Seismic data processing using DUnet

A seismic data processing apparatus for exploring an underground formation, the apparatus comprising:

• an interface configured to obtain seismic data acquired over an underground formation;
• a data processing unit configured to generate a denoised image of the underground formation by reconstructing seismic signals from a noisy input domain of the seismic data using a DUnet engine,
• wherein the DUnet engine reconstructs the seismic signals via:
• a contractive path that performs multilayer convolutions and contraction to extract a code and contractive features from the seismic data input to the DUnet engine,
• an expansive path configured to perform multilayer convolutions and expansion of the code to produce expansive features, using the contractive features provided by the contractive path through skip connections,
• a model level that performs multilayer convolutions on a highest level of the contractive features and a highest level of the expansive features, the highest level of the contractive features and the highest level of the expansive features having a size as the seismic data input, and outputs of the multilayer convolutions are averaged to generate a model output;
• and
• a reweighting layer comprising a locally connected layer that receives the model output and applies at least one of spatial and temporarily smooth coefficients and additional multilayer convolutions to preserve a signal amplitude trend of the seismic data to generate the denoised image.

A non-transitory computer readable recording medium storing executable codes that when executed by a computer make the computer perform a seismic exploration method for denoising and/or deghosting seismic data, the method comprising:

• obtain seismic data acquired over an underground formation;
• and
• generate a denoised image of the underground formation by reconstructing seismic signals from a noisy input domain of the seismic data using a DUnet engine,
• wherein the DUnet engine reconstructs the seismic signals via:
• a contractive path that performs multilayer convolutions and contraction to extract a code and contractive features from the seismic data input to the DUnet engine,
• an expansive path configured to perform multilayer convolutions and expansion of the code to produce expansive features, using the contractive features provided by the contractive path through skip connections,
• a model level that performs multilayer convolutions on a highest level of the contractive features and a highest level of the expansive features, the highest level of the contractive features and the highest level of the expansive features having a size as the seismic data input, and outputs of the multilayer convolutions are averaged to generate a model output;
• and
• a reweighting layer comprising a locally connected layer that receives the model output and applies at least one of spatial and temporarily smooth coefficients and additional multilayer convolutions to preserve a signal amplitude trend of the seismic data to generate the denoised image.