An Operational Earthquake Digital Twin Based on Empirical Ground-Motion Models and Period Estimation: Integration of SEISAID and B-Wave within the VIGIRISKS Platform

Digital Twins (DTs) are increasingly used as integrative frameworks to combine data streams, numerical models and automated workflows for monitoring complex systems and supporting decision-making. In the field of seismic risk management, operational DTs must rely on fast, robust and reproducible modelling approaches, capable of assimilating real-time observations despite strong epistemic uncertainty. This contribution presents an operational earthquake DT implemented on the VIGIRISKS platform, and illustrated through two complementary rapid-response tools: SEISAid, dedicated to territorial-scale impact assessment, and B-Wave, focused on near real-time structural damage monitoring.

Rather than relying on detailed physics-based representations of subsurface processes, the proposed DT is built upon empirical ground-motion models and vulnerability models, which can be considered as meta-models linking observed seismic signals to expected ground motion and damage. Real-time seismic data from regional and national monitoring networks are continuously ingested through Pulsar approach. Seismic intensity fields are generated using the USGS ShakeMap framework, which embeds data weighting and uncertainty propagation to combine ground-motion prediction equations, instrumental recordings, macroseimic observations, and site-effect information. These ShakeMap products are then encapsulated within the VIGIRISKS infrastructure, where they trigger automated impact assessment workflows.

At the territorial scale, SEISAid exploits ShakeMap outputs and empirically calibrated vulnerability models to estimate building damage and potential human losses within 15–30 minutes after earthquake detection. Calculations are performed using reproducible scientific codes hosted on VIGIRISKS, and results are automatically aggregated and disseminated to decision-makers through standardized notification reports. This workflow supports rapid situational awareness and early operational decision-making under uncertainty.

At the structural scale, B-Wave extends the DT by integrating recorded dynamic responses from instrumented buildings. Damage assessment relies on data-driven signal processing methods, such as continuous wavelet transform–based frequency identification, to detect changes in structural dynamic properties. These changes are empirically related to damage states aligned with European EMS-98 classes, enabling near real-time alerts on the condition of critical structures without requiring detailed mechanical models.

A key characteristic of the framework is its event-driven and iterative cycle: each new earthquake updates data, models and outputs, progressively enriching the DT. By embedding empirical modelling, uncertainty handling and updating (via ShakeMap), and automated decision support within a unified infrastructure, this work illustrates how DT concepts can be operationally implemented for natural risk applications, contributing methodological insights relevant to subsurface-related DT workflows focused on data integration and decision support. Although this contribution focuses on the event-driven DT cycle triggered by real earthquakes, the proposed framework also enables “what-if scenario” based impact assessments, illustrating the flexibility of the DT for both operational response and prospective risk analysis.