The EU Center of Excellence for Exascale in Solid Earth (ChEESE): Implementation, results, and roadmap for the second phase

Folch, Arnau; Abril, Claudia; Afanasiev, Michael; Amati, Giorgio; Bäder, Michael; Badía, Rosa M.; Bayraktar, Hafize Başak; Barsotti, Sara; Basili, Roberto; Bernardi, Fabrizio; Boehm, Christian; Brizuela, Beatriz; Brogi, Federico; Cabrera, Eduardo; Casarotti, Emanuele; Castro, Manuel J.; Cerminara, Matteo; Cirella, A.; Cheptsov, Alexey; Conejero, Javier; Costa, Antonio; Asunción, Marc de la; Puente, Josep de la; Djuric, Marco; Dorozhinskii, Ravil; Espinosa, G.; Ongaro, Tomaso Esposti; Farnós, J.; Favretto-Cristini, Nathalie; Fichtner, Andreas; Fournier, Alexandre; Gabriel, Alice-Agnes; Gallard, Jean-Matthieu; Gibbons, Steven J.; Glimsdal, Sylfest; González‐Vida, J. M.; Gracia, José Luis Pano; Gregorio, Rose; Gutierrez, N.; Halldórsson, Benedikt; Hamitou, Okba; Houzeaux, Guillaume; Jaure, Stephan; Kessar, Mouloud; Krenz, Lukas; Krischer, Lion; Laforet, Soline; Lanucara, Piero; Li, Bo; Lorenzino, Maria Concetta; Lorito, Stefano; Løvholt, Finn; Macedonio, Giovanni; Macı́as, Jorge; Marín, Guillermo; Montesinos, Beatriz Martínez; Mingari, Leonardo; Moguilny, Geneviève; Montellier, Vadim; Monterrubio-Velasco, Marisol; Moulard, Georges Emmanuel; Nagaso, Masaru; Nazaria, Massimo; Niethammer, Christoph; Pardini, Federica; Pienkowska, Marta; Pizzimenti, Luca; Poiata, Natalia; Rannabauer, Leonhard; Rojas, Otilio; Rodriguez, J. E.; Romano, Fabrizio; Rudyy, Oleksandr; Ruggiero, Vittorio; Samfass, Philipp; Sánchez-Linares, Carlos; Sanchez, Sabrina; Sandri, Laura; Scala, Antonio; Schaeffer, Nathanaël; Schuchart, Joseph; Selva, Jacopo; Sergeant, Amadine; Stallone, Angela; Taroni, Matteo; Thrastarson, Soelvi; Titos, Manuel; Tonelllo, Nadia; Tonini, Roberto; Ulrich, Thomas; Vilotte, Jean‐Pierre; Vöge, Malte; Volpe, Manuela; Wirp, Sara Aniko; Wössner, Uwe

doi:10.1016/j.future.2023.04.006

Cited by 13 publications

(4 citation statements)

References 42 publications

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“…Looking ahead, one can anticipate the development of more advanced methods for handling the uncertainties in seismic hazard datasets, components and models. These methods are likely to involve the use of physicsbased simulations of both earthquake ruptures and/or ground shaking (Bradley, 2019;Paolucci et al, 2021;Li et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

“…The integration of physics-based simulations into seismic hazard modelling has the potential to improve the main pool of existing records, enhance ground shaking characterization particularly in the magnitude-distance range poorly covered in current strong-motion databases, improve the ground motion characteristic models, augment our understanding of earthquake scenarios, and support earthquake preparedness and mitigation strategies (Graves et al, 2011). In addition to using physicsbased simulations, one can also anticipate significant progress in computational capabilities, such as high-performance computing (Folch et al, 2023), and artificial intelligence (AI)-driven analytics in the coming years. These leading-edge tools have the potential to significantly accelerate the processing of extensive, multidisciplinary datasets and complex calculations (Dal Zilio et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

The 2020 European Seismic Hazard Model: Overview and Results

Danciu,

Giardini,

Weatherill

et al. 2024

Preprint

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Abstract. The 2020 update of the European Seismic Hazard Model (ESHM20) is the most recent and up-to-date assessment of seismic hazard for the Euro-Mediterranean region. The new model, publicly released in May 2022, incorporates refined and cross-border harmonised earthquake catalogues, homogeneous tectonic zonation, updated active faults datasets and geological information, complex subduction sources, updated area source models, a smoothed seismicity model with an adaptive kernel optimised within each tectonic region and a novel ground motion characteristic model. ESHM20 supersedes the 2013 European Seismic Hazard Model (ESHM13, Wössner et al 2015) and provides full sets of hazard outputs such as hazard curves, maps, and uniform hazard spectra for the Euro-Mediterranean region. The model provides two informative hazard maps that will serve as a reference for the forthcoming revision of the European Seismic Design Code (CEN EC8) and provides input to the first earthquake risk model for Europe (Crowley et al., 2021). ESHM20 will continue to evolve and act as a key resource for supporting earthquake preparedness and resilience throughout Euro-Mediterranean region under the umbrella of the European Facilities for Seismic Hazard and Risk Consortium (EFEHR Consortium).

