The goal of the section is to achieve progress beyond the state-of-the-art, in all steps leading to an improved assessment of probabilistic seismic hazards.
We analyze the stability of sub-surface fracture systems for energy technologies and carry out field experiments on fracture propagation and fault activation (Contact Arno Zang).
We develop 4D stress model workflows for inter- and intraplate earthquake cycles and radioactive waste repositories and manage the World Stress Map project (Contact Oliver Heidbach).
We develop site-specific, physics-based, and data-driven ground-shaking models for the improvement of seismic building codes (Contact Dino Bindi).
We compute seismic hazard assessments based on a consistent probabilistic treatment of all input data and the transparent evaluation of epistemic uncertainties (Contact Graeme Weatherill).
We develop methods to test the components and results of seismic hazard and risk models (ContactDanijel Schorlemmer)
Yen MH, von Specht S, Lin YY, Cotton F, Ma KF (2021) Within‐ and Between‐Event Variabilities of Strong‐Velocity Pulses of Moderate Earthquakes within Dense Seismic Arrays. Bulletin of the Seismological Society of America: online first. doi.org/10.1785/0120200376
Loviknes K, Kotha SR, Cotton F, Schorlemmer D (2021) Testing Nonlinear Amplification Factors of Ground‐Motion Models. Bulletin of the Seismological Society of America 111(4): 2121-2137. https://doi.org/10.1785/0120200386
Dabbeek J, Crowley H, Silva V, Weatherill G, Paul N, Nievas CI (2021) Impact of exposure spatial resolution on seismic loss estimates in regional portfolios. Bulletin of Earthquake Engineering: online first. https://doi.org/10.1007/s10518-021-01194-x
Bindi D, Razafindrakoto HNT, Picozzi M, Oth A (2021) Stress Drop Derived from Spectral Analysis Considering the Hypocentral Depth in the Attenuation Model: Application to the Ridgecrest Region, California. Bulletin of the Seismological Society of America: online first. https://doi.org/10.1785/0120210039
Zaccarelli R, Bindi D, Strollo A (2021) Anomaly Detection in Seismic Data–Metadata Using Simple Machine‐Learning Models. Seismological Research Letters 92(4): 2627-2639. https://doi.org/10.1785/0220200339