Seabed classification based on sub-bottom profile data in modified geo-acoustic model
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摘要:
浅地层剖面仪发射的声脉冲能够穿透海底面进入沉积层内部，其回波中携带了丰富的底质信息。地声模型是底质声学与物理性质关系的数学描述，广泛用于海底声学与地声反演研究。本文通过对浅地层剖面数据的处理、解译得到海底反射系数，与考虑底质松密影响的改进Biot-Stoll模型相结合，提出底质反演新方法并开展实例验证。研究结果表明：通过对浅地层剖面原始记录的读取、解译，提取反射波振幅，并结合设备声源级，可有效求取海底反射系数。通过引入相对密度改进孔隙度计算公式，进而在基于Biot-Stoll模型构建海底反射系数和底质平均粒径关系过程中进一步考虑了底质松密的影响。基于山东威海某海域及文献的算例均显示，本文提出的改进地声模型可缩小底质反演与实测结果之间的相对误差、提升基于浅地层剖面数据的海底底质地声反演精度。
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关键词:
- 浅地层剖面 /
- 底质反演 /
- Biot-Stoll模型 /
- 海底反射系数 /
- 平均粒径
Abstract:The echoes signal of sub-bottom profilers (SBP) carry abundant information of sediment because the acoustic pulses emitted by SBP can penetrate the seafloor surface into the interior of sediment layers and get reflected from different impedance interface. A geoacoustic model describing the relationship between the acoustics and physical properties of sediments mathematically, is widely used in sediment classification and acoustics inversion. We applied the method to obtain the bottom reflection coefficients by decoding the SBP data, and then combined it with a modified Biot-Stoll model considering the influence of sediment’s degree of compaction, based on which a new method of sediment inversion was proposed to evaluate its capacity by examples. Results show that the bottom reflection coefficient of seafloor can be effectively obtained by decoding the original records of SBP, extracting the amplitude of the reflected waves, and combining with the sound source level of the equipment. To build the relationship between bottom reflective coefficient and mean grain size, the degree of sediment compaction was considered based on Biot-Stoll model and the parameter of relative density was introduced into the porosity calculation formula. Examples of both measured data of Weihai sea area and those obtained from available literatures indicate that the presented method could reduce the relative error between the inversion and the measured mean grain sizes, contributing to improve the accuracy of submarine sediment geoacoustic inversion based on SBP data.
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图 2 基于浅地层剖面数据计算得到的某测线海底反射系数
Figure 2. Sea bottom reflection coefficients of a survey line calculated from SBP data
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图 3 海底反射系数与平均粒径的关系
a: Biot-Stoll模型；b: 改进Biot-Stoll模型。
Figure 3. The relationship between reflection coefficient and mean grain size
a: Biot-Stoll model; b: Modified Biot-Stoll model.
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图 4 研究区位置及浅地层剖面航迹线和底质取样站位
Figure 4. The study area and the deployment of the sub-bottom profiler track lines, and sediment sampling stations (red dots)
表 1 不同平均粒径条件下最大和最小孔隙度取值[34-37]
Table 1 Maximum and minimum porosity values of different mean grain-sizes
平均粒径/Ф 最大孔隙度nmax/% 最小孔隙度nmin/% 0～2 41 29 2～3 53 37 3～4 60 40 4～5 69 40 5～6 75 42 6～7 83 56 7～8 86 57 8～9 90 59 9～10 91 66 
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表 2 状态可控试验实测数据[33]
