Easier access to affordable and powerful hardware (CPU and GPU) solutions together with user-friendly open-source software such as TensorFlow, has been the key to the accelerated use of machine learning within various areas of technology. In seismic data processing, artificial neural networks (ANNs) have the potential to be applied to many of the key processing steps (deblending, seismic interference attenuation, deghosting, etc.) which today involve significant testing time and computational power. Once trained, ANNs are computationally very light and potentially adaptable to different datasets. Sorting the data from common source domain to another by using mathematical transformation such as Wavelet transform or Shearlet transform may make the target signals and noise have more differences in characteristics. Their use could, therefore, save processing time and, in the long term, impact the whole business sector. The proposed doctoral work is about the usage of ANNs and transformations 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, deep learning-based methods can save significant computer resources and time.
Project leader: Hege Nielsen
Institution: CGG SERVICES (NORWAY) AS