Real-world application of deep learning in large-scale seismic interference attenuation: A case study in the Camie field of Angola
In marine seismic acquisition, seismic interference (SI) occurs when energy from nearby external seismic source(s) is captured. It typically appears as coherent noise with linear or non-linear movement and varying amplitudes across different sail lines. SI is commonly observed and poses a challenge for seismic data processing. 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. This field survey covers over 345 km2 and is marked by the challenge of multiple SI types. The employed DNN-based workflow performs SI attenuation in the common shot domain based on a super...
| Authors | Jing Sun, Song Hou |
|---|---|
| Primary category | physics.geo-ph |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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