Summary of High-quality and Full Bandwidth Seismic Signal Synthesis Using Operational Gans, by Ozer Can Devecioglu et al.
High-Quality and Full Bandwidth Seismic Signal Synthesis using Operational GANs
by Ozer Can Devecioglu, Serkan Kiranyaz, Zafer Yilmaz, Onur Avci, Moncef Gabbouj, Ertugrul Taciroglu
First submitted to arxiv on: 6 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel seismic signal synthesis method that transforms signals acquired from inferior sensors to achieve high-quality and full-bandwidth signals comparable to those from state-of-the-art sensors. The authors employ 1D Operational Generative Adversarial Networks (Op-GANs) with novel loss functions to synthesize the signals. The proposed approach is evaluated on the Simulated Ground Motion (SimGM) benchmark dataset, showing significant improvements in signal quality and bandwidth for various sensors, including a cheap seismic sensor and integrated accelerometers of Android and iOS phones. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps address limitations in earthquake assessment by proposing a new way to transform low-quality signals into high-quality ones. The authors use deep learning techniques to create a virtual seismic sensor that can provide accurate results without the need for expensive equipment. This is important because currently, high-end sensors are not affordable for many users. |
Keywords
* Artificial intelligence * Deep learning
