Detecting and modelling transient crustal deformation using Sentinel-1 InSAR and Machine Learning

使用 Sentinel-1 InSAR 和机器学习检测和建模瞬态地壳变形

• 批准号: 2604205
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2604205
• 关键词: Detecting; modelling; transient; crustal; deformation

As the quality of satellite geodetic observations of surface deformation from Global Navigation Satellite Systems (GNSS) and Satellite Radar Interferometry (InSAR) have improved it has been increasingly clear that fault behaviour is not steady in time. On short time scales, slow earthquakes, often associated with seismic tremor, have been observed at numerous subduction zones around the world (Burgmann, 2018), and some segments of strike-slip faults in the continents have also been shown to "creep" episodically in "slow" or "silent" earthquakes (Rousset et al., 2016) [Figure 1]. Significant changes in deformation rate have been observed on a decadal scale prior to major earthquakes (Mavrommatis et al., 2014). Following earthquakes, postseismic transient afterslip can last for up to a century (Ingleby and Wright, 2017). And on a longer time scale, faults have been observed to change their rates of slip over millennia (Cowie et al., 2017).The student in this project will use the wealth of geodetic data from Sentinel-1 InSAR, processed by COMET scientists in Leeds, alongside archive data from satellites including ERS and Envisat, and any available GNSS (GPS) data, to investigate how widespread transient behaviour is on faults. They will use machine learning approaches to mine the data to identify deformation transients. This may include the use of supervised learning methods to classify faults, as well as tools from fields such as time series analysis, point processes, and anomaly detection. They will use estimates of Quaternary slip rates to understand how deformation rates vary over different timescales. They will use the results to build and test models of the earthquake deformation cycle, to understand the geological controls on deformation transients, and to explore the impact of transient behaviour on our understanding of seismic hazard. The results will have potential applications for monitoring different hazards, including volcanoes worldwide and landslides and sinkholes in the UK.The project would suit a numerate student with a background in earth sciences, geology, or geophysics who is enthusiastic about problem solving and the use of EO and machine learning approaches. The student will be provided with training in state-of-the-art geodetic and machine learning methods and will have the opportunity to participate in field campaigns. The student will be part of the UK Natural Environmental Research Council's Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET) and will be expected to interact with COMET students with different skills and backgrounds from across the UK.References / Further ReadingBÜRGMANN, R. 2018. The geophysics, geology and mechanics of slow fault slip. Earth and Planetary Science Letters, 495, 112-134.COWIE, P. A., PHILLIPS, R. J., ROBERTS, G. P., MCCAFFREY, K., ZIJERVELD, L. J. J., GREGORY, L. C., FAURE WALKER, J., WEDMORE, L. N. J., DUNAI, T. J., BINNIE, S. A., FREEMAN, S. P. H. T., WILCKEN, K., SHANKS, R. P., HUISMANS, R. S., PAPANIKOLAOU, I., MICHETTI, A. M. & WILKINSON, M. 2017. Orogen-scale uplift in the central Italian Apennines drives episodic behaviour of earthquake faults. Scientific Reports, 7, 44858.INGLEBY, T. & WRIGHT, T. 2017. Omori-like decay of postseismic velocities following continental earthquakes. Geophysical Research Letters, 44, 3119-3130.MAVROMMATIS, A. P., SEGALL, P. & JOHNSON, K. M. 2014. A decadal-scale deformation transient prior to the 2011 Mw 9.0 Tohoku-oki earthquake. Geophysical Research Letters, 41, 4486-4494.ROUSSET, B., JOLIVET, R., SIMONS, M., LASSERRE, C., RIEL, B., MILILLO, P., ÇAKIR, Z. & RENARD, F. 2016. An aseismic slip transient on the North Anatolian Fault. Geophysical Research Letters, 43, 3254-3262.WEISS, J. R., WALTERS, R. J., MORISHITA, Y., WRIGHT, T. J., LAZECKY, M., WANG, H., HUSSAIN, E., HOOPER, A. J., ELLIOTT, J. R., ROLLINS, C., YU, C., GONZÁLEZ, P. J., SPAANS, K., LI, Z. &

