Tomographic Detection of Low-Velocity Anomalies with Limited Data Sets (Velocity and Attenuation)
Tomographic Detection of Low-Velocity Anomalies with Limited Data Sets (Velocity and Attenuation)
复制标题
使用有限数据集（速度和衰减）对低速异常进行层析成像检测
DOI：
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发表时间：
2000
期刊：
影响因子：
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通讯作者：
J. Santamarina
中科院分区：
文献类型：
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作者：
J. Prada;D. Fratta;J. Santamarina
低速异常，如裂缝，空腔，蜂窝，颈缩，局部退化可能是非常有害的桩，沉箱和泥浆墙的工程性能。与低速异常对波传播的影响、有限的数据集和受限的照明角度相关的固有物理困难影响桩、沉箱、泥浆墙和其他类似岩土系统的层析成像评估。这些异常也影响了诸如柱和梁等土木系统的层析成像评估。本研究评估了各种反演方法的层析检测低速异常。在实验室中，通过模拟真实的现场条件收集旅行时间和振幅数据。反演方法包括数据预处理，模糊逻辑约束，以及基于介质的像素或参数表示的各种形式的层析反演。它示出的方差和分辨率之间的权衡，在基于像素的反演可以克服通过添加信息，如正则化的解决方案，或通过捕获的问题，在参数化的形式为一个假定的简单的几何形状。结果表明，基于振幅的反演可能比基于时间的反演更有利于检测低速异常，但是，一致的耦合换能器是必需的。在本研究中测试的用于在标准现场情况下检测低速异常的最稳健的反演方法（即，有限的数据和受限的照明角度）涉及模糊逻辑约束和随后的基于参数的反演的组合。
Low-velocity anomalies such as cracks, cavities, honeycombs, necking, and localized degradation can be very detrimental to the engineering performance of piles, caissons, and slurry walls. Inherent physical difficulties associated with the effect of low-velocity anomalies on wave propagation, limited data sets, and restricted illumination angles affect the tomographic assessment of piles, caissons, slurry walls, and other similar geotechnical systems. The anomalies also affect the tomographic assessment of civil systems such as columns and beams. This study evaluates various inversion methodologies for the tomographic detection of low-velocity anomalies. Travel time and amplitude data are gathered in the laboratory by simulating realistic field conditions. The inversion methodology involves data preprocessing, fuzzy logic constraining, and various forms of tomographic inversion based on either pixel or parametric representations of the medium. It is shown that the tradeoff between variance and resolution in pixel-based inversions can be overcome by adding information, such as regularized solutions, or by capturing the problem in parametric form for a presumed simple geometry. Results show that amplitude-based inversion may be more advantageous than time-based inversion in the detection of low-velocity anomalies; however, consistent coupling of transducers is required. The most robust inversion method tested in this study for the detection of low-velocity anomalies under standard field situations (i.e., limited data and restricted illumination angles) involves a combination of fuzzy logic constraining followed by parametric-based inversion.