Table 2 Measured physical parameters of state-controlled experiments
底质类别 密度ρ/(g·cm−3) 相对密度 孔隙度n /% 饱和度Sr 粉土 2.00 0.67 40.79 0.994 1.99 0.60 41.81 0.995 2.01 0.69 40.45 0.994 2.01 0.70 40.41 0.997 2.03 0.75 39.66 0.998 2.05 0.86 37.97 0.998 2.07 0.92 37.10 0.999 2.04 0.82 38.57 0.999 砂土 2.03 0.39 38.33 0.992 2.16 0.70 30.72 0.994 1.93 0.44 44.18 0.982 2.05 0.67 37.55 0.994 2.00 0.52 40.45 0.992 2.13 0.77 32.64 0.989 1.94 0.33 43.37 0.981 2.00 0.63 39.9 0.978 2.08 0.44 35.55 0.989 2.17 0.74 30.23 0.993 1.92 0.32 44.99 0.986 2.02 0.58 38.8 0.971
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表 3 Biot-Stoll模型参数取值
Table 3 The input physical parameters of the Biot-Stoll model
物理参数 参数取值 颗粒密度ρs /kg·m−3 2690 颗粒体积模量Ks /Pa $ 3.2 \times {10^{10}} $ 流体密度ρf /kg·m−3 1023 流体体积模量Kf /Pa $ 2.395 \times {10^9} $ 黏滞系数η/kg·m−1·s−1 0.001 渗透率κ/m2 $ \kappa = \dfrac{{{d^2}{n^3}}}{{180{{\left( {1 - n} \right)}^2}}}\dfrac{1}{{\sqrt {10} }},\;d{\text{为粒径}}({\rm{mm}}) $ 弯曲度Γ $ \Gamma = \left\{ \begin{array}{*{20}{l}} {\text{1}}{\text{.35 }}&\varphi {\text{≤}} {\text{4}} \\ {{ - 0}}{\text{.3}} + {\text{0}}{\text{.4125}}\varphi &{\text{ 4}} {\text{＜}} \varphi {\text{＜}} 8 \\ {\text{3}}{\text{.0 }}&\varphi {\text{≥}}{\text{8}} \\ \end{array} \right. $ 孔隙半径r/m $ r = \dfrac{d}{3}\dfrac{n}{{\left( {1 - n} \right)}}\dfrac{1}{{1.8}} $ 骨架体积模量Kb /Pa $ {K_{\text{b}}} = \dfrac{{2\mu (1 + \sigma )}}{{3(1 - 2\sigma )}} $，σ为骨架泊松比，
$ \sigma = \left\{ \begin{array}{*{20}{l}} {\text{0}}{\text{.15 }}&\varphi {\text{≤}} {\text{4}} \\ {{ - 0}}{\text{.05}} + {\text{0}}{\text{.05}}\varphi &{\text{ 4}} {\text{＜}} \varphi {\text{＜}} 8 \\ {\text{0}}{\text{.35 }}&\varphi {\text{≥}} {\text{8}} \\ \end{array} \right. $骨架剪切模量μ/Pa $ 1.3 \times {10^7} $
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表 4 地声反演与实测结果比较
Table 4 Comparison between inversion and measured mean grain-size
实测平均
粒径/ФBiot-Stoll模型反演结果 改进Biot-Stoll模型反演结果 反演值 相对误
差/%平均
值/%反演值 相对误
差/%平均
值/%5.28 5.89 11.70 6.51 5.79 8.87 5.06 5.04 5.50 9.11 5.41 6.34 5.08 5.23 3.02 5.14 0.41 5.12 5.54 8.20 5.44 5.46 5.24 5.29 0.95 5.19 1.61 5.24 5.56 6.06 5.46 3.37 5.20 5.60 7.67 5.50 4.94 5.17 5.51 6.48 5.41 3.79 5.50 5.59 1.63 5.49 0.94 5.56 5.26 5.31 5.17 7.71 1.96 2.06 5.01 2.02 2.20 5.11 4.45 12.93 4.37 15.13
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表 5 地声反演与文献结果比较
Table 5 Comparison between inversion results and those from published sources
数据
来源实测平均
粒径/ФBiot-Stoll模型反演结果 改进Biot-Stoll模型反演结果 反演值 相对误
差/%平均
值/%反演值 相对误
差/%平均
值/%周庆杰[20] 5.50 7.31 32.85 29.69 7.12 29.46 27.22 6.20 8.14 31.24 7.93 27.89 8.30 9.87 18.86 9.61 15.81 8.40 10.27 22.22 10.00 19.08 8.70 10.77 23.74 10.49 20.55 黄必桂[40] 6.56 7.38 12.55 7.20 9.68 6.94 7.54 8.65 7.35 5.88 7.49 9.10 21.45 8.86 18.33 7.25 9.40 29.66 9.16 26.33 7.06 10.55 49.48 10.28 45.62 7.48 10.92 45.99 10.64 42.22 6.78 10.46 54.33 10.19 50.35 7.08 10.57 49.33 10.30 45.49 6.81 10.05 47.64 9.80 43.84 6.64 9.95 49.91 9.70 46.05 6.39 9.64 50.90 9.39 47.02 6.08 9.81 61.41 9.56 57.26 6.27 9.81 56.52 9.56 52.49 6.23 9.32 49.59 9.08 45.75 Zheng[19] 6.64 4.45 33.03 4.34 34.73 6.64 7.28 9.63 7.10 6.84
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