随着全球导航卫星系统和卫星雷达干涉测量（干涉合成孔径雷达）对地表变形进行的卫星大地测量观测质量的提高，越来越明显的是，断层行为在时间上并不稳定。在短时间尺度上，在世界各地的许多俯冲带都观察到了通常与地震震颤相关的慢地震（Burgmann，2018），并且大陆上的一些走滑断层段也被证明在“慢”或“无声”地震中偶尔“蠕动”（Burgset等人，2016年）[图1]。在大地震之前，已经观察到十年尺度的变形率的显著变化（Mavromnovel等人，2014年）。地震发生后，震后瞬时后滑可持续长达世纪（Ingleby和Wright，2017）。在更长的时间尺度上，已经观察到断层在数千年内改变其滑动速率（Cowie等人，2017).该项目的学生将利用由利兹的COMET科学家处理的Sentinel-1干涉合成孔径雷达提供的大量大地测量数据，以及来自ERS和Envisat等卫星的档案数据，以及任何可用的全球导航卫星系统（GPS）数据，调查故障的瞬时行为有多普遍。他们将使用机器学习方法来挖掘数据，以识别变形瞬态。这可能包括使用监督学习方法对故障进行分类，以及使用来自时间序列分析、点过程和异常检测等领域的工具。他们将使用第四纪滑动速率的估计来了解变形速率如何在不同的时间尺度上变化。他们将利用这些结果来建立和测试地震变形周期的模型，了解地质对变形瞬变的控制，并探索瞬变行为对我们理解地震危害的影响。研究结果将有可能应用于监测不同的危险，包括世界各地的火山和英国的山体滑坡和天坑。该项目将适合具有地球科学，地质学或地球物理学背景的数学学生，他们热衷于解决问题以及使用EO和机器学习方法。学生将接受最先进的大地测量和机器学习方法的培训，并将有机会参加实地活动。该学生将成为英国自然环境研究理事会地震、火山和构造观测和建模中心（COMET）的一员，并将与来自英国各地具有不同技能和背景的COMET学生进行互动。2018.断层缓慢滑动的物理学、地质学和力学。地球和行星科学快报，495，112- 134.菲利普斯河J.，罗伯茨，G。P.的人，McCAFFREY，K.，济耶尔费尔德湖杰杰，格雷戈里湖C.的方法，Faure步行者，J.，韦德莫尔湖N. J.，杜奈J.，宾尼一、弗里曼P. H. T.，维尔肯，K.，尚克斯河P.的人，惠斯曼河S.，帕帕尼科拉岛，米切蒂，A. M. &威尔金森，M. 2017.意大利亚平宁山脉中部造山带规模的隆起驱动地震断层的幕式行为。科学报告，7，44858. & Wright，T. 2017.大陆地震后震后速度的Omori样衰减。Geophysical Research Letters，44，3119- 3130. MAVROMMATIS，A. P.，SEGALL，P. &约翰逊，K. M. 2014. 2011年东北冲9.0级地震前的十年尺度形变瞬变。地球物理研究快报，41，4486- 4494. ROUSSET，B.，Jolivet河，SIMONS，M.，拉塞尔角，RIEL，B.，MILILLO，P.，扎金索斯·扎金索斯& Renard，F. 2016.北安纳托利亚断层上的一次地震滑动瞬变。Geophysical Research Letters，43，3254- 3262.沃尔特斯河J.，Morishita，Y.，赖特J.，Lazecky，M.，王，H.，侯赛因，E.，霍普J.，埃利奥特，J.R.，罗林斯角，YU，C.，GONZÁLEZ，P. J.，K.斯帕恩斯，李，Z。&