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Targeted Full-Waveform Inversion for Recovering Thin- and Ultra-Thin-Layer Properties Using Radar and Seismic Reflection Methods

机译:目标全波形反演,利用雷达和地震反射方法恢复薄层和超薄层特性

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摘要

Ground penetrating radar (GPR) and seismic reflection methods are useful geophysical tools for near-surface characterization. Analysis of radar or seismic reflection data can combine velocity analysis with common physical transformations to provide subsurface physical properties such as subsurface porosity, density, and contaminant locations. However, reliable quantitative characterization of thin subsurface layers may be impossible using standard reflection data processing techniques, e.g. velocity analysis, if the layer thickness is below the conventional resolution limits of the data. The limiting layer thickness for layer resolution may be up to ½ or even ¾ of the dominant wavelength (λ) of the signal in the medium of interest. This limitation often depends on data noise levels and source characteristics. In many environmental problems, target layers may be below this layer thickness and accurate determination of layer properties becomes problematic. In order to reliably quantify thin-layer parameters in these cases, geophysical practitioners require additional tools such as attribute analyses and inversion methodologies. Full-waveform inversions may be able to quantify layer parameters even in the case of thin (u3c ½λ) and ultra-thin (u3c ⅛λ) layers by inverting directly for thin-layer properties. Therefore, I provide a targeted full-waveform inversion algorithm to quantify thin- and ultra-thin layer parameters for multiple relevant environmental problems including oil in and under sea ice and basal conditions of glaciers. I demonstrate the efficacy of this approach on model and field data collected using radar and seismic reflection methods. These methods depend on surface records of reflection information from subsurface interfaces and may fail if reflections are obscured or attenuated in the subsurface. Therefore, I demonstrate that a new dual-polarization system can mitigate the effects of the overburden anisotropy and conductivity attenuation on radar data collected in Arctic conditions. Combining my full-waveform inversion algorithm with improved sea ice radar data collection may enhance reliable quantification of spilled oil in the event of an accidental release in Arctic environments.
机译:探地雷达(GPR)和地震反射方法是用于近地表表征的有用地球物理工具。雷达或地震反射数据的分析可以将速度分析与常见的物理转换相结合,以提供地下物理属性,例如地下孔隙度,密度和污染物位置。但是,使用标准的反射数据处理技术,例如薄表面层,不可能可靠地进行定量表征。速度分析(如果层厚度低于数据的常规分辨率极限)。用于层分辨率的限制层厚度可以高达感兴趣介质中信号的主波长(λ)的½甚至3/4。此限制通常取决于数据噪声水平和源特性。在许多环境问题中,目标层可能会低于此层厚度,因此准确确定层的性能会成为问题。为了在这些情况下可靠地量化薄层参数,地球物理专业人员需要其他工具,例如属性分析和反演方法。通过在薄层特性上直接进行反演,即使在薄( u3c½λ)和超薄( u3c⅛λ)层的情况下,全波形反演也能够量化层参数。因此,我提供了一种有针对性的全波形反演算法,可以对涉及多个相关环境问题(包括海冰中和海面下的石油以及冰川的基础条件)的薄层和超薄层参数进行量化。我演示了这种方法对使用雷达和地震反射方法收集的模型和现场数据的有效性。这些方法取决于来自地下界面的反射信息的表面记录,并且如果在地下掩盖或衰减反射,则可能会失败。因此,我证明了一种新的双极化系统可以减轻覆盖各向异性和电导率衰减对北极条件下收集的雷达数据的影响。将我的全波形反演算法与改进的海冰雷达数据收集相结合,可以在北极环境中意外泄漏的情况下增强对溢油的可靠量化。

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    Babcock Esther;

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