Abstract

Machine learning has grown into a topic of much interest in the seismic industry. Recently, machine learning was introduced in the field of seismic processing for applications such as demultiple, regularization, and tomography. Here, two novel machine learning algorithms are introduced that can perform deblending and automated blending noise classification. Conventional deblending algorithms require a priori information and user expertise to properly select and parameterize a specific algorithm. The potential benefits of machine learning methods include their hands-off implementation and their ability to learn an efficient deblending algorithm directly from data. The introduced methods are supervised learning methods. Their specific tasks (deblending/noise classification) are learned from training data consisting of data example pairs of input and labeled output. For instance, training a deblending algorithm requires pairs of blended data with their unblended counterparts. The availability of training data or the possibility of creating training data are key to the success of these supervised methods. Another aspect is how well the algorithms generalize. Can we expect good performance on (unseen) data that vary from the training data? We address these aspects and further illustrate with synthetic and field data examples. The classification and deblending examples show promising results, indicating that these machine learning algorithms can support and/or replace existing deblending approaches.

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