Preprints
https://doi.org/10.5194/se-2021-79
https://doi.org/10.5194/se-2021-79

  11 Jun 2021

11 Jun 2021

Review status: this preprint is currently under review for the journal SE.

Exploration of the data space via trans-dimensional sampling: the case study of seismic double difference data

Nicola Piana Agostinetti1,2 and Giulia Sgattoni3 Nicola Piana Agostinetti and Giulia Sgattoni
  • 1Department of Earth and Environmental Sciences, Universitá di Milano Bicocca, Milano, Italy
  • 2Department of Geology, Universitat Wien, Althanstrasse 14, 1090, Wien, Austria
  • 3Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Bologna, Bologna, Italia

Abstract. Double differences (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 earthquakes recordings through expert-opinion, where couples of earthquakes are selected based on some user-defined criteria, and DD data are computed from the selected couples. 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 couples of events. We apply it to a seismic database recorded on the flank of Katla volcano (Iceland), where elasticity variations in time has 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 behavior of the trans-dimensional algorithm, only changepoints supported by the data are retrieved. Our results indicate that: (a) retrieved changepoints are consistent with first-order variations in the data (i.e. most striking changes in the DD data are correctly reproduced in the changepoint distribution in time); (b) changepoint locations in time do correlate neither with changes in seismicity rate, nor with changes in waveforms similarity (measured through the cross-correlation coefficients); and (c) noteworthy, the changepoint distribution in time seems to be insensitive to variations in the seismic network geometry during the experiment. Our results proofs that trans-dimensional algorithms can be positively applied to pre-processing of geophysical data before the application of standard routines (i.e. before using them to solve standard geophysical inverse problems) in the so called exploration of the data space.

Nicola Piana Agostinetti and Giulia Sgattoni

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on se-2021-79', Cliff Thurber, 27 Jun 2021
  • RC2: 'Comment on se-2021-79', Jiaqi Li, 27 Jun 2021
  • RC3: 'Comment on se-2021-79', Anonymous Referee #3, 15 Jul 2021

Nicola Piana Agostinetti and Giulia Sgattoni

Nicola Piana Agostinetti and Giulia Sgattoni

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Short summary
One of the present-day challenges for geoscientists is tackling the Big Data revolution. An ever-growing amount of data needs to be processed and data are subjectively handled before using them to make inferences on the Earth’s interior. But imposing subjective decisions on the data might have strong influences on the final outputs. Here we present a totally novel and automatic application for screening the data and for defining data-volumes that are consistent with physical hypotheses.