  Vibration sensors are essential in acquiring seismic activity for an accurate
earthquake assessment. The state-of-the-art sensors can provide the best signal
quality and the highest bandwidth; however, their high cost usually hinders a
wide range of applicability and coverage, which is otherwise possible with
their basic and cheap counterparts. But, their poor quality and low bandwidth
can significantly degrade the signal fidelity and result in an imprecise
analysis. To address these drawbacks, in this study, we propose a novel,
high-quality, and full bandwidth seismic signal synthesis by transforming the
signal acquired from an inferior sensor. We employ 1D Operational Generative
Adversarial Networks (Op-GANs) with novel loss functions to achieve this.
Therefore, the study's key contributions include releasing a new dataset,
addressing operational constraints in seismic monitoring, and pioneering a
deep-learning transformation technique to create the first virtual seismic
sensor. The proposed method is extensively evaluated over the Simulated Ground
Motion (SimGM) benchmark dataset, and the results demonstrated that the
proposed approach significantly improves the quality and bandwidth of seismic
signals acquired from a variety of sensors, including a cheap seismic sensor,
the CSN-Phidgets, and the integrated accelerometers of an Android, and iOS
phone, to the same level as the state-of-the-art sensor (e.g.,
Kinemetrics-Episensor). The SimGM dataset, our results, and the optimized
PyTorch implementation of the proposed approach are publicly shared.
