Real-world application of deep learning in large-scale seismic interference attenuation: A case study in the Camie field of Angola

Key points

• In marine seismic acquisition, seismic interference (SI) occurs when energy from nearby external seismic source(s) is captured.1
• SI is commonly observed and poses a challenge for seismic data processing.1
• A key highlight of this case history is its scale: this represents a real-world, large-scale processing project and we present a comprehensive comparison of the DNN-based workflow with the conventional geophysical algorithm across the entire survey block, focusing on both processing quality and processing time.1
• The promising results of this application also open up possibilities for integrating deep learning into other seismic denoising tasks.1

Sources (1)

• [1]Real-world application of deep learning in large-scale seismic interference attenuation: A case study in the Camie field of Angola
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 08:30 AM

  We present a case history of a previously proposed deep neural network (DNN)-based workflow applied for SI attenuation across a marine seismic block in the Camie Field of Angola.

  In marine seismic acquisition, seismic interference (SI) occurs when energy from nearby external seismic source(s) is captured.

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