Fully Data-Driven Model for Increasing Sampling Rate Frequency of Seismic Data using Super-Resolution Generative Adversarial Networks

High-quality data is one of the key requirements for any engineering application. In earthquake engineering practice, accurate data is pivotal in predicting the response of structure or damage detection process in an Structural Health Monitoring (SHM) application with less uncertainty. However, obtaining high-resolution data is fraught with challenges, such as significant costs, extensive data channels, and substantial storage requirements. To address these challenges, this study employs super-resolution generative adversarial networks (SRGANs) to improve the resolution of time-history data such as …

adversarial application cs.lg data data-driven detection earthquake eess.sp engineering generative generative adversarial networks health key monitoring networks physics.geo-ph pivotal practice process quality quality data rate requirements sampling the key uncertainty