Machine learning in seismic tomography

地震层析成像中的机器学习

• 批准号: 2393950
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2393950
• 关键词: Machine; learning; seismic; tomography

The aim of the project is to develop novel seismic data processing and imaging methods based on recent advances in machine learning and apply them to passive and active source data. In the case of passive data, the University of Cambridge has been operating temporary broadband seismic arrays in Iceland since 2006, and has assembled a world-class dataset that includes high quality recordings of tens of thousands of earthquakes related to its unique tectonic setting (Ágústsdóttir et al., 2016). Particular target areas include the plumbing systems beneath recently active volcanoes, such as those found in the Northern Volcanic Zone (Greenfield et al. 2016). For example, the Askja volcanic system, which last experienced an eruption as recently as 1961, is one of the largest volcanic systems in Iceland, yet we have only recently begun to understand its internal structure and dynamics. These volcanic regions also have geothermal energy potential that has not yet been fully explored, which is another possible target for newly developed imaging methods. Moreover, the high concentration of seismic instruments over the active rift zone means that methods such as attenuation tomography are well placed to image structure related to crustal accretion processes. The involvement of CGG means that the student will also have access to OBS and OBN data on which to test the newly developed methodology and workflows. A useful side project for the student to undertake would be to invert ambient noise surface wave data from the OBN array for subsurface structure; this would be an interesting application of machine learning that could be compared to recent results from similar applications on land.

该项目的目的是在机器学习最新进展的基础上开发新的地震数据处理和成像方法，并将其应用于被动和主动震源数据。在被动数据方面，剑桥大学自2006年以来一直在冰岛运营临时宽带地震台阵，并汇编了一个世界级的数据集，其中包括与其独特构造环境有关的数万次地震的高质量记录(ágústsdóttir等人，2016年)。具体的目标区域包括最近活火山之下的管道系统，例如在北部火山带发现的那些(Greenfield等人。2016)。例如，最近一次喷发的Askja火山系统是冰岛最大的火山系统之一，但我们直到最近才开始了解其内部结构和动态。这些火山区还拥有尚未完全勘探的地热能潜力，这是新开发的成像方法的另一个可能目标。此外，活动裂谷带上方的地震仪器高度集中，这意味着衰减层析成像等方法非常适合与地壳吸积过程有关的图像结构。CGG的参与意味着学生还可以访问OBS和OBN数据，以测试新开发的方法和工作流程。学生要做的一个有用的附带项目是从OBN阵列中反演环境噪声表面波数据，以了解地下结构；这将是机器学习的一个有趣的应用，可以与陆地上类似应用的最新结果进行比较。