Development of a dataservice for precomputed seismic noise cross-correlations functions

Analysis of ambient seismic noise using interferometric techniques has become a common tool in seismology. Typical applications include the use of seismic noise correlation wavefields for imaging of the subsurface and observing velocity changes over time, e.g. to monitor a volcano or hydrological conditions. All techniques rely on timeseries of noise cross-correlation functions (CCFs) between pairs of stations and/or channels which require computationally expensive processing steps that are most often preceded by downloading huge amounts of raw data. Even though there is not (yet) a single unique processing scheme, most researchers apply a selection of fairly standard procedures and aim for similar outcomes. Hence, many researchers are currently repeating very similar computations over and over again.

We believe that these computational costs and data traffic can be drastically reduced by processing the data and computing the CCFs directly at the data center and providing them as a data product to the community. We also believe that the open availability of community datasets of ambient noise correlation functions will facilitate new research in the field of seismic interferometry as it removes the computational burden of the initial processing. We are currently developing such a data service to be hosted at the GEOFON data center. We envision that researchers can request the computation of CCF data sets for stations and time periods of their interest using a combination of the standard processing methods of their choice. The typical use-case is the computation of a CCF dataset for a temporary networks initiated by the PI of the experiment. The resulting dataset of CCFs will then be available to the community via a webservice. The processing steps and necessary parameters can be selected and tested beforehand using a small subset of stations.

Although still in a very early stage, we are interested in feedback, ideas and suggestions from and exchange with potential users. By providing pre-computed CCFs, we hope, to not only save network and computational resources but also enable researchers from different fields, e.g. environmental research, to benefit from seismological research and data.