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A technique for the automatic estimation of ice thickness and bedrock properties from radar sounder data acquired at Antarctica

机译:从南极地区获取的雷达探测器数据自动估计冰厚度和基岩特性的技术

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The rise in the temperature of the planet has a very negative impact on the subsurface dynamics of the Earth Polar Regions. This makes it crucial to investigate the features present in the ice subsurface. The analysis of these features is typically performed manually, by examining radargrams acquired by radar sounder (RS) instruments operated at the Polar Caps. However, in order to cope with the very large amount of data that RSs can acquire, it is necessary to develop data analysis techniques that can identify and extract subsurface features automatically. To address this problem, in this paper we propose a novel technique for the automatic estimation of the ice thickness and bedrock properties from RS data acquired in Antarctica. The proposed technique generates a statistical map of the subsurface by exploiting the statistical properties of the radar signal. Then, it applies a segmentation algorithm properly tuned to the characteristics of the investigated areas. In order to assess the effectiveness of the proposed technique, we analyzed its performance when applied to Multichannel Coherent Radar Depth Sounder (MCoRDS) data acquired in Antarctica.
机译:地球的温度的升高对地球区域的地下动态产生了非常负面影响。这使得研究冰地下表面存在的特征至关重要。这些特征的分析通常通过检查由在极性盖上操作的雷达探测器(RS)仪器获取的雷达格来手动执行。但是,为了应对RSS可以获取的大量数据,有必要开发数据分析技术,可以自动识别和提取地下功能。为了解决这个问题,本文提出了一种新的技术,用于自动估计南极洲RS数据的冰厚度和基岩特性。所提出的技术通过利用雷达信号的统计特性来产生地下的统计地图。然后,它适用于调查区域的特征的分段算法。为了评估所提出的技术的有效性,我们在应用于在南极洲获取的多通道相干雷达深度声音(MCORDS)数据时分析了其性能。

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