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

Yang, Liuqing; Wang, Shoudong; Chen, Xiaohong; Saad, Omar M.; Cheng, Wanli; Chen, Yangkang

doi:10.1007/s10712-023-09779-8

Cited by 6 publications

(1 citation statement)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wu et al improve seismic structural interpretation by training CNNs on synthetic seismic images generated from diverse, realistic 3D structural models [15]. Yang et al introduce GRDNet, a fully convolutional framework featuring dense connections, designed for the efficient attenuation of ground roll noise in land seismic data, significantly improving seismic data quality [16]. However, DL's powerful learning ability lies in its large number of optimization parameters, which leads to the need for sufficient high-quality training sets.…”

Section: Introductionmentioning

confidence: 99%

Seismic Resolution Enhancement Using a Cycle Generative Adversarial Neural Network with Pseudo-Well Data

Zhao,

Gao,

Guo

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

High-resolution seismic processing involves the recovery of high-frequency components from seismic data with lower resolution. Traditional methods typically impose prior knowledge or predefined subsurface structures when modeling seismic high-resolution processes, and they are usually model-driven. Nowadays, there has been a growing utilization of deep learning techniques to enhance seismic resolution. These approaches involve feature learning from extensive training datasets through multi-layered neural networks and are fundamentally data-driven. However, the reliance on labeled data has consistently posed a primary challenge for deploying these methods in practical applications. To address this issue, a novel approach for seismic high-resolution reconstruction is introduced, employing a Cycle Generative Adversarial Neural Network (CycleGAN) trained on authentic pseudo-well data. The application of the CycleGAN involves creating dual mappings connecting low-resolution and high-resolution data. This enables the model to comprehend both the forward and inverse processes, ensuring the stability of the inverse process, particularly in the context of high-resolution reconstruction. More importantly, statistical distributions are extracted from well logs and used to randomly generate extensive sets of low-resolution and high-resolution training pairs. This training set captures the structural characteristics of the actual subsurface and leads to significant improvement of the proposed method. The results from experiments conducted on both synthetic and field examples validate the effectiveness of the proposed approach in significantly enhancing seismic resolution and achieving superior recovery of thin layers when compared with the conventional method and the deep-learning-based method.

show abstract

Section: Introductionmentioning

confidence: 99%