We provide particle image correlation data from the 16 laboratory experiments with Foamquake seismotectonic model simulating analog megathrust seismic cycles and supporting scripts. To monitor analog seismic cycles, we use a high-resolution camera taking images at 50 frames per second as an analog of a geodetic satellite. We are using a trench orthogonal surface velocity time series extracted from the central points located above the seismic asperities using Particle Image Velocimetry (PIV) method.
The scripts and datasets are provided as supplementary materials to the article "Neighbouring segments control on earthquake recurrence patterns: Insights from scaled seismotectonic models" by Latypova et al., 2025. The data originate from analog experiments using the Foamquake seismotectonic model, designed at the Laboratory of Experimental Tectonics (LET) at Roma Tre University to replicate megathrust seismic cycles.
Observations were recorded with a high-resolution camera, and surface velocity fields were extracted using the Particle Image Velocimetry (PIV) technique, which applies cross-correlation between consecutive frames.
This dataset includes the results of Particle Image Velocimetry (PIV) 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; https://aspsync.wordpress.com). Detailed descriptions of the experiments and monitoring techniques can be found in Corbi et al. (2017). This data set is from one experiment characterized by the presence of a 7 cm wide barrier separating two asperities with equal size, geometry and friction. Here we provide PIV data relative to a 16.3 min long interval during which the experiment produces 138 analog earthquakes with an average recurrence time of 7 s. The PIV analysis yields quantitative information 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 velocity variations (Fig. 2 in Corbi et al., 2021) and rupture patterns (Fig. 3a, b in Corbi et al., 2021) occurring during the velocity peak of one of the two asperities (aka trigger).
This dataset includes particle image correlation data from 26 experiments performed with Foamquake, a novel analog seismotectonic model reproducing the megathrust seismic cycle. The seismotectonic model has been monitored by the means of a high-resolution top-view monitoring camera. The dataset presented here represents the particle image velocimetry surface velocity field extracted during the experimental model through the cross-correlation between consecutive images. This dataset is supplementary to Mastella et al. (2021) where detailed descriptions of models and experimental results can be found.
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.