High-Performance Deep Learning Models for Seismic Noise Detection and Quality Control in the Processing Workflow

Autor:	P. Thomas, A. Shen, T. Gong, A. Kudarova, J. Eldridge, Christian Sutton, Pandu R. Devarakota, J. Liu, P. Webster
Rok vydání:	2021
Předmět:	Artificial neural network Computer science business.industry Deep learning Supervised learning Seismic noise computer.software_genre Workflow Interference (communication) Component (UML) Key (cryptography) Data mining Artificial intelligence business computer
Zdroj:	82nd EAGE Annual Conference & Exhibition.
DOI:	10.3997/2214-4609.202112414
Popis:	Summary In this paper, we demonstrate deep neural network models’ ability to recognize noises with complex patterns in seismic images with high accuracy and generalizability. We designed a creative labeling strategy generating many high-quality labels for the supervised learning component. We built three deep learning models, predicting key quality metrics for the noise attenuation workflow in seismic processing projects: including a swell noise level model, a Seismic Interference (SI) noise level model, and a signal leakage model. These models have been successfully deployed to Shell exploration projects in the Gulf of Mexico.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b0622848543c7942caaa5ecb38f3e5d2 https://doi.org/10.3997/2214-4609.202112414 Zobrazit plný text záznamu