Fully Data-Driven Model for Increasing Sampling Rate Frequency of
  Seismic Data using Super-Resolution Generative Adversarial Networks
Navid Gholizadeh Javad Katebi
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.