Physical properties of the seabed inversed based on Chirp data and the Biot-Stoll model in the northern continental slope of the South China Sea
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摘要: 浅地层剖面是基于声学信号(频率在几百至几千赫兹)在沉积物中的传播得到可反映沉积地层结构的数据,海底反射系数与沉积物物理性质密切相关。Biot-Stoll声波传播理论模型可以预测海底沉积物的物理性质,构建反射系数等声学参数与物理参数之间的关系,但在不同的海域采用不同的参数所获得的效果不同。为此,本文基于南海北部陆坡海底表层沉积物的实测物理参数,利用Biot-Stoll模型建立研究区海底反射系数和沉积物物理性质之间的关系,结果表明模型计算值与样品实测值吻合度总体较好,偏差在0.1%~4.9%之间,并建立了频率3.5 kHz时海底反射系数与沉积物孔隙度、密度、平均粒径之间的关系方程,且方程拟合度较高,可决系数R2均大于0.99。在对典型Chirp剖面数据计算其海底反射系数的基础上,反演了海底表层沉积物的孔隙度、密度、颗粒平均粒径等物理性质,其中反演孔隙度、密度、平均粒径与实测孔隙度、密度、平均粒径相对误差均小于5%,结果与实测值基本相符,表明该反演方法在南海北部陆坡区的应用是可行的。
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关键词:
- Chirp浅剖数据 /
- Biot-Stoll模型 /
- 海底反射系数 /
- 沉积物物理性质 /
- 南海北部陆坡
Abstract: Sub-bottom profile is based on the acoustic signal (frequency in hundreds to thousands Hz) in the sediment propagation to reflect the sedimentary formation structure. The seabed reflection coefficient is closely related to the sediments physical properties. The Biot-Stoll theoretical model can predict the physical properties of seabed sediments and establish the relationship between acoustic parameters such as reflection coefficient and physical parameters, but the results obtained by using different parameters in different sea areas are different. For this, this article is based on the measured sediments physical parameters in the northern slope of the South China Sea to establish the relationship between the reflection coefficient and the sediments physical parameters based on Biot-Stoll model. The results show that the calculated value of the model is in good agreement with the measured value of the sample, and the equation for the relationship between the bottom reflection coefficient and the porosity, density, mean grain size of sediments at a frequency of 3.5 kHz is established. The equation has a high fitting degree, and the determination coefficient R2 is all greater than 0.99. On the basis of calculating the seabed reflection coefficient by the typical Chirp profile data, the porosity, density and mean grain size of the sub-bottom sediments are inversed. The relative errors of the inversion porosity, density, mean grain size and the measured porosity, density, mean grain size are all less than 5%, and the results are basically consistent with the measured values, indicating that the inversion method is feasible in the northern continental slope area of the South China Sea. -
图 1 Biot参数计算反射系数的几何示意图(据文献[2])
${D_i}$和${D_r}$分别为入射波和反射波的复位移振幅值;${A_1}$和${A_2}$分别为沉积物骨架在快波和慢波作用下的复位移振幅值;${B_1}$和${B_2}$分别为快波和慢波作用下孔隙流体相对于骨架运动的复相对位移
Fig. 1 Geometric sketch of reflection coefficient calculated by Biot parameter (according to reference [2])
${D_i}$ and ${D_r}$ are the reset amplitude values of incident wave and reflected wave respectively; ${A_1}$ and ${A_2}$are the resetting amplitude of sediment skeleton under the action of fast wave and slow wave respectively; ${B_1}$ and ${B_2}$ are the complex relative displacement of pore fluid relative to skeleton motion under the action of fast wave and slow wave respectively
