Seismic risk assessment of the Lake Baikal railway infrastructure based on Unified Scaling Law for Earthquakes and anisotropic seismic impact
- 1Institute of Earthquake Prediction Theory and Mathematical Geophysics, Russian Acad. Sc. (IEPT RAS), Moscow, Russia
- 2Schmidt Institute of Physics of the Earth, Russian Acad. Sc. (IPE RAS), Moscow, Russia
- 3International Seismic Safety Organisation (ISSO), Arsita, Italy
- 4MIREA – Russian Technological University (RTU MIREA), Moscow, Russia
Seismic hazard assessment (SHA) and associated risks (SRs) require necessarily an adequate understanding of earthquake distribution in magnitude, space, and time at regional scale. The Neo-Deterministic Seismic Hazard Assessment (NDSHA) is the innovative multi-disciplinary scenario-physics-based approach for reliable evaluation of seismic hazard and risks, which have been developed to overcome evident shortcomings of the outdated and very often wrong Probabilistic Seismic Hazard Analysis (PSHA). The NDSHA applications in many countries worldwide (Panza et al., 2021) pass intensive testing by instrumental and historical evidence, as well as by realistic modelling of scenario earthquakes. NDSHA results confirm reliable and effective input for mitigating object-oriented SRs. We applied two agents of the NDSHA synergy, i.e. Unified Scaling Law for Earthquakes (USLE) and anisotropic propagation of seismic effect, to evaluate SRs for the railway infrastructure in the Lake Baikal region.
USLE states that the logarithm of expected annual number of earthquakes of magnitude M or larger in an area of linear dimension L follows within the magnitude range [M– , M+] the relationship log N(M, L) = A + B×(5 − M) + C×log L, where A, B and C are constants. Naturally, A and B are analogous to the a- and b-values of the classical Gutenberg-Richter relationship (G-RR), while C compliments to G-RR with an estimate of local fractal dimension of earthquake epicentres allowing for realistic rescaling seismic hazard to the size of exposure at risk. USLE implies that the maximum magnitude MX expected with p% chance in T years can be obtained from N(MX, L) = p%, then used for estimating ground shaking effect.
We used as essentials (i) macroseismic intensity scale that provides a robust estimate for realistic modelling of maximal potential ground shaking in assessment of regional seismic hazard and associated risks and (ii) anisotropic propagation of seismic effect that is evidently following, in most cases of large earthquakes, dominant direction of active faults nearby epicentre and apply these to the earthquake catalogue compiled at the Baikal Division of the Geophysical Survey, Federal Research Centre of the Russian Academy of Sciences (http://www.seis-bykl.ru/), Active Faults of Eurasia Database (http://neotec.ginras.ru/database.html) and data on railroads from the OpenStreetMap project (https://www.openstreetmap.org).
We present the SRs for railway lines, hubs and tunnels in the Lake Baikal region based on the maps of maximum macroseismic intensity expected in a period of 50 years with a probability of 10%, 5% and 1% (Nekrasova&Kossobokov, 2022) and different model vulnerability functions attributed to the exposed infrastructure elements of different kind.
The study is carried on in the framework of the Russian State Task of Scientific Research Works of IEPT RAS and IPE RAS.
References
Nekrasova A, Kossobokov V (2022) Seismic risk assessment for the infrastructure in the regions adjacent to the Russian Federation Baikal–Amur Mainline based on the Unified Scaling Law for Earthquakes. Natural Hazards, https://doi.org/10.1007/s11069-022-05750-9
Panza G, Kossobokov V, De Vivo B, Laor E (Eds) (2021) Earthquakes and Sustainable Infrastructure: neo-deterministic (NDSHA) approach guarantees prevention rather than cure. Elsevier, xxv, 672 p. https://doi.org/10.1016/C2020-0-00052-6
How to cite: Nekrasova, A., Kossobokov, V., and Podolskaia, E.: Seismic risk assessment of the Lake Baikal railway infrastructure based on Unified Scaling Law for Earthquakes and anisotropic seismic impact, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-1710, https://doi.org/10.5194/egusphere-egu23-1710, 2023.