Supplementary material to "Machine Learning can predict the timing and size of analog earthquakes"

Corbi, Fabio; Sandri, Laura; Bedford, Jonathan; Funiciello, Francesca; Brizzi, Silvia; Rosenau, Matthias; Lallemand, Serge;

2016-01

This data set includes the results of digital image correlation of one experiment on subduction megathrust earthquakes with interacting asperities performed at the Laboratory of Experimental Tectonics (LET) Univ. Roma Tre in the framework of AspSync, the Marie Curie project (grant agreement 658034) lead by F. Corbi in 2016-2017. Detailed descriptions of the experiments and monitoring techniques can be found in Corbi et al. (2017 and 2019) to which this data set is supplementary material.



We here provide Digital Image Correlation (DIC) data relative to a 7 min long interval during which the experiment 
produces 40 seismic cycles with average duration of about 10.5 s (see Figure S1 in Corbi et al., 2019). The DIC analysis yields quantitative about the velocity field characterizing two consecutive frames, measured in this case at the model surface. For a detailed description of the experimental procedure, set-up and materials used, please refer to the article of Corbi et al. (2017) paragraph 2. This data set has been used for: a) studying the correlation between apparent slip-deficit maps and earthquake slip pattern (see Corbi et al., 2019; paragraph 4); and b) as input for the Machine Learning investigation (see Corbi et al., 2019; paragraph 5).



Further technical information about the methods, data products and matlab scripts is proviced in the data description file. The list of files explains the file and folder structure of the data set.



Originally assigned keywords

Corresponding MSL vocabulary keywords

MSL enriched keywords

Originally assigned sub domains
  • analogue modelling of geologic processes
MSL enriched sub domains
  • analogue modelling of geologic processes
Source http://dx.doi.org/doi:10.5880/fidgeo.2018.071
DOI 10.5880/fidgeo.2018.071
License CC BY 4.0
Authors
  • Bedford, Jonathan
  • 0000-0002-8954-4367
  • GFZ German Research Centre for Geosciences, Potsdam, Germany
  • Rosenau, Matthias
  • 0000-0003-1134-5381
  • GFZ German Research Centre for Geosciences, Potsdam, Germany
  • Lallemand, Serge
  • 0000-0003-1924-9423
  • Géosciences Montpellier, CNRS, Montpellier, France / Montpellier University, Montpellier, France
References
  • Corbi, F., Sandri, L., Bedford, J., Funiciello, F., Brizzi, S., Rosenau, M., & Lallemand, S. (2019). Machine Learning Can Predict the Timing and Size of Analog Earthquakes. Geophysical Research Letters, 46(3), 1303–1311. Portico. https://doi.org/10.1029/2018gl081251
  • 10.1029/2018GL081251
  • IsSupplementTo
  • Corbi, F., Funiciello, F., Brizzi, S., Lallemand, S., & Rosenau, M. (2017). Control of asperities size and spacing on seismic behavior of subduction megathrusts. Geophysical Research Letters, 44(16), 8227–8235. Portico. https://doi.org/10.1002/2017gl074182
  • 10.1002/2017GL074182
  • IsSupplementTo
  • References
  • Corbi, F., Bedford, J., Sandri, L., Funiciello, F., Gualandi, A., & Rosenau, M. (2020). Predicting Imminence of Analog Megathrust Earthquakes With Machine Learning: Implications for Monitoring Subduction Zones. Geophysical Research Letters, 47(7). Portico. https://doi.org/10.1029/2019gl086615
  • 10.1029/2019GL086615
  • IsSupplementTo
Contact
  • Corbi, Fabio
  • Univ. Roma Tre
  • fabio.corbi3@gmail.com
Citation Corbi, F., Sandri, L., Bedford, J., Funiciello, F., Brizzi, S., Rosenau, M., & Lallemand, S. (2018). Supplementary material to "Machine Learning can predict the timing and size of analog earthquakes" [Data set]. GFZ Data Services. https://doi.org/10.5880/FIDGEO.2018.071