Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2019GL084993 |
The Application of Convolutional Neural Networks to Detect Slow, Sustained Deformation in InSAR Time Series | |
Anantrasirichai, N.1; Biggs, J.2; Albino, F.2; Bull, D.1 | |
2019-11-07 | |
发表期刊 | GEOPHYSICAL RESEARCH LETTERS |
ISSN | 0094-8276 |
EISSN | 1944-8007 |
出版年 | 2019 |
卷号 | 46期号:21页码:11850-11858 |
文章类型 | Article |
语种 | 英语 |
国家 | England |
英文摘要 | Automated systems for detecting deformation in satellite interferometric synthetic aperture radar (InSAR) imagery could be used to develop a global monitoring system for volcanic and urban environments. Here, we explore the limits of a convolutional neural networks for detecting slow, sustained deformations in wrapped interferograms. Using synthetic data, we estimate a detection threshold of 3.9 cm for deformation signals alone and 6.3 cm when atmospheric artifacts are considered. Overwrapping reduces this to 1.8 and 5.2 cm, respectively, as more fringes are generated without altering signal to noise ratio. We test the approach on time series of cumulative deformation from Campi Flegrei and Dallol, where overwrapping improves classification performance by up to 15%. We propose a mean-filtering method for combining results of different wrap parameters to flag deformation. At Campi Flegrei, deformation of 8.5 cm/year was detected after 60 days and at Dallol, deformation of 3.5 cm/year was detected after 310 days. This corresponds to cumulative displacements of 3 and 4 cm consistent with estimates based on synthetic data. |
英文关键词 | Interferometric Synthetic Aperture Radar machine learning detection deformation |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000494954100001 |
WOS关键词 | UNREST EPISODES ; CAMPI FLEGREI ; VOLCANO ; SUBSIDENCE ; INSIGHTS ; UPLIFT ; GPS |
WOS类目 | Geosciences, Multidisciplinary |
WOS研究方向 | Geology |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/224973 |
专题 | 环境与发展全球科技态势 |
作者单位 | 1.Univ Bristol, Visual Informat Lab, Bristol, Avon, England; 2.Univ Bristol, Sch Earth Sci, Bristol, Avon, England |
推荐引用方式 GB/T 7714 | Anantrasirichai, N.,Biggs, J.,Albino, F.,et al. The Application of Convolutional Neural Networks to Detect Slow, Sustained Deformation in InSAR Time Series[J]. GEOPHYSICAL RESEARCH LETTERS,2019,46(21):11850-11858. |
APA | Anantrasirichai, N.,Biggs, J.,Albino, F.,&Bull, D..(2019).The Application of Convolutional Neural Networks to Detect Slow, Sustained Deformation in InSAR Time Series.GEOPHYSICAL RESEARCH LETTERS,46(21),11850-11858. |
MLA | Anantrasirichai, N.,et al."The Application of Convolutional Neural Networks to Detect Slow, Sustained Deformation in InSAR Time Series".GEOPHYSICAL RESEARCH LETTERS 46.21(2019):11850-11858. |
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