[DTC-E6] WF7602: ShakeMaps from Parametric Data Version 1
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This workflow generates evolutionary ShakeMaps by combining multiple parametric data sources:
event alerts, automatic and manual peak motions, and crowdsourced felt reports.
It integrates pyFinDer, FinDer, ShakeMap, RRSM, and EMSC to continuously update ground motion maps as new information arrives.
In its full configuration, WF7602 is also capable of:
- Sending event alerts for each ShakeMap update.
Note: In this distribution, the alerting feature is disabled.
The main CWL definition is provided in WF7602.cwl.
Workflow metadata follows the workflow-ro-crate-1.0 profile.
Workflow Structure
The workflow consists of the following datasets (DT) and software services (SS):
-
Data Ingestion
- DT7602: Event alerts from seismic services.
- DT7603: Automated peak motions from RRSM.
- DT7604: Not Implemented: Felt reports from EMSC.
- DT7605: Manual peak motions from ESM.
-
Processing & ShakeMap Generation
- SS7602: Parametric web services and Python wrapper for FinDer (SS7603)
- SS7603: FinDer – finite fault estimation.
- SS7601: Swiss, Italian and European ShakeMap – produces ground motion maps.
-
Output Storage
- DT7606: Evolutionary shake maps calculated at every update.
Inputs
| ID | Name | Description | Type |
|---|---|---|---|
| DT7602 | n/a | Event alerts from seismic services. |
|
| DT7603 | n/a | Automated peak motions from RRSM. |
|
| DT7604 | n/a | Felt reports from EMSC. |
|
| DT7605 | n/a | Manual peak motions from ESM. |
|
| DT7607 | n/a | Dynamic GMMs / site amplification parameters (DTC-E3). |
|
| useMLStep | n/a | Whether to run the optional ML based prediction step (ST760103). |
|
| PretrainedMlModel | n/a | Pretrained machine learning model to be used in ML step. |
|
Steps
| ID | Name | Description |
|---|---|---|
| ST760202 | n/a | Collect event alerts. |
| ST760203 | n/a | Automated peak motions from RRSM. |
| ST760204 | n/a | EMSC felt reports - planned but not implemented |
| ST760205 | n/a | Integrate manual peak motions into the workflow. |
| ST760103 | n/a | ML-based algorithm to estimate peak motions at stations. |
| ST760104 | n/a | Finite-source characterization using template matching (FinDer). |
| ST760105 | n/a | Generate ground motion and shaking intensity maps. |
| ST760106 | n/a | Prepare evolutionary shake maps for output using finite fault characterization. |
Outputs
| ID | Name | Description | Type |
|---|---|---|---|
| DT7606 | n/a | Evolutionary shake maps with finite fault characterization. |
|
Version History
Version 1 (earliest) Created 14th Oct 2025 at 14:23 by Johannes Kemper
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Created: 14th Oct 2025 at 14:23
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https://orcid.org/0000-0003-1841-9590
With present computational capabilities and data volumes entering the Exascale Era, digital twins of the Earth system will be able to mimic the different system components (atmosphere, ocean, land, lithosphere) with unrivaled precision, providing analyses, forecasts, and what if scenarios for natural hazards and resources from their genesis phases and across their temporal and spatial scales. DT-GEO aims at developing a prototype for a digital twin on geophysical extremes including earthquakes, ...
Teams: WP5 - Volcanoes, WP6 - Tsunamis, WP7 - Earthquakes, WP8 - Anthropogenic geophysical extremes
Web page: https://dtgeo.eu/
Provide an integrated, comprehensive, modular modelling and testing framework
Develop multi-scale workflows applicable beyond the identified test-areas enabling improved physical understanding and progress beyond state-of-the-art in the earthquake process.
Develop and implement 6 DTCs covering earthquake-related aspects over long and short time scales
Test the 6 DTC-E at 4 relevant sites: Euro-Med (SD8), Central Apennines and Alto-Tiberina (SD9), Bedretto Lab (SD10) and the Alps (SD11).
Space: A Digital Twin for GEOphysical extremes (DT-GEO)
Public web page: https://dtgeo.eu/