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The 2020 European Seismic Hazard Model: Overview and Results

Danciu,

Giardini,

Weatherill

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…Two main challenges are (i) building interdisciplinary groups and ensuring effective interactions between the disciplinary experts, and (ii) engaging with civil society (a structured and sometimes lengthy process) by building trust between scientists and stakeholders (UNDRR, 2022b). Research infrastructures can foster the development of a transdisciplinary research community in the field of disaster risk (Peek et al, 2020) and provide powerful tools (e.g., data, codes, expertise) to research groups (e.g., Folch et al, 2023;Calatrava et al, 2023;Dañobeitia et al, 2020). Access and interaction with research infrastructures should therefore be promoted and encouraged among the disaster risk community to exploit these opportunities.…”

Section: Transdisciplinaritymentioning

confidence: 99%

The need for open, transdisciplinary, and ethical science in seismology

Dallo,

Herrmann,

Supino

et al. 2023

Seismica

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Reducing the seismic risk for societies requires a bridge between scientific knowledge and societal actions. In recent years, three subjects that facilitate this connection gained growing importance: open science, transdisciplinarity, and ethics. We outline their relevance in general and specifically at the example of `'dynamic seismic risk' as explored in a dedicated workshop. We argue that these reflections can be transferred to other research fields for improving their practical and societal relevance. We provide recommendations for scientists at all levels to make science more open, transdisciplinary, and ethical. Only with a transition can we, as scientists, address current societal challenges and increase societies' resilience to disasters.

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“…Approximations of results using methods like Green's Function, amplification factors, and reduced complexity models (Molinari et al, 2016;Løvholt et al, 2016;Glimsdal et al, 2019;Gailler et al, 2018;Grzan et al, 2021;Röbke et al, 2021) 3. Hardware and computational improvements like a nesting of grid domains, adaptive variable grid resolution, parallelisation and GPU-based acceleration, coupled multi-scale modelling, exascale codes (LeVeque et al, 2011;Shi et al, 2012;Oishi et al, 2015;Macías et al, 2017;Marras and Mandli, 2021;Folch et al, 2023) 4. Surrogates using statistical emulators and machine learning models (Sarri et al, 2012;Salmanidou et al, 2021;Mulia et al, 2018;Fauzi and Mizutani, 2019;Makinoshima et al, 2021;Fukutani et al, 2023;Mulia et al, 2022) Running the numerical simulations, especially for modelling the tsunami inundation with an NLSWE model takes a lot of time and computational resources, for example accounting for about 13,600 GPU hours in the local PTHA study for Catanai by Gibbons et al (2020), refer Table S5 for runtimes of this study.…”

mentioning

confidence: 99%

Advancing nearshore and onshore tsunami hazard approximation with machine learning surrogates

Ragu Ramalingam,

Johnson,

Pagani

et al. 2024

Preprint

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Abstract. Probabilistic tsunami hazard and risk assessment (PTHA and PTRA) are vital methodologies for computing tsunami risk and prompt measures to mitigate impacts. At large regional scales, their use and scope are currently limited by the computational costs of numerically intensive simulations behind them, which may be feasible only with advanced computational resources like high-performance computing (HPC) and may still require reductions in resolution, number of scenarios modelled, or use of simpler approximation schemes. To conduct PTHA and PTRA for large proportions of the coast, we therefore need to develop concepts and algorithms for reducing the number of events simulated and for more efficiently approximating the needed simulation results. This case study for a coastal region of Tohoku, Japan, utilises a limited number of tsunami simulations from submarine earthquakes along the subduction interface to build a wave propagation and inundation database and fits these simulation results through a machine learning-based variational encoder-decoder model. This is used as a surrogate to predict the tsunami waveform at the coast and the maximum inundation depths onshore at the different test sites. The performance of the surrogate models was assessed using a 5-fold cross validation assessment across the simulation events. Further to understand its real world performance and test the generalisability of the model, we used 5 very different tsunami source models from literature for historic events to further benchmark the model and understand its current deficiencies.

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The EU Center of Excellence for Exascale in Solid Earth (ChEESE): Implementation, results, and roadmap for the second phase

Cited by 13 publications

References 42 publications

The 2020 European Seismic Hazard Model: Overview and Results

The 2020 European Seismic Hazard Model: Overview and Results

The need for open, transdisciplinary, and ethical science in seismology

Advancing nearshore and onshore tsunami hazard approximation with machine learning surrogates

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