Characterising sediment thickness beneath a Greenlandic outlet glacier using distributed acoustic sensing: preliminary observations and progress towards an efficient machine learning approach
Characterising sediment thickness beneath a Greenlandic outlet glacier using distributed acoustic sensing: preliminary observations and progress towards an efficient machine learning approach
复制标题
使用分布式声学传感表征格陵兰出口冰川下的沉积物厚度：初步观察和高效机器学习方法的进展
DOI：
10.1017/aog.2023.15
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发表时间：
2023
影响因子：
2.9
通讯作者：
Booth A
中科院分区：
文献类型：
--
作者：
Booth A
分布式声学传感(DAS)越来越被认为是冰川学地震应用的一种有价值的工具，尽管分析采集产生的大量数据会带来计算挑战。我们展示了活动源DAS在快速流动的格陵兰出口冰川之下成像和描述冰下沉积物的潜力，估计沉积层的厚度为20-30米。然而，缺乏冰下速度约束限制了这一估计的准确性。可以通过例如地震层析成像在被动地震活动的对应3天记录中分析低温地震事件来提供约束，但在9TB数据量内定位它们的计算效率很低。我们描述了在训练卷积神经网络之前使用频率-波数(f-k)变换进行数据压缩的实验，其效率提高了约300倍。通过将主动和被动源和我们的机器学习框架相结合，可以为未来的一系列应用释放大型DAS数据集的潜力。
Distributed Acoustic Sensing (DAS) is increasingly recognised as a valuable tool for glaciological seismic applications, although analysing the large data volumes generated in acquisitions poses computational challenges. We show the potential of active-source DAS to image and characterise subglacial sediment beneath a fast-flowing Greenlandic outlet glacier, estimating the thickness of sediment layers to be 20–30 m. However, the lack of subglacial velocity constraint limits the accuracy of this estimate. Constraint could be provided by analysing cryoseismic events in a counterpart 3-day record of passive seismicity through, for example, seismic tomography, but locating them within the 9 TB data volume is computationally inefficient. We describe experiments with data compression using the frequency-wavenumber (f-k) transform ahead of training a convolutional neural network, that provides a ~300-fold improvement in efficiency. In combining active and passive-source and our machine learning framework, the potential of large DAS datasets could be unlocked for a range of future applications.
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影响因子：
8.4
作者：
Hubbard, Bryn;Christoffersen, Poul;Bougamont, Marion
通讯作者：
Bougamont, Marion
影响因子：
2.8
作者：
Xin Zhang;C. Roy;A. Curtis;A. Nowacki;B. Baptie
通讯作者：
Xin Zhang;C. Roy;A. Curtis;A. Nowacki;B. Baptie
影响因子：
13.6
作者：
Law R;Christoffersen P;Hubbard B;Doyle SH;Chudley TR;Schoonman CM;Bougamont M;des Tombe B;Schilperoort B;Kechavarzi C;Booth A;Young TJ
通讯作者：
Young TJ
西南极洲 Skytrain Ice Rise 的井下分布式声学地震剖面
DOI：
10.5194/tc-2021-1
发表时间：
2021
期刊：
--
影响因子：
--
作者：
Brisbourne A
通讯作者：
Brisbourne A