Cgg Services Sas

Song Hou

Henning Hoeber

Stefano Angio

Jing Sun

Peng Zhao

Seismic exploration methods and data processing apparatuses employ a deep neural network to remove seismic interference (SI) noise. Training data is generated by combining an SI model extracted using a conventional model from a subset of the seismic data, with SI free shots and simulated random noise. The trained DNN is used to process the entire seismic data thereby generating an image of subsurface formation for detecting presence and/or location of sought-after natural resources.

Seismic interference noise attenuation using DNN

One method interpolates simulated seismic data of a coarse spatial sampling to a finer spatial sampling using a neural network. The neural network is previously trained using a set of simulated seismic data with the finer spatial sampling and a subset thereof with the coarse spatial sampling. The data is simulated using an image of the explored underground formation generated using real seismic data. The seismic dataset resulting from simulation and interpolation is used for denoising the seismic data acquired over the underground formation. Another method demigrates seismic data at a sparse density and then increases density by interpolating traces using a neural network.

Using neural networks for interpolating seismic data

Property values inside an explored underground subsurface are determined using hybrid analytic and machine learning. A training dataset representing survey data acquired over the explored underground structure is used to obtain labels via an analytic inversion. A deep neural network model generated using the training dataset and the labels is used to predict property values corresponding to the survey data using the DNN model.

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Crawley, United Kingdom

Song Hou