces. The reflection peak amplitude at the water-sediment interface is the strongest. As the distance between the sediment surface and the transducer surface increases, the reflection peaks at the water-sediment interface decrease to varying degrees. <xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4C</bold>
</xref> shows a situation where the amplitude of the third reflection peak (acrylic and the bottom of the water tank) is higher than those of the first and second reflection peaks. This could be due to the mutual interference between the bottom of the water tank, the acrylic, and the laboratory floor. It should be noted that our water tank was made of iron and was only 5 mm thick. <xref ref-type="fig" rid="f4">
<bold>Figures&#xa0;4A&#x2013;F</bold>
</xref> show that the amplitude of the second peak is not stable. In <xref ref-type="fig" rid="f4">
<bold>Figures&#xa0;4A, D</bold>
</xref>, the second reflection peak is located between the first and third peaks. In <xref ref-type="fig" rid="f4">
<bold>Figures&#xa0;4B, C, and F</bold>
</xref>, the amplitude of the second reflection peak is the smallest. In <xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4E</bold>
</xref>, the difference in the amplitudes of the second and third peaks is not noticeable. Therefore, the first reflection peak is particularly valuable for studying sediments.</p>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>Reflectance peaks in sandy sediments of six grain sizes. <bold>(A)</bold> 0.1-0.3 mm; <bold>(B)</bold> 0.3-0.5 mm; <bold>(C)</bold> 0.5-1.0 mm; <bold>(D)</bold>1.0-1.5 mm; <bold>(E)</bold> 1.5-2.0 mm; <bold>(F)</bold> 2.0-2.5 mm.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1370274-g004.tif"/>
</fig>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>Waveforms of pulse compression of acoustic reflection signals from sandy sediments ranging in size from 1.0-1.5 mm.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1370274-g005.tif"/>
</fig>
</sec>
<sec id="s5_2">
<label>5.2</label>
<title>Analysis of the first reflection peak</title>    <p>To investigate the relationship between sediment grain size and reflection peak amplitude, we analyzed the extracted values of the first reflection peak amplitude and sediment grain size. The results are presented in <xref ref-type="fig" rid="f6">
<bold>Figure&#xa0;6</bold>
</xref>. In general, the amplitude of the first reflection peak increases with increasing sediment grain size. This trend is consistent with the findings of (<xref ref-type="bibr" rid="B11">Ivakin and Sessarego, 2007</xref>, <xref ref-type="bibr" rid="B6">Eleftherakis et&#xa0;al., 2014</xref>). in the high-frequency broadband range. However, grain sizes A (0.1&#x2013;0.3 mm) and F (2.0&#x2013;2.5 mm) do not exhibit an obvious amplitude correlation. This may be due to the positive correlation between reflection peak amplitude and grain size within a specific range of grain sizes. The findings of <xref ref-type="bibr" rid="B9">Hamilton (1972)</xref> are similar, indicating that acoustic attenuation is lower in coarse sand and clay sediments, but higher in fine sand and silt sediments. As the sediment surface moves away from the transducer, the reflection peak amplitudes decrease to varying degrees. For particle size E, the reflection peak amplitude remains relatively stable. This may be because the acoustic emission signal of the sediment surface is less sensitive to distance changes at this particle size. The roughness of the sediment surface is caused by varying degrees of roughness.</p>
<fig id="f6" position="float">
<label>Figure&#xa0;6</label>
<caption>
<p>Peak amplitude of reflections at the water-sediment interface for six sediments.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1370274-g006.tif"/>
</fig>
</sec>
<sec id="s5_3">
<label>5.3</label>
<title>Signal of the first reflection peak histogram</title>
<p>A calculation was performed based on the speed of sound in the sediment as presented in the literature (<xref ref-type="bibr" rid="B18">Park et&#xa0;al., 2023</xref>; <xref ref-type="bibr" rid="B22">Tian et&#xa0;al., 2023</xref>) and the thickness of the sediment as measured in the laboratory. This calculation yielded a one-way transmission propagation time of the sound wave in the sediment of 80 &#x3bc;s. By calculating the one-way propagation time of the sound wave in the sediment and the propagation time in the water, we can establish the time of the first wave of the signal reflected from the sediment surface. This allows us to determine the return signal of the sediment layer without interference. The mean value of the signal amplitude was plotted after extracting the 80 &#x3bc;s signal. The results are presented in <xref ref-type="fig" rid="f7">
