A synthetic study to assess the applicability of full-waveform inversion to infer snow stratigraphy from upward-looking ground-penetrating radar data
A synthetic study to assess the applicability of full-waveform inversion to infer snow stratigraphy from upward-looking ground-penetrating radar data
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DOI：
10.1190/geo2015-0152.1
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发表时间：
2016-01-01
期刊：
影响因子：
3.3
通讯作者：
Maurer, Hansruedi
中科院分区：
文献类型：
--
作者：
Schmid, Lino;Schweizer, Jurg;Maurer, Hansruedi
雪层和液态水含量是雪崩形成的关键因素。上视探地雷达（upGPR）系统允许无损监测积雪，但推导密度和液态水含量的配置文件是不可能的基础上的反射响应的直接分析。我们已经研究了推导这些数量使用全波形反演（FWI）技术应用于upGPR数据的可行性。为此，我们开发了一种频域FWI算法，其中我们还利用了时域特征，如反射波的到达时间。我们的结果表明，FWI应用于upGPR数据总体上是可行的。更具体地说，我们可以证明，在干积雪的情况下，如果有足够的先验信息，就有可能推导出雪密度和层厚度。在湿积雪的情况下，也需要对液态水含量进行反演，即使有足够的先验信息，算法也可能失败，特别是在存在现实噪声的情况下。最后，我们研究了FWI解决薄层雪稳定性评价中发挥关键作用的能力。我们的模拟表明，可以识别出厚度远低于GPR波长的层，但在存在大量液态水的情况下，薄层特性可能容易出现不准确。这些结果是令人鼓舞的，激励应用程序的现场数据，但仍有待解决的重大问题，如确定一般未知的upGPR源函数和确定反演模型中的最佳层数。此外，需要相对高水平的先验知识来使算法收敛。然而，我们认为这些问题并非不可克服，新技术在改善现场数据分析方面具有巨大潜力。
Snow stratigraphy and liquid water content are key contributing factors to avalanche formation. Upward-looking ground-penetrating radar (upGPR) systems allow nondestructive monitoring of the snowpack, but deriving density and liquid water content profiles is not yet possible based on the direct analysis of the reflection response. We have investigated the feasibility of deducing these quantities using full-waveform inversion (FWI) techniques applied to upGPR data. For that purpose, we have developed a frequency-domain FWI algorithm in which we additionally took advantage of time-domain features such as the arrival times of reflected waves. Our results indicated that FWI applied to upGPR data is generally feasible. More specifically, we could show that in the case of a dry snowpack, it is possible to derive snow densities and layer thicknesses if sufficient a priori information is available. In case of a wet snowpack, in which it also needs to be inverted for the liquid water content, the algorithm might fail, even if sufficient a priori information is available, particularly in the presence of realistic noise. Finally, we have investigated the capability of FWI to resolve thin layers that play a key role in snow stability evaluation. Our simulations indicate that layers with thicknesses well below the GPR wavelengths can be identified, but in the presence of significant liquid water, the thin-layer properties may be prone to inaccuracies. These results are encouraging and motivate applications to field data, but significant issues remain to be resolved, such as the determination of the generally unknown upGPR source function and identifying the optimal number of layers in the inversion models. Furthermore, a relatively high level of prior knowledge is required to let the algorithm converge. However, we feel these are not insurmountable and the new technology has significant potential to improve field data analysis.