Shallow Subsurface Imaging Using Challenging Urban DAS Data

Abstract

Abstract We present a workflow for producing shallow subsurface velocity models from passive urban distributed acoustic sensing (DAS) data. This method is demonstrated using a dataset collected in Bern, Switzerland, using in situ telecommunications fiber. We compute noise correlations to extract Rayleigh-wave dispersion curves, which we then use to produce a series of overlapping 1D velocity models of the top tens of meters of the subsurface. This dataset represents a realistic “best-case” scenario when using real urban telecommunications fiber—the cable layout is linear, its location is well known, and coupling is broadly sufficient. Nevertheless, a number of nontrivial complexities still exist in such a dataset and are highlighted in this study. Rather than prescribing one optimal workflow for all similar experiments, we focus on the steps taken and decisions made that led to a velocity model in this setting. It is our hope that such a text will be useful to future researchers exploring DAS interferometry and may provide some guidance on overcoming the difficulties and imperfections of working with such datasets.