论文标题
使用深度学习在InSAR时间序列中自主提取毫米级变形的自主提取
Autonomous Extraction of Millimeter-scale Deformation in InSAR Time Series Using Deep Learning
论文作者
论文摘要
在主动断层上进行滑动行为的系统表征是揭示构造断层物理和慢速和快速地震之间的相互作用的关键。干涉合成孔径雷达(INSAR),每隔几天就可以在全球范围内对地面变形进行测量,可以保留这些相互作用的关键。然而,尽管进行了最新的处理,但大气传播延迟通常超过了兴趣的地面变形,因此内部分析需要专家解释和对断层系统的先验知识,从而排除了全球对变形动态的研究。在这里,我们表明,量身定制的深层自动编码器体系结构是从Insar时间序列中自动提取变形信号的噪声的噪声变形的,而没有事先了解故障的位置或滑动行为。我们的方法应用于北安纳托利亚断层上的Insar数据,达到2 mM检测,揭示了慢速地震的速度是以前认识的两倍。我们进一步探讨了通货膨胀/通气诱导的变形方法的概括,并将相同的方法应用于加利福尼亚州Coso的地热场。
Systematic characterization of slip behaviours on active faults is key to unraveling the physics of tectonic faulting and the interplay between slow and fast earthquakes. Interferometric Synthetic Aperture Radar (InSAR), by enabling measurement of ground deformation at a global scale every few days, may hold the key to those interactions. However, atmospheric propagation delays often exceed ground deformation of interest despite state-of-the art processing, and thus InSAR analysis requires expert interpretation and a priori knowledge of fault systems, precluding global investigations of deformation dynamics. Here we show that a deep auto-encoder architecture tailored to untangle ground deformation from noise in InSAR time series autonomously extracts deformation signals, without prior knowledge of a fault's location or slip behaviour. Applied to InSAR data over the North Anatolian Fault, our method reaches 2 mm detection, revealing a slow earthquake twice as extensive as previously recognized. We further explore the generalization of our approach to inflation/deflation-induced deformation, applying the same methodology to the geothermal field of Coso, California.