Bagher Farmani | Convolutional Neural Networks
11 December | Session: Noise Attenuation Methods | 15:00 - 16:00 (CET), 6th speaker. Application of convolutional neural network in automated hydrophone swell noise attenuation
Authors: Bagher Farmani, Morten Pedersen, PGS. Click on the link below for the abstract.
Bagher Farmani presents a Deep Learning-based method for the detection of residual swell noise and signal leakage in seismic shot gathers. He shows how this classification of noise and signal can be used together with traditional noise removal algorithms to automate a target-oriented swell noise removal. Real data examples show how the automated and target-oriented noise removal improves the data quality of not only seismic shot gathers, but also on the resulting stacks.