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Magnetic susceptibility and thin sections data from the the Alftafjordur volcanic system in Eastern Iceland

This dataset contains supplementary materials to the manuscripts “Interpreting inverse magnetic fabric in Miocene dikes from Eastern Iceland” by Trippanera et al., (submitted to JGR) and “Anatomy of an extinct magmatic system along a divergent plate boundary: Alftafjordur, Iceland” by Urbani et al. 2015. These works present an extensive multi-scale and multi-disciplinary study focused on the magnetic fabric of dikes belonging to the Alftafjordur volcanic system in Eastern Iceland. Eastern Iceland is one of the most suitable places to analyze the roots of the volcanic systems that are composed of central volcanoes and fissure swarms. We sampled 19 NNE-SSW oriented dikes (for a total of 383 samples) belonging to the exhumed fissure swarm portion of Alftafjordur volcanic system, aiming at understanding the direction of magma propagation in the swarm by using Anisotropy of Magnetic Susceptibility (AMS) analysis. However, most of the samples (80% out of the measured cores) show an inverse geometric magnetic fabric (kmax is perpendicular to the dike margins and sub-horizontal)- therefore the study of the flow direction is complicated. Nevertheless, this result poses the problem of why the geometrically inverse fabric is present and widespread in the whole dike swarm. In order to understand the origin of this inverse fabric, besides standard AMS measurements, we also performed additional analysis such as different field and temperature AMS, Anisotropy of Anhystheretic Remanent Magnetization (AARM), Hysteresis loops and First-order reversal curves (FORC), Scanning Electron Microscope (SEM) and Optic microscope images analysis. This dataset includes the following materials: • Location of the sampled sites (.kml) • AMS measurements at room temperature by using H=300 A/m for all samples (.ran) • AMS measurements at room temperature by using H=200 A/m and H=600 A/m for selected samples (.ran) • AMS measurements at different temperature (from 20 to 580 ℃) for selected samples (.ran) • AARM measurements for selected samples (.ran) • DayPlots data for selected samples (.xls or .csv) • SEM and Optical microscope images of thin sections of selected samples. AMS and AARM data can be opened through Anisoft open-source software provided by Agico (Chadima and Jelinek, 2009; https://www.agico.com/text/software/anisoft/anisoft.php). Data have been acquired at: Roma Tre University (Rome, Italy), Istuto di Geofisica e Vulcanologia (INGV, Rome, Italy) and Laboratoire des Sciences du Climat et de l'Environnement, CEA, CNRS, UVSQ (Gif-sur-Yvette Cedex, France). For the interpretation of the data refer to Urbani et al., 2015 and Trippanera et al., (submitted). The description of each dataset is provided in the description file.

Supplementary material to "Rough subducting seafloor reduces interseismic coupling and mega-earthquake occurrence: insights from analogue models"

This dataset contains digital image correlation (DIC) data of eight seismotectonic analogue experiments that were performed at the Laboratory of Experimental Tectonics (LET), Univ. Rome Tre, to investigate the effect of subduction interface roughness on the seismogenic behaviour of the megathrust. The study has been done in the framework of the Marie Sklodowska-Curie grant agreement 642029 – ITN CREEP. Together with DIC data we also provide analogue earthquake characteristics and Matlab scripts for visualization. Here we provide Digital Image Correlation data for eight experiments that last about 20 minutes (i.e., including tens of seismic cycles), of which four experiments include a smooth subduction interface and four a rough subduction interface. The DIC analysis provides a velocity field between two consecutive frames, measured at the surface of the model. Details about the nature and geometry of this interface, as well as the experimental procedure, model set-up and materials can be found in van Rijsingen et al. (2019), paragraph 2 and supporting information. A more detailed description of the data that we provide, the methods and the matlab scripts used for visualisation can be found in the data description file. An overview of the dataset can be found in the list of files.

