High-Quality and Full Bandwidth Seismic Signal Synthesis using Operational GANs

TL;DR

This paper introduces a novel deep learning method using Operational GANs to transform low-quality seismic signals into high-quality, full bandwidth signals, enabling cost-effective seismic monitoring with improved accuracy.

Contribution

The study presents the first deep-learning transformation technique for seismic signals, introduces a new dataset, and demonstrates the effectiveness of Op-GANs in enhancing seismic data quality.

Findings

Significant improvement in seismic signal quality and bandwidth.

Effective transformation of signals from low-cost sensors to high-quality levels.

Open-source dataset and implementation provided.

Abstract

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.