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 imposing 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 presence of changepoints that separate the seismic events in time windows. Within each time window, the DD data are consistent 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) retrieved 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 waveform similarity (measured through the cross-correlation coefficients); and (c) the changepoint distribution in time seems to be insensitive to variations in the seismic network geometry 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).