Study on multiphase discrete random medium model and its GPR wave field characteristics
-
摘要: 沥青混凝土是由骨料、沥青胶浆、空气按照一定的体积百分比混合而成的多相非匀质混合物, 其骨料、沥青胶浆和空气的体积不等、形状各异、介电特性不同、空间位置随机分布, 具有明显的多相、离散、随机介质特征.本文基于随机介质模型理论, (1)测量与统计了介电常数在典型沥青混凝土芯样空间上的随机分布统计特征; (2)估算了沥青混凝土介质的自相关函数及其特征参数(自相关长度、自相关角度等), 确定其随机介质类型; (3)提出了量化约束下的多相离散随机介质建模算法, 以混合型椭圆自相关函数为基础, 构建了不同粗糙度因子的多相离散随机介质模型; (4)构建了不同空隙率的多相离散随机介质模型, 正演模拟与对比分析了探地雷达波在均匀介质、连续型随机介质和多相离散随机介质中的传播特征.结果表明:多相离散随机介质模型不仅描述了沥青混凝土的多相、离散与空间随机分布统计特征, 而且进一步描述了其各组成物质体积百分比, 能更全面、准确地描述沥青混凝土的介质特征, 同时也为描述其他类似材料或介质提供了新的方法和途径; 在多相离散随机介质模型中, 探地雷达波散射强烈, 随机、无序传播的散射波相互叠加干涉, 形成了明显的随机扰动和"噪声", 致使异常体反射波扭曲变形、不连续, 降低了探地雷达回波的信噪比和分辨率.研究探地雷达波的随机扰动特征与多相离散随机介质模型参数之间的关系, 将为定量评价多相离散随机介质的属性参数提供参考和帮助.Abstract: Asphalt concrete is a type of multiphase, heterogeneous mixture. It is composed of aggregate, asphalt mortar and air with different volume fraction. Aggregate, asphalt mortar and air usually have different sizes, shapes, dielectric properties, and distribute randomly in space. Asphalt concrete has typical features of multiphase, discreteness and randomness. Scattering waves appear when high-frequency electromagnetic waves propagate in such medium. Thus, asphalt concrete cannot be simplified as homogeneous medium, but as multiphase discrete random medium and construct models based on the statistical characteristics of spatial random distribution of dielectric constant and volume fraction of each component of asphalt concrete, and study the propagation characteristics of high-frequency ground penetrating radar wave in such model by numerical simulation.This paper is based on random medium theory,(1)we measured the dielectric constants of the asphalt concrete samples, and computed the statistical characteristics(such as mean values, standard deviations)of spatial random distribution of dielectric constants and the volume fraction of each component;(2)we calculated the autocorrelation function of asphalt concrete based on Wiener-Khintchine theorem, and extracted its characteristic parameters(such as autocorrelation length, autocorrelation angle), and then classified the type of random media;(3)we developed the modeling algorithm of multiphase discrete random medium under quantization constrain, and constructed multiphase discrete random medium model based on intermixed elliptic autocorrelation function. Additionally, we studied the propagation characteristics of ground penetrating radar wave in multiphase discrete random medium model and compared the model with homogeneous medium model and continuous random medium model using numerical simulation.The calculated results show that the autocorrelation functions of a large number of asphalt concrete sections are approximate to ellipsoidal autocorrelation functions, which provide the foundation for using random medium theory to describe asphalt concrete. The multiphase discrete random medium model that is built by the modeling algorithm presented in this paper not only describes the statistical characteristics of spatial random distribution of asphalt concrete, but also describe the volume fractions of its composition when compare with homogeneous model and continuous random medium model. For multiphase discrete random medium model and continuous random medium model, ground penetrating radar wave has strong scattering phenomenon. Random and disorderly scattering waves overlie and interfere with each other, which resulting random perturbations and noise in received waves. The reflected waves from anomalous body are with distortion and discontinuity and reduce the signal to noise ratio and resolution of ground penetrating radar data. When multiphase discrete random medium model and continuous random medium model have the same given model parameters, ground penetrating radar waves have stronger scattering in continuous random medium model. The study reveals that the multiphase discrete random medium model can describe asphalt concrete more comprehensive and precise than homogeneous medium model and continuous random medium model. The multiphase discrete random medium model also provides a new way for studying similar media or materials. The radar profile is more consistent with the field measured data, and more conducive to guide the interpretation of the ground penetrating radar profile data. In the future, we will construct some multiphase discrete random medium models with different porosity parameters to study its effective permittivity and wave field features through numerical simulations, and explore the relations between the permittivity and wave field for quantitative interpretation of porosities of asphalt concrete for ground penetrating radar.
-
Key words:
- Random medium /
- Multiphase discrete /
- Model parameters /
- Reconstruction /
- Wave field characteristics
-
-
[1] Amin P, Subbalakshmi K P. 2007. Detecting hidden messages using image power spectrum.//ICIP 2007 Conference and Exhibition Extended Abstracts, 1:I-421-I-424.
[2] Alani A M, Aboutalebi M, Kilic G. 2013. Applications of ground penetrating radar(GPR)in bridge deck monitoring and assessment. Journal of Applied Geophysics, 97:45-54, doi:10.1016/j.jappgeo.2013.04.009.
