Self-Supervised Ground-Roll Noise Attenuation Using Self-Labeling and Paired Data Synthesis

Within seismology, geology, civil and structural engineering, deep learning (DL), especially via generative adversarial networks (GANs), represents an innovative, engaging, and advantageous way to generate reliable synthetic data that represent actual samples’ characteristics, providing a handy data augmentation tool. Indeed, in many practical applications, obtaining a significant number of high-quality information is demanding. Data augmentation is generally based on artificial intelligence (AI) and machine learning data-driven models. The DL GAN-based data augmentation approach for generating synthetic seismic signals revolutionized the current data augmentation paradigm. This study delivers a critical state-of-art review, explaining recent research into AI-based GAN synthetic generation of ground motion signals or seismic events, and also with a comprehensive insight into seismic-related geophysical studies. This study may be relevant, especially for the earth and planetary science, geology and seismology, oil and gas exploration, and on the other hand for assessing the seismic response of buildings and infrastructures, seismic detection tasks, and general structural and civil engineering applications. Furthermore, highlighting the strengths and limitations of the current studies on adversarial learning applied to seismology may help to guide research efforts in the next future toward the most promising directions.

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Generative adversarial networks review in earthquake-related engineering fields

Marano

Rosso

Aloisio

et al. 2023

Bull Earthquake Eng

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Deep Learning with Fully Convolutional and Dense Connection Framework for Ground Roll Attenuation

et al. 2023

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Random Noise Attenuation by Self-supervised Learning from Single Seismic Data

Wang

Sui²,

Wang

et al. 2022

Math Geosci

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Self-Supervised Ground-Roll Noise Attenuation Using Self-Labeling and Paired Data Synthesis

Cited by 19 publications

References 20 publications

Generative adversarial networks review in earthquake-related engineering fields

Generative adversarial networks review in earthquake-related engineering fields

Deep Learning with Fully Convolutional and Dense Connection Framework for Ground Roll Attenuation

Random Noise Attenuation by Self-supervised Learning from Single Seismic Data

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