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Machine learning and artificial neural networks in seismic processing


Description:

The recent availability of powerful GPUs and open source software have enabled artificial neural networks (ANNs) to be applied to several practical and industrial scale problems. In seismic data processing, artificial neural networks (ANNs) have the potential to be applied to many of the key processing steps (swell noise attenuation, seismic interference attenuation, deblending, deghosting, etc.) which today involve significant testing time and computational power. Once trained, ANNs are computationally very light and potentially adaptable to different datasets. Their use could, therefore, save processing times and, in the long term, impact the whole business sector. The proposed doctoral work is about the usage of ANNs for processing of marine seismic data. The goal is to achieve similar or better quality results compared to conventional processing methods. If this is achieved, ANNs can save significant computer resources and time.


Project leader: Hege Nielsen

Started: 2019

Ends: 2022

Category: Næringsliv

Sector: Næringsliv

Budget: 1690000

Institution: CGG SERVICES (NORWAY) AS

Address: Oslo