Enhancing seismic calving event identification in Svalbard through empirical matched field processing and machine learning
Enhancing seismic calving event identification in Svalbard through empirical matched field processing and machine learning
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通过经验匹配场处理和机器学习增强斯瓦尔巴群岛地震崩解事件识别
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通讯作者：
S. Mæland
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作者：
A. K¨ohler;E. B. Myklebust;S. Mæland
摘要冰山崩解产生的地震信号可用于监测潮水冰川的冰流失，具有高时间分辨率，不受能见度的影响。我们将经验匹配fi场(EMF)方法和卷积神经网络(CNN)机器学习相结合，用于斯皮茨卑尔根(SPITS)地震台阵和北极斯瓦尔巴特群岛KBS单一宽带台站的崩解事件检测。地震台阵的电磁场检测试图通过评估使用阵列站之间的经验相位延迟获得的波束功率来识别由类似于单P和/或S相位模板的confiNed目标区中的事件产生的所有信号。误检率取决于阈值设置，因此需要适当的调整或后处理。我们将SPITS阵列上的电动势检测器以及KBS站的STALTA(短期平均/长期平均)检测器与使用CNN的检测后Classifi阳离子步骤相结合。美国有线电视新闻网Classifier使用KBS的三分量记录的波形作为输入。我们应用这种方法对斯瓦尔巴群岛西北部Kongsfjord地区KBS站附近潮水冰川的冰解事件进行了检测和分类。在以前的研究中，我们实现了一种更简单的方法来fi和这些产犊事件在KBS数据中，并且我们使用它作为基线，我们试图改进检测和分类fi阳离子的性能。Cnn Classifier使用来自Kongsfjord地区四个不同冰川的fiRed崩解信号、地震噪声实例和区域构造地震事件进行培训。随后，我们对2016年6个月的连续数据进行了处理。我们测试了不同的CNN架构和数据扩充，以处理可用的有限训练数据集。以Kronebreen冰川为目标，我们发现，表现最好的模型可以显著改善基线fifier。这一结果既适用于在KBS进行的STA/LTA检测，随后是CNN Classifi阳离子，也适用于在KBS结合CNN Classifier的SPITS电动势检测，尽管SPITs距离目标冰川100公里，而KBS距离15公里。我们的结果将进一步增加从地震观测得出的克隆布赖恩冰川损失估计的可信度，这反过来又有助于更好地了解斯瓦尔巴特群岛气候变化的影响。
SUMMARY Seismic signals generated by iceberg calving can be used to monitor ice loss at tidewater glaciers with high temporal resolution and independent of visibility. We combine the empirical matched field (EMF) method and machine learning using convolutional neural networks (CNNs) for calving event detection at the Spitsbergen (SPITS) seismic array and the single broad-band station KBS on the Arctic Archipelago of Svalbard. EMF detection with seismic arrays seeks to identify all signals generated by events in a confined target region similar to single P and/or S phase templates by assessing the beam power obtained using empirical phase delays between the array stations. The false detection rate depends on threshold settings and therefore needs appropriate tuning or, alternatively, post-processing. We combine the EMF detector at the SPITS array, as well as an STA/LTA (short term average/long term average) detector at the KBS station, with a post-detection classification step using CNNs. The CNN classifier uses waveforms of the three-component record at KBS as input. We apply the methodology to detect and classify calving events at tidewater glaciers close to the KBS station in the Kongsfjord region in Northwestern Svalbard. In a previous study, a simpler method was implemented to find these calving events in KBS data, and we use it as the baseline in our attempt to improve the detection and classification performance. The CNN classifier is trained using classes of confirmed calving signals from four different glaciers in the Kongsfjord region, seismic noise examples and regional tectonic seismic events. Subsequently, we process continuous data of six months in 2016. We test different CNN architectures and data augmentations to deal with the limited training data set available. Targeting Kronebreen, one of the most active glaciers in the Kongsfjord region, we show that the best performing models significantly improve the baseline classifier. This result is achieved for both the STA/LTA detection at KBS followed by CNN classification, as well as EMF detection at SPITS combined with a CNN classifier at KBS, despite of SPITS being located at 100 km distance from the target glacier in contrast to KBS at 15 km distance. Our results will further increase confidence in estimates of ice loss at Kronebreen derived from seismic observations which in turn can help to better understand the impact of climate change in Svalbard.
影响因子：
2.8
作者：
Zhu, Weiqiang;Beroza, Gregory C.
通讯作者：
Beroza, Gregory C.
影响因子：
3
作者：
Ross, Zachary E.;Meier, Men-Andrin;Heaton, Thomas H.
通讯作者：
Heaton, Thomas H.