<bold>Figure&#xa0;7</bold>
</xref>, which shows the consistency between the histogram of the 80 &#x3bc;s signal and the trend of the first wave of the time-domain echo signal.</p>
<fig id="f7" position="float">
<label>Figure&#xa0;7</label>
<caption>
<p>Histograms of 80 &#x3bc;s signals from six sandy sediments. <bold>(A)</bold> 80 cm; <bold>(B)</bold> 85 cm; <bold>(C)</bold> 90 cm; <bold>(D)</bold> 95 cm; <bold>(E)</bold> 110 cm; <bold>(F)</bold> 105 cm; <bold>(G)</bold> 110 cm; <bold>(H)</bold> 115 cm.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1370274-g007.tif"/>
</fig>
</sec>
<sec id="s5_4">
<label>5.4</label>
<title>Sediment classification</title>
<p>
<xref ref-type="fig" rid="f8">
<bold>Figure&#xa0;8</bold>
</xref> illustrates the first wave amplitude and waveforms of the reflection signals for six grain sizes of sandy sediments at different distances. It can be observed that the grain sizes of the sediments and the amplitude of the first reflection peaks are approximately positively correlated. Furthermore, the amplitude of the first reflection peaks increases gradually with the increase in sediment grain sizes. When the particle sizes are A, C, and D, the amplitude of the sediment reflection peaks exhibits minimal variation, whereas the time-domain waveforms show pronounced differences. As shown in the upper time-domain waveform in <xref ref-type="fig" rid="f8">
<bold>Figure&#xa0;8</bold>
</xref>, the beam opening angle is greater for particle size A. In contrast, particle sizes C and D exhibit smaller beam opening angles than A. The difference in reflected peak amplitude between particle sizes E and F is not particularly large, but the beam opening angle of the reflected waveform for particle size E is relatively small, while that for particle size F is relatively large. Therefore, the combination of acoustic reflection amplitude and echo waveform can be used to more finely distinguish sediment grain sizes.</p>
<fig id="f8" position="float">
<label>Figure&#xa0;8</label>
<caption>
<p>Classification of sediment particle size.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fmars-11-1370274-g008.tif"/>
</fig>
</sec>
</sec>
<sec id="s6" sec-type="conclusions">
<label>6</label>
<title>Conclusions</title>
<p>The laboratory research on the acoustic reflection signals of sandy sediments with varying grain sizes revealed that the shallow low-level HF-SSBP is capable of precise measurements. In this study, we utilized this equipment to analyze six sandy sediments with different grain sizes in the laboratory. The following conclusions were reached:</p>
<p>(1) We independently developed the HF-SSBP used in the experiments, and this instrument can accurately and precisely measure sandy sediments in a small space. The equipment is capable of testing the accuracy and stability of the acoustic reflection echoes of sandy sediments in the laboratory.</p>
<p>(2) Six types of sediment with uniform grain sizes were obtained from sandy sediments using standard sieves. Their acoustic reflection echoes were then tested, and it was found that there was a positive correlation between the amplitude and grain size. The amplitude of the reflection peaks increased with increasing grain size.</p>
<p>(3) By analyzing the amplitude of the reflection peaks and echo waveforms, sediment grain sizes can be distinguished in a more precise manner.</p>
<p>This study provides a valuable guide for the fine-grained classification of sediment grain size.</p>
</sec>
</body>
<back>
<sec id="s7" sec-type="data-availability">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.</p>
</sec>
<sec id="s8" sec-type="author-contributions">
<title>Author contributions</title>
<p>HZ: Data curation, Investigation, Writing &#x2013; original draft, Writing &#x2013; review &amp; editing. XC: Funding acquisition, Project administration, Supervision, Writing &#x2013; review &amp; editing. ZQ: Data curation, Methodology, Software, Writing &#x2013; review &amp; editing. DZ: Conceptualization, Formal analysis, Supervision, Validation, Writing &#x2013; review &amp; editing. SX: Data curation, Investigation, Methodology, Writing &#x2013; review &amp; editing. JS: Data curation, Investigation, Writing &#x2013; review &amp; editing. HG: Data curation, Formal analysis, Investigation, Visualization, Writing &#x2013; review &amp; editing.</p>
</sec>
<sec id="s9" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Natural Science Foundation of the Xinjiang Uygur Autonomous Region (grant numbers 2022D01C736 and 2022D01C348).</p>
</sec>
<sec id="s10" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="s11" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
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