Acquisition of incomplete data, i.e., blended, sparsely sampled, and narrowband data, allows for cost-effective and efficient field seismic operations. This strategy becomes technically acceptable, provided that a satisfactory recovery of the complete data, i.e., deblended, well-sampled, and broadband data, is attainable. Hence, we explore a machine-learning approach that simultaneously performs suppression of blending noise, reconstruction of missing traces, and extrapolation of low frequencies. We have applied a deep convolutional neural network in the framework of supervised learning in which we train a network using pairs of incomplete-complete data sets. Incomplete data, which are never used for training and use different subsurface properties and acquisition scenarios, are subsequently fed into the trained network to predict complete data. We develop matrix representations indicating the contributions of different acquisition strategies to reducing the field operational effort. We also determine that the simultaneous implementation of source blending, sparse geometry, and band limitation leads to a significant data compression where the size of the incomplete data in the frequency-space domain is much smaller than the size of the complete data. This reduction is indicative of survey cost and duration that our acquisition strategy can save. Synthetic and field data examples demonstrate the applicability of the proposed approach. Despite the reduced amount of information available in the incomplete data, the results obtained from the numerical and field data cases clearly show that the machine-learning scheme effectively performs deblending, trace reconstruction, and low-frequency extrapolation in a simultaneous fashion. It is noteworthy that no discernible difference in prediction errors between extrapolated frequencies and preexisting frequencies is observed. The approach potentially allows seismic data to be acquired in a significantly compressed manner while subsequently recovering data of satisfactory quality.
Bibliographical noteAccepted Author Manuscript
- data reconstruction
- low frequency
- machine learning