Sentinel - 1 卫星图像中用于检测火山变形的机器学习的大规模演示。Large-scale demonstration of machine learning for the detection of volcanic deformation in Sentinel-1 satellite imagery.-医云文献数字医云科研云海量医学决策数据服务

Abstract：

Radar (SAR) satellites systematically acquire imagery that can be used for volcano monitoring, characterising magmatic systems and potentially forecasting eruptions on a global scale. However, exploiting the large dataset is limited by the need for manual inspection, meaning timely dissemination of information is challenging. Here we automatically process ~ 600,000 images of > 1000 volcanoes acquired by the Sentinel-1 satellite in a 5-year period (2015-2020) and use the dataset to demonstrate the applicability and limitations of machine learning for detecting deformation signals. Of the 16 volcanoes flagged most often, 5 experienced eruptions, 6 showed slow deformation, 2 had non-volcanic deformation and 3 had atmospheric artefacts. The detection threshold for the whole dataset is 5.9 cm, equivalent to a rate of 1.2 cm/year over the 5-year study period. We then use the large testing dataset to explore the effects of atmospheric conditions, land cover and signal characteristics on detectability and find that the performance of the machine learning algorithm is primarily limited by the quality of the available data, with poor coherence and slow signals being particularly challenging. The expanding dataset of systematically acquired, processed and flagged images will enable the quantitative analysis of volcanic monitoring signals on an unprecedented scale, but tailored processing will be needed for routine monitoring applications.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s00445-022-01608-x.

摘要：

雷达（SAR）卫星系统地获取可用于火山监测的图像，表征岩浆系统并可能在全球范围内预测喷发。然而,利用大型数据集受人工检查需求的限制，这意味着信息的及时传播是具有挑战性的。在这里，我们自动处理Sentinel-1卫星在5年内（2015-2020年）获取的超过1000座火山的约600,000张图像，并使用该数据集来演示机器学习在检测变形信号方面的适用性和局限性。在最常见的16座火山中，经历了5次喷发，6显示缓慢变形，2个具有非火山变形，3个具有大气伪影。整个数据集的检测阈值为5.9cm，相当于5年研究期间1.2厘米/年的速度。然后，我们使用大型测试数据集来探索大气条件的影响，土地覆盖和信号特征的可检测性，发现机器学习算法的性能主要受到可用数据质量的限制，连贯性差和信号缓慢尤其具有挑战性。系统获取的不断扩大的数据集，经过处理和标记的图像将能够以前所未有的规模对火山监测信号进行定量分析，但定制的处理将需要为常规监测应用程序。
UNASSIGNED：在线版本包含补充材料，可在10.1007/s00445-022-01608-x获得。