The Application of Semisupervised Attentional Generative Adversarial Networks in Desert Seismic Data Denoising
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DOI: 10.1109/lgrs.2021.3073419
Abstract: For imaging and interpretation, high-quality seismic data are necessary. However, noise, which is strong in field desert seismic data, inevitably diminishes the quality of the data and reduces the signal-to-noise ratio. Moreover, the effective signals… read more here.
Keywords: seismic data; attentional generative; generative adversarial; application semisupervised ... See more keywords