Seismotectonics of the Lesser Antilles Arc

背景：俯冲带是世界上最大地震的发生地，有时还会引发毁灭性的海啸。人们对小安的列斯岛弧（LAA）的地震潜力知之甚少，这在很大程度上是因为很难沿着这条由小岛组成的岛弧获取数据。历史上两次地震被认为是典型的俯冲逆冲事件，可能具有引发海啸的潜力。然而，大逆冲地震耦合的程度仍存在争议。加勒比地区保护不力的大型社区面临的潜在风险很大。原理：在最近的一项大型财团资助下，我们收集了新的地震数据，包括在LAA内部和周围安装的海底仪器。我们的实验数据被用来对俯冲带进行空前详细的成像。我们编制了一份在部署期间检测到的地震的初步目录，并开发了一种新的矩张量方法来表征断层样式。我们已经能够追踪更广泛的水合进入板块的区域是如何与沿弧的小地震和增强的火山生产力的集中相关联的。下一阶段是对VoiLA数据进行深入分析，以提高对LAA地震潜力的认识。这个项目：在这个博士项目中，volila地震数据将与俯冲带的热模型和力学模型一起使用，以提高对LAA地震潜力和构造的理解。机器学习技术将用于识别和定位尽可能多的事件，以表征空间分布，大小频率特征以及可能的事件风格。这些结果将与现有的地震目录相结合，然后与基于我们对俯冲带的详细成像而定制的模型对热结构和俯冲板块应力的预测进行比较。考虑到LAA是一个重要的端元，该学生将加入帝国板块、地幔和地核小组，研究浅层和深部地球物理成像、地表过程和地幔动力学。

Background: Subduction zones are the sites of the world's largest earthquakes, sometimes generating devastating tsunamis. The earthquake potential of the Lesser Antilles Arc (LAA) is poorly known, largely because of the challenge of obtaining data along this arc of small islands. Two historic earthquakes are thought to have been classical subduction thrust events, possibly with tsunamigenic potential. However, the extent of megathrust seismic coupling remains debated. The potential risk to large, poorly-protected communities in the Caribbean region is large.Rationale: In a recent large consortium grant, VoiLA, we collected new seismic data, including seabed-installed instruments, in and around the LAA. Data from our experiment has been used to image the subduction zone in unprecedented detail. We have compiled a preliminary catalogue of earthquakes detected during our deployment and developed a new moment tensor method to characterise faulting styles. We have been able to track how regions of more extensively hydrated incoming plate correlate with concentrations of small earthquakes and enhanced volcanic productivity along the arc. The next stage is to undertake an in-depth analysis of the VoiLA data to improve understanding of earthquake potential of the LAA.This project: In this PhD project the VoiLA seismic data will be used together with thermal and mechanical odels of the subduction zone to improve the understanding of the seismic potential and tectonics of the LAA. Machine learning techniques will be used to identify and locate as many events as possible, to characterise the spatial distribution, size-frequency characteristics and where possible styles of events. These results will be combined with existing earthquake catalogues, and then compared with predictions of the thermal structure and subducting plate stresses from models that are tailored based on our detailed imaging of the subduction zone. Given the LAA is an important end-member, where slow-convergence of The student would join the Imperial Plates, Mantle and Core group working on shallow and deep geophysical imaging, surface processes and mantle dynamics.