Changepoint detection in seismic double-difference data: application of a trans-dimensional algorithm to data-space exploration

Abstract

Double-difference (DD) seismic data are widely used to define elasticity distribution in the Earth’s interior and its variation in time. DD data are often pre-processed from earthquake recordings through expert opinion, whereby pairs of earthquakes are selected based on some user-defined criteria and DD data are computed from the selected pairs. We develop a novel methodology for preparing DD seismic data based on a trans-dimensional algorithm, without impos- ing pre-defined criteria on the selection of event pairs. We apply it to a seismic database recorded on the flank of Katla volcano (Iceland), where elasticity variations in time have been indicated. Our approach quantitatively defines the pres- ence of changepoints that separate the seismic events in time windows. Within each time window, the DD data are con- sistent with the hypothesis of time-invariant elasticity in the subsurface, and DD data can be safely used in subsequent analysis. Due to the parsimonious behaviour of the trans- dimensional algorithm, only changepoints supported by the data are retrieved. Our results indicate the following: (a) re- trieved changepoints are consistent with first-order variations in the data (i.e. most striking changes in the amplitude of DD data are correctly reproduced in the changepoint distribution in time); (b) changepoint locations in time correlate neither with changes in seismicity rate nor with changes in wave- form similarity (measured through the cross-correlation co- efficients); and (c) the changepoint distribution in time seems to be insensitive to variations in the seismic network ge- ometry during the experiment. Our results demonstrate that trans-dimensional algorithms can be effectively applied to pre-processing of geophysical data before the application of standard routines (e.g. before using them to solve standard geophysical inverse problems).