Preprints
https://doi.org/10.5194/gi-2023-4
https://doi.org/10.5194/gi-2023-4
08 May 2023
 | 08 May 2023
Status: this preprint was under review for the journal GI but the revision was not accepted.

Benefits of using convolutional neural networks for seismic data quality analysis

Paolo Casale and Alessandro Pignatelli

Abstract. Seismic data represent an excellent source of information and can be used to investigate several phenomena such as earthquake nature, faults geometry, tomography etc. These data are affected by several types of noise that are often grouped into two main classes: anthropogenic and environmental ones. Nevertheless instrumental noise or malfunctioning stations detection is also a relevant step in terms of data quality control and in the efficiency of the seismic network. As we will show, visual inspection of seismic spectral diagrams allows us to detect problems that can compromise data quality, for example invalidating subsequent calculations, such as Magnitude or Peak Ground Acceleration (PGA). However, such visual inspection requires human experience (due to the complexity of the diagrams), time demanding and effort as there are too many stations to be checked. That’s why, in this paper, we have explored the possibility of “transferring” such human experience into an artificial intelligence system in order to automatically and quickly perform such detection. The results have been very encouraging as the automatic system we have set up shows a detection accuracy of over 90 % on a set of 840 noise spectral diagrams obtained from seismic station records.

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Paolo Casale and Alessandro Pignatelli

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gi-2023-4', Anonymous Referee #1, 31 May 2023
  • RC2: 'Comment on gi-2023-4', Anonymous Referee #2, 02 Jun 2023
  • AC1: 'Comment on gi-2023-4', Alessandro Pignatelli, 23 Jun 2023
  • AC2: 'Further answers to comments (to be completed in the revised version)', Alessandro Pignatelli, 27 Jun 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gi-2023-4', Anonymous Referee #1, 31 May 2023
  • RC2: 'Comment on gi-2023-4', Anonymous Referee #2, 02 Jun 2023
  • AC1: 'Comment on gi-2023-4', Alessandro Pignatelli, 23 Jun 2023
  • AC2: 'Further answers to comments (to be completed in the revised version)', Alessandro Pignatelli, 27 Jun 2023
Paolo Casale and Alessandro Pignatelli
Paolo Casale and Alessandro Pignatelli

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Short summary
Thanks to technological developments, collecting seismic signals is hugely increasing. Unfortunately, having more data is subject to limited human capability of handling such data in reasonable time. That's why, in this paper, we propose to “transfer” the human experience into an artificial intelligence based system able to automatically distinguish seismometers collected data as “good” or “bad” using spectral diagram images.