[3] Balboa R M, Grzywacz N M. 2003. Power spectra and distribution of contrasts of natural images from different habitats. Vision Research, 43(24):2527-2537, doi:10.1016/S0042-6989(03)00471-1.
[4] Birchak J R, Gardner C G, Hipp J E, et al. 1974. High dielectric constant microwave probes for sensing soil moisture. Proceedings of the IEEE, 62(1):93-98, doi:10.1109/PROC.1974.9388.
[5] Cai J C, Yu B M. 2011. A discussion of the effect of tortuosity on the capillary imbibition in porous media. Transport in Porous Media, 89(2):251-263.
[6] Cai Y C. 2008. Inverse analysis of dielectric properties for layered inhomogeneous media(in Chinese). Dalian:Dalian University of Technology.
[7] Dai Q W, Wang H H. 2013. Element free method forward modeling of GPR based on random medium model. The Chinese Journal of Nonferrous Metals(in Chinese), 23(9):2436-2443.
[8] Ding L, Han B, Liu R Z, et al. 2012. Inversion imaging method for concrete non-destructive testing based on GPR. Chinese J. Geophys.(in Chinese), 55(1):317-326, doi:10.6038/j.issn.0001-5733.2012.01.032.
[9] Ergintav S, Canitez N. 1997. Modeling of multi-scale media in discrete form. Journal of Seismic Exploration, 6(1):77-96.
[10] Guo N C, Wang S X, Dong C H, et al. 2012. Description of small scale inhomogeneities in seismic prospecting and long-wavelength theory. Chinese J. Geophys.(in Chinese), 55(7):2385-2401, doi:10.6038/j.issn.0001-5733.2012.07.023.
[11] Huang Z L, Zhang J Z. 2013. An inversion method for geometric and electric parameters of layered media using spectrum of GPR signal. Chinese J. Geophys.(in Chinese), 56(4):1381-1391, doi:10.6038/cjg20130432.
[12] Ikelle L T, Yung S K, Daube F. 1993. 2-D random media with ellipsoidal autocorrelation functions. Geophysics, 58(9):1359-1372, doi:10.1190/1.1443518.
[13] Jiang Z M, Zeng Z F, Li J, et al. 2013. Simulation and analysis of GPR signal based on stochastic media model with an ellipsoidal autocorrelation function. Journal of Applied Geophysics, 99:91-97, doi:10.1016/j.jappgeo.2013.08.005.
[14] Klime L. 2002. Correlation functions of random media. Pure and Applied Geophysics, 159(7-8):1811-1831, doi:10.1007/s00024-002-8710-2.
[15] Liu Y X, Xu T, Zhao B, et al. 2007. Seismic sounding of anisotropic self-similar self-organized medium. Chinese J. Geophys.(in Chinese), 50(1):221-232.
[16] Lu C M, Qin Z, Zhu H L, et al. 2007. Practical methods for detection of concealed cracks in highway pavement using ground penetrating radar data. Chinese J. Geophys.(in Chinese), 50(5):1558-1568.
[17] Maierhofer C. 2003. Nondestructive evaluation of concrete infrastructure with ground penetrating radar. Journal of Materials in Civil Engineering, 15(3):287-297, doi:10.1061/(ASCE)0899-1561(2003)15:3(287).
[18] Saarenketo T, Scullion T. 2000. Road evaluation with ground penetrating radar. Journal of Applied Geophysics, 43(2-4):119-138, doi:10.1016/S0926-9851(99)00052-X.
[19] Wu F S. 2009. A study of ground penetrating radar methods and techniques in concrete detection(in Chinese). Changchun:Jilin University.
[20] Wu J J, Liu S X, Dong H, et al. 2011. Permittivity measurement for rock and ore samples by open-ended coaxial method. Chinese J. Geophys.(in Chinese), 54(2):457-465, doi:10.3969/j.issn.0001-5733.2011.02.024.
[21] Xi X, Yao Y. 2002. Simulations of random medium model and intermixed random medium. Earth Science-Journal of China University of Geosciences(in Chinese), 27(1):67-71.
[22] Xi X, Yao Y. 2005. Non-stationary random medium model. Oil Geophysical Prospecting(in Chinese), 40(1):71-75.
[23] Xu T, Ning J R, Liu C C, et al. 2007. Influence of the self-organization of the earth interior upon the traveltime and amplitude of seismic wave. Chinese J. Geophys.(in Chinese), 50(4):1174-1181, doi:10.3321/j.issn:0001-5733.2007.04.026.
[24] Zhang Y L, Sundararajan S. 2006. Generating random surfaces with desired autocorrelation length. Applied Physics Letters, 88(14):141903, doi:10.1063/1.2191882.
[25] Zhao Y Y, Gu H M, Wang Y, et al. 2013. Research on constructing clastic reservoir seismic geologic model under no well condition. Chinese J. Geophys.(in Chinese), 56(6):2055-2064, doi:10.6038/cjg20130626.
[26] Zhong Y H, Li Q, Chen Z P, et al. 2007. A study on application of GPR segregation detection of asphalt concrete pavement. Highway(in Chinese),(4):117-123.
-
计量
- 文章访问数:
- PDF下载数:
- 施引文献: 0
下载: