ALBERT

Description: This dataset provides friction and elasticity data from ring shear and axial tests, respectively, on rock analogue materials used at the University Roma Tre (Rome, IT) in “Foamquake”, a novel seismotectonic analog model mimicking the megathrust seismic cycle (Mastella et al., under review). Two granular materials (quartz sand and Jasmine rice) have been characterized by means of internal friction coefficients µ and cohesions C. An elastic material (foam rubber) have been characterized by means of Young’s modulus E and Poisson’s ratio v. According to our analysis the granular materials show Mohr-Coulomb behaviour characterized by linear failure envelopes in the shear stress vs. normal load Mohr space. Peak, dynamic and reactivation friction coefficients of the quartz sand are µP = 0.69, µD = 0.56 and µR = 0.64, respectively. Cohesion ranges between 50 and 100 Pa. Rate-dependency of friction in quartz sand seems insignificant. Peak, dynamic and reactivation friction coefficients of the Jasmine rice are µP = 0.70, µD = 0.59 and µR = 0.61, respectively. Cohesion ranges between 30 and 50 Pa. Rate-weakening of Jasmine rice is c. 6% per tenfold change in shear velocity v. The Young’s modulus of the foam rubber has been constrained to 30 kPa, its Poisson’s ratio is v=0.1.

Description: 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.

Description: In the last decades, seismotectonic analog models have been developed to better understand many aspects of the seismic cycle. Differently from other lab-quake experiments, seismotectonic models mimic the first order characteristics of the seismic cycle in a scaled fashion. Here we introduce Foamquake: a novel seismotectonic model with a granular frictional interface that as a whole behaves elastoplastically. The model experiences cycles of elastic loading and release via spontaneous nucleation of frictional instabilities at the base of an elastic foam wedge, hereafter called foamquakes. These analog earthquakes show source parameters (i.e., moment-duration and moment-rupture area) scaling as great interplate earthquakes and a coseismic displacement of few tens of meters when scaled to nature. Models with two asperities separated by a barrier can be performed with Foamquake given the 3D nature of the setup. Such model configuration generates sequences of full and partial ruptures with different recurrence intervals as well as rupture cascades. By tuning the normal load acting on individual asperities, Foamquake reproduces superimposed cycles rupture patterns such as those observed along natural megathrusts. The physical properties of asperities and barriers affect model seismic behavior. Asperities with similar properties and low yield strength fail preferentially in a simultaneous manner. The combination of all those characteristics suggests that Foamquake is a valuable tool for investigating megathrust seismicity and seismic processes that depend on the 3D nature of the subduction environment.

Description: EPOS, the European Plate Observing System, is a unique e-infrastructure and collaborative environment for the solid earth science community in Europe and beyond (https://www.epos-eu.org/). A wide range of world-class experimental (analogue modelling and rock and melt physics) and analytical (paleomagnetic, geochemistry, microscopy) laboratory infrastructures are concerted in a “Thematic Core Service” (TCS) labelled “Multi-scale Laboratories” (MSL) (https://www.epos-eu.org/tcs/multi-scale-laboratories). Setting up mechanisms allowing for sharing metadata, data, and experimental facilities has been the main target achieved during the EPOS implementation phase. The TCS Multi-scale Laboratories offers coordination of the laboratories’ network, data services, and Trans-National access to laboratory facilities. In the framework of data services, TCS Multi-Scale Laboratories promotes FAIR (Findable-Accessible-Interoperable-Re-Usable) (FAIR) sharing of experimental research data sets through Open Access data publications. Data sets are assigned with digital object identifiers (DOI) and are published under the CC BY license. Data publications are now conventionally citable in scientific journals and develop rapidly into a common bibliometric indicator and research metric. A dedicated metadata scheme (following international standards that are enriched with disciplinary controlled community vocabulary) facilitates ease exploration of the various data sets in a TCS catalogue (https://epos-msl.uu.nl/). Concerning analogue modelling, a growing number of data sets includes analogue material physical and mechanical properties and modelling results (raw data and processed products such as images, maps, graphs, animations, etc.) as well as software (for visualization, monitoring and analysis). The main geoscience data repository is currently GFZ Data Services, hosted at GFZ German Research Centre for Geosciences (https://dataservices.gfz-potsdam.de), but others are planned to be implemented within the next years. In the framework of Trans-National access (TNA), TCS Multi-scale laboratories’ facilities are accessible to any researchers, creating new opportunities for synergy, collaboration and scientific innovation, according to TNAtrans-national access rules. TNA can be realized in the form of physical access (on-site experimenting and analysis), remote service (sample analysis) and virtual access (remotely operated processing). After three successful TNA calls, the pandemic has forced a moratorium on the TNA program. The EPOS TCS Multiscale Laboratories framework is also providing the foundation for a comprehensive database of rock analogue materials, a dedicated bibliography, and facilitates the organization of community-wide activities (e.g., meetings, benchmarking) to stimulate collaboration among analogue laboratories and the exchange of know-how. Recent examples of these community efforts are also the contributions to the monthly MSL seminars, available on the MSL YouTube channel (https://www.youtube.com/channel/UCVNQFVql_TwcSBqgt3IR7mQ/featured), as well as the Special Issue on basin inversion in Solid Earth that is currently open for submissions (https://www.solid-earth.net/articles_and_preprints/scheduled_sis.html#1160).

Description: It has been recently demonstrated that Machine Learning (ML) can predict laboratory earthquakes. Here we propose a prediction framework that allows forecasting future surface velocity fields from past ones for analog experiments of megathrust seismic cycles. Using data from two types of experiments, we explore the prediction performances of multiple Deep Learning (DL) and ML algorithms. In such a self-supervised regression, no feature extraction is required and the entire seismic cycle is forecasted. The onset, magnitude, and propagation of analog earthquakes can thus be predicted at different prediction horizons. From all architectures tested in this study, convolutional recurrent neural networks (CNN-LSTM and CONVLSTM) provide the best predictions although their performances depend on experiment characteristics and hyperparameters tuning. Analog earthquakes can be successfully anticipated up to a horizon of the order of their duration. This laboratory-based study may open new avenues for transfer learning applications with data from natural subduction zones.

Description: When analysing GNSS displacement time series with regression approaches, a common simplifying assumption is that annual and semi-annual oscillations, which are mainly controlled by geophysical fluid loading, are perfectly repeating with no variations in phase or amplitude from year to year. In this oversimplified model, sines and cosines with periods 1 year and 0.5 years can be used to represent seasonal oscillations (Fourier terms). Here we show the modification of an existing algorithm, Greedy Automatic Signal Decomposition (GrAtSiD), to now include interannually varying seasonal oscillations. This is achieved by applying a time-varying amplitude weighting on the Fourier terms of the full trajectory model. We demonstrate the algorithm’s performance first with synthetic examples, before applying it to Nevada Geodetic Laboratory’s (NGL’s) daily PPP GNSS displacement time series from South America.In addition to isolation of interannual seasonal oscillations, we demonstrate the effect of applying common-mode filtering (that relies on an initial trajectory model to generate residuals) versus a deep-learning approach that removes higher frequency scatter from the displacement time series before a trajectory model is applied. This supervised deep learning-based method assumes no spatial dependency and is applied on a station-by-station basis. The model has been trained on thousands of freely available PPP daily displacement time series from NGL, augmented by synthetic time series for a more comprehensive training set. This work demonstrates how, by combining advanced time series analysis tools, we are able to better separate tectonic and fluid loading signals in GNSS displacement time series.

Description: Accurate assessment of the rate and state friction parameters of rocks is essential for producing realistic earthquake rupture scenarios and, in turn, for seismic hazard analysis. Those parameters can be directly measured on samples, or indirectly based on inversion of coseismic or postseismic slip evolution. However, both direct and indirect approaches require assumptions that might bias the results. Aiming to reduce the potential sources of bias, we take advantage of a downscaled analog model reproducing megathrust earthquakes. We couple the simulated annealing algorithm with quasi-dynamic numerical models to retrieve rate and state parameters reproducing the recurrence time, rupture duration and slip of the analog model, in the ensemble. Then, we focus on how the asperity size and the neighboring segments' properties control the seismic cycle characteristics and the corresponding variability of rate and state parameters. We identify a tradeoff between (a–b) of the asperity and (a–b) of neighboring creeping segments, with multiple parameter combinations that allow mimicking the analog model behavior. Tuning of rate and state parameters is required to fit laboratory experiments with different asperity lengths. Poorly constrained frictional properties of neighboring segments are responsible for uncertainties of (a–b) of the asperity in the order of per mille. Roughly one order of magnitude larger uncertainties derive from asperity size. Those results provide a glimpse of the variability that rate and state friction estimates might have when used as a constraint to model fault slip behavior in nature.