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

Abstract: 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 the data obtained by a sensor network in an SHM application, marking the first application of SRGANs in earthquake engineering domain. The time-series data are transformed into RGB values, converting raw data into images. SRGANs are then utilized to upscale these low-resolution images, thereby enhancing the overall sensor resolution. This methodology not only offers potential reductions in data storage requirements but also simplifies the sensor network, which could result in lower installation and maintenance costs. The proposed SRGAN method is rigorously evaluated using real seismic data, and its performance is compared with traditional enhancement techniques. The findings of this study pave the way for cost-effective and efficient improvements in the resolution of sensors used in SHM systems, with promising implications for the safety and sustainability of infrastructures worldwide.