图 5 反射系数与孔隙度的关系
盒图为海底表层沉积物样品测试数据计算的反射系数;红色实线为Biot-Stoll模型计算的反射系数,样品测试频率与模型计算频率均为25 kHz
Fig. 5 The relationship between reflection coefficient and porosity
The boxplot is the reflection coefficient calculated from the test data of seabed surface sediment samples; the red solid line is the reflection coefficient calculated by the biot-stoll model, and the sample test frequency and model calculation frequency are both 25 kHz
图 6 海底反射系数与沉积物物理性质的相关关系
a. 反射系数随频率的变化;b. 反射系数随孔隙度的变化(f=3.5 kHz);c. 反射系数随密度的变化(f=3.5 kHz);d. 反射系数随平均粒径的变化(f=3.5 kHz)
Fig. 6 Correlation between bottom reflection coefficients and sediment physical properties
a. Variation of reflection coefficient with frequency; b. variation of reflection coefficient with porosity (f = 3.5 kHz); c. variation of reflection coefficient with density (f = 3.5 kHz); d. variation of reflection coefficient with mean grain size (f = 3.5 kHz)
表 1 Biot-Stoll模型输入的沉积物物理参数
Tab. 1 The input sediment physical parameters of the Biot-Stoll model
参数 Biot-Stoll模型取值 颗粒密度${\rho _g}/ {\rm {kg\cdot{m^{-3}}}}$ 实测 孔隙度$n$ 实测 孔隙曲折度$\alpha $ $\alpha = \left\{ {\begin{aligned}& {\begin{array}{*{20}{c}} {1.35}&{\varphi \leqslant 4} \end{array}} \\ & {\begin{array}{*{20}{c}} {{\rm{ - }}0.3 + 0.412\;5\varphi }&{4 < \varphi < 8} \end{array}} \\ & {\begin{array}{*{20}{c}} {3.0}&{\varphi \geqslant 8} \end{array}} \end{aligned}} \right.\;\;\;\;\;\varphi = {\rm{ - lo}}{{\rm{g}}_2}d$,$\varphi $为中值粒径;d为颗粒直径,单位:mm 渗透率$\kappa /{\rm {m}^2}$ $\kappa = \dfrac{{{d^2}{n^3}}}{{180{{(1 - n)}^2}}}\dfrac{1}{{\sqrt {10} }}$ 海水动力黏度$\eta /{\rm {Pa}} \cdot {\rm s}$ 0.001 颗粒体积模量${K_g}/{\rm {Pa}}$ 3.2×1010 海水体积模量${K_w}/{\rm {Pa}}$ 2.395×109 海水密度${\rho _w}/ {\rm {kg\cdot{m^{-3}}}}$ 1 023 框架剪切模量${\mu _0}/{\rm {Pa}}$ ${\mu _0} = 1.835 \times {10^5}{\left(\dfrac{n}{{1 - n}}\right)^{ - 1.12}}\sqrt {{\tau _a}(z)} $${\tau _a}(z) = (1 - n)({\rho _s} - {\rho _f})gz$,${\tau _a}(z)$为沉积物平均有效压力,重力加速度$g = 9.8\;{\rm {m/{s^{ 2} } } }$,z为海底以下沉积物深度,单位:m,ρs为颗粒密度,ρf为孔隙流体密度 框架体积模量${K_0}/{\rm {Pa}}$ ${K_0} = \dfrac{{2{\mu _0}(1 + \sigma )}}{{3(1 - 2\sigma )}}$,$\sigma $为沉积物骨架的泊松比 孔隙大小$a$ $ a = \dfrac{d}{3}\cdot \dfrac{n}{{1 - n}} \cdot \dfrac{1}{{1.8}}$ 体积对数衰减${\delta _f}$ ${\delta _f}({z_s}) = {\delta _f}({z_0})\sqrt {{z_0}/{z_s}} $,z0,zs分别为浅表层和表层沉积物深度 表 2 Chirp子波相关参数
Tab. 2 The relevant parameters of Chirp wavelet
属性 子波特征 频带宽度 1 000~6 000 Hz 脉冲长度 5 ms 脉冲幅度 2.5 ms 采样间隔 13 µs 表 3 站位实测物理性质与反演结果对比
Tab. 3 Comparison between the measured physical properties and the inversion results
取样站位 表层沉积物类型 孔隙度 密度/kg·m−3 平均粒径(Φ) 实测值 反演值 相对误差/% 实测值 反演值 相对误差/% 实测值 反演值 相对误差/% GLW3101 粉砂质黏土 0.651 0.645 −0.92 1 440 1 450 0.69 5.5 5.7 3.63 GLW3102 粉砂质黏土 0.747 0.738 −1.20 1 368 1 374 0.43 6.2 6.5 4.84 GLW3103 粉砂质黏土 0.783 0.758 −3.19 1 350 1 343 −0.52 8.3 8.2 −1.20 GLW3105 粉砂质黏土 0.798 0.792 −0.75 1 310 1 306 −0.31 8.4 8.6 2.38 GLW3108 黏土 0.806 0.812 0.74 1 290 1 303 1.01 8.7 9.1 4.60 注:Φ=−log2d,d为颗粒粒径,单位: mm。 -
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