Attribute-based analysis of time-lapse ground-penetrating radar data
Attribute-based analysis of time-lapse ground-penetrating radar data
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DOI：
10.1190/geo2015-0171.1
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发表时间：
2016
期刊：
影响因子：
3.3
通讯作者：
N. Allroggen;J. Tronicke
中科院分区：
文献类型：
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作者：
N. Allroggen;J. Tronicke
对时延探地雷达(GPR)数据的分析可以提供有关地下水文过程的信息，如优先流。然而，时延数据的分析往往受到数据质量的限制；例如，对于有噪声的输入数据，差异图像的解释往往是困难的。在现代图像处理工具的激励下，我们开发了两个稳健的探地雷达属性，使我们能够区分与单个延时探地雷达数据集之间的差异有关的幅度(对比度相似性)和时移(结构相似性)变化。我们使用不同复杂性的合成数据来测试和评估我们的属性。然后，我们将它们应用到一个野外数据实例中，在该实例中，地下水流是由人工降雨事件引起的。对于所有的例子，我们确定我们的结构相似性属性是一种稳健的度量，用于突出低信噪比数据中的时间推移变化。我们确定我们的新属性-b..。
ABSTRACTAnalysis of time-lapse ground-penetrating radar (GPR) data can provide information regarding subsurface hydrological processes, such as preferential flow. However, the analysis of time-lapse data is often limited by data quality; for example, for noisy input data, the interpretation of difference images is often difficult. Motivated by modern image-processing tools, we have developed two robust GPR attributes, which allow us to distinguish amplitude (contrast similarity) and time-shift (structural similarity) variations related to differences between individual time-lapse GPR data sets. We tested and evaluated our attributes using synthetic data of different complexity. Afterward, we applied them to a field data example, in which subsurface flow was induced by an artificial rainfall event. For all examples, we identified our structural similarity attribute to be a robust measure for highlighting time-lapse changes also in data with low signal-to-noise ratios. We determined that our new attribute-b...