Neural Network-Based Subduction Ground Motion Model and Its Application to New Zealand and the Andaman and Nicobar Islands
Sreenath Vemula, K P Sreejaya, S. T. G. Raghukanth
Abstract
A deep learning model is developed for the Next Generation Attenuation – Subduction database for predicting spectral accelerations and peak amplitude measures. The developed model satisfies the statistical criteria necessary for prediction. Standard deviations lie in 0.2864–0.3809, 0–0.2696, and 0.4514–0.7892, range for inter-event, -region, and intra-events, respectively. Transfer learning is applied to the New Zealand region. Probabilistic seismic hazard analysis is performed for the Andaman-Nicobar region and obtained a peak ground acceleration of 0.6–0.7 g and 0.4–0.5 g at the Andaman and the Nicobar Islands, respectively, for a 2475-year return period.
Topics & Concepts
SubductionSeismologyGeologyGround motionAmplitudeAttenuationRange (aeronautics)Probabilistic logicPeak ground accelerationHazardAccelerationSpectral accelerationTectonicsComputer scienceArtificial intelligencePhysicsEngineeringAerospace engineeringOpticsQuantum mechanicsOrganic chemistryChemistryClassical mechanicsSeismic Waves and AnalysisSeismic Performance and Analysisearthquake and tectonic studies