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（GNSS）和卫星雷达干涉法（INSAR）的表面变形的卫星测量观测质量已改善，因此越来越明显的是，断层行为的时间并不稳定。在短时间内，在世界各地的许多俯冲带中都观察到了经常与地震树相关的缓慢地震（Burgmann，2018年），并且连续存在中的一些走滑断层的某些段也被证明在“慢”或“ silter”地球上的“ slite”或“ silter”地球上的“蠕变”（Rouset et al。，2016年）[图1] [图1]。在大地地震之前，已经在十年尺度上观察到变形率的显着变化（Mavrommatis等，2014）。跟随地震，后瞬间瞬间滑行可以持续一个世纪（Ingleby and Wright，2017年）。 And on a longer time scale, faults have They 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 satelites including ERS ​​and Envisat, and any available GNSS (GPS) data, to investigate how widespread transient behaviour is on faults.他们将使用机器学习方法来挖掘数据以识别变形瞬变。这可能包括使用监督学习方法对故障进行分类，以及时间序列分析，点过程和异常检测等领域的工具。他们将使用第四纪滑移速率的估计来了解变形率在不同的时间范围内的变化方式。他们将使用结果来构建和测试地震变形周期的模型，了解对变形瞬变的地质控制，并探讨瞬态行为对我们对地震危险的理解的影响。结果将有潜在的应用程序来监测不同的危害，包括全球火山以及英国的山地滑坡和凹坑。该项目将适合具有地球科学，地质或地球物理学背景的数字学生，他们对解决问题以及EO和机器学习方法的使用充满热情。将向学生提供最先进的大地测量和机器学习方法的培训，并有机会参加现场活动。该学生将成为英国自然环境研究委员会的一部分，地震，火山和构造学（彗星）的观察和建模中心，并有望与英国各地具有不同技能和背景的彗星学生进行互动。参考 /进一步的ReadingBürgmann，R.2018。2018年。地球物理学，地球物理学，地理学和机械师。地球和行星科学来信，495，112-134.Cowie，P.A.，Phillips，R.J.，Roberts，G.P.，McCaffrey，K.尚克斯（R.科学报告，7，44858。地球物理研究信，44，3119-3130。地球物理研究信，41，4486-4494.Rousset，B.，Jolivet，R.，Simons，M.，Lasserre，C.，Riel，B.，Milillo，P.，Z.＆Renard，Z。＆Renard，F.2016。地球物理研究信，43，3254-3262.Weiss，J.R.，Walters，R.J.，Morishita，Y.