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Open AccessLetter

Seismic Data Augmentation Based on Conditional Generative Adversarial Networks

1
Department of Video Information Processing, Korea University, Seoul 136713, Korea
2
School of Electrical Engineering, Korea University, Seoul 02841, Korea
3
Korea Meteorological Administration, Seoul 07062, Korea
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(23), 6850; https://doi.org/10.3390/s20236850
Received: 12 October 2020 / Revised: 23 November 2020 / Accepted: 28 November 2020 / Published: 30 November 2020
(This article belongs to the Special Issue Sensor Fusion for Object Detection, Classification and Tracking)
Realistic synthetic data can be useful for data augmentation when training deep learning models to improve seismological detection and classification performance. In recent years, various deep learning techniques have been successfully applied in modern seismology. Due to the performance of deep learning depends on a sufficient volume of data, the data augmentation technique as a data-space solution is widely utilized. In this paper, we propose a Generative Adversarial Networks (GANs) based model that uses conditional knowledge to generate high-quality seismic waveforms. Unlike the existing method of generating samples directly from noise, the proposed method generates synthetic samples based on the statistical characteristics of real seismic waveforms in embedding space. Moreover, a content loss is added to relate high-level features extracted by a pre-trained model to the objective function to enhance the quality of the synthetic data. The classification accuracy is increased from 96.84% to 97.92% after mixing a certain amount of synthetic seismic waveforms, and results of the quality of seismic characteristics derived from the representative experiment show that the proposed model provides an effective structure for generating high-quality synthetic seismic waveforms. Thus, the proposed model is experimentally validated as a promising approach to realistic high-quality seismic waveform data augmentation. View Full-Text
Keywords: generative adversarial networks; data augmentation; seismic waveforms generative adversarial networks; data augmentation; seismic waveforms
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MDPI and ACS Style

Li, Y.; Ku, B.; Zhang, S.; Ahn, J.-K.; Ko, H. Seismic Data Augmentation Based on Conditional Generative Adversarial Networks. Sensors 2020, 20, 6850.

AMA Style

Li Y, Ku B, Zhang S, Ahn J-K, Ko H. Seismic Data Augmentation Based on Conditional Generative Adversarial Networks. Sensors. 2020; 20(23):6850.

Chicago/Turabian Style

Li, Yuanming; Ku, Bonhwa; Zhang, Shou; Ahn, Jae-Kwang; Ko, Hanseok. 2020. "Seismic Data Augmentation Based on Conditional Generative Adversarial Networks" Sensors 20, no. 23: 6850.

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