What controls sill formation: an overview from analogue models

This data publication includes movies and figures of twenty-six analogue models which are used to investigate what controls sill emplacement, defining a hierarchy among a selection of the proposed factors: compressive stresses, interface strength between layers, rigidity contrast between layers, density layering, ratio of layer thickness, magma flow rate and driving buoyancy pressure (Sili et al., 2019). Crust layering is simulated by pig-skin gelatin layers and magma intrusions is simulated by colored water. The experimental set-up is composed of a 40.5 X 29 X 40 cm3 clear-Perspex tank where a mobile wall applies a deviatoric compressive stress (C, in Table 1) to the solid gelatin (Figure 1). In each experiment is imposed two layers with different density and rigidity, separated by a weak or strong interface, excluding two experiments characterized by homogeneous gelatin (experiment 4 and 12). Three different rigidity contrast (1, 1.3, 1.8) between the two layers are imposed, defined as the ratio between the Young’s moduli of the upper (Eu) and lower (El) layer. By using NaCl and gelatin concentration, two layers with same rigidity but different densities are obtained, investigating the influence of the density contrasts on sill emplacement. The effects of the ratio between layer thicknesses (i.e. the ratio between upper and lower layer thickness: Thu/Thl) was simulated by changing only the thickness of the upper layer; while magma flow rate are studied changing the flow rate of peristaltic pump. Water density was increased by adding NaCl to analyze the effect of changing driving buoyancy pressure (Pm) that depends on the density difference between host rock and magma (Δρ), gravitational acceleration (g) and intrusion length (H). In the table different colors indicate the experiment result: black = dike; red = sill and blue = sheet. The here provided material includes time-lapse movies showing intrusion propagation of the twenty-six models with a velocity of 5 times higher compared to the real time (1 second in the movie is 25 real seconds). These visualizations are side (XZ or YZ plane in Figure 1) and/or top views (XY plane in Figure 1).

Supplementary Material for Analogue Experiments on Lateral versus Vertical Dike Propagation

The dataset includes movies of 29 analogue experiments performed to investigate the effects on dike propagation by the following imposed parameters: density ratio between host-rock and magma analogues, rigidity layering and density layering of the host medium, flow rate and topography. The purpose of the experiments is to define a hierarchy of all the parameters considered, by varying systematically each of them, comparing semi-quantitatively the variations on dike geometry and velocity. Experimental setup The experimental set-up consists of a 33 × 58 × 38.5 cm3 Plexiglas box and a peristaltic pump that injects water (magma analogue) into pig-skin gelatin (crustal analogue) alternatively from the bottom (Set 1) and the side of the box (Set 2). The gelatin rheological properties are varied by mixing different concentrations of gelatin powder and NaCl. We refer to “rigidity layering” when the rigidity ratio (i.e. Young’s Modulus) between the upper and lower layer (Eu/El) is < or > 1, and to “density layering” when Eu/El ~ 1, but the two layers show different densities (i.e. the ratio between the density of the upper and lower layer, ρU/ρL). The experiments with topography are prepared by imposing a mold with gently inward dipping flanks (2.4° and 3.7°) on the opposite sides of the box separated by a 8 cm wide horizontal plain on the gelatin surface. This configuration simulates the 2-D along-strike topography of the 2014 Bardarbunga intrusion (Iceland) and allows investigating the role of two opposite slopes on dike propagation. The topography profile dips parallel to the long side of the Plexiglas box (x axis in Figure 1 of Urbani et al. 2018). The flow rate has been changed between 0.079 and 0.435 ml/s. For the details about the model set-up, experimental results and interpretation refer to Urbani et al. (2018). The time-lapse movies show the time evolution of the dike shape, in side and map view, of 29 out of 33 models presented in Urbani et al. (2018). It is recommended to open the films with the VLC media player. The time-lapse of each experiment is indicated in the bottom left corner. A full list of files is given in “Experiments_Summary_2018-012.pdf” in which Set 1 (bottom injection) and Set 2 (lateral injection) experiments are indicated in red and blue color respectively. The same file also provides a summary of the boundary conditions imposed in each experiment. Tu and Tl indicate the thickness of the upper and lower layer respectively.

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

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.

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