FIELD: geophysical surveys.
SUBSTANCE: invention relates to geophysical research and, in particular, to seismic interpretation, is a technology for post-processing seismic data using machine learning methods to improve their quality and increase accuracy of structural interpretation of geological structures. Invention can be used for the purposes of exploration geophysics. According to the invention, initial synthetic seismic data with a wide frequency range of the wavelet used for generation is generated. Then, various types of noise, processing artefacts of structural and tectonic objects are added to the initial synthetic seismic data. Further, the convolutional neural network is trained on datasets with and without noise. After that, the trained neural network is used for further cleaning of real data and increasing the resolution of the seismic record by increasing the frequency range of real data. Processed data are then analysed for structural interpretation of geological objects.
EFFECT: possibility of noise cleaning and increase of recording resolution at the initial stage of data interpretation.
1 cl, 2 dwg
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