Machine learning in seismic tomography

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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.