Semi-Supervised Seismic Stratigraphic Interpretation Constrained by Spatial Structure

Seismic stratigraphic interpretation plays an important role in geophysics and geoscience. Recently, deep learning has been widely applied to seismic stratigraphic interpretation. These deep-learning-based stratigraphic interpretation methods have shown greater potential than traditional methods. Despite the promising results achieved by deep-learning-based methods, it is still necessary to enhance their generalization capabilities and the reasonability of stratigraphic interpretation. Therefore, we propose a semi-supervised deep-learning-based method to improve the accuracy and reasonability of interpretation results. First, we quantitatively describe the correlation of adjacent seismic data using the dynamic time warping (DTW) algorithm. The correlation of all seismic data can be regarded as the spatial structure of seismic data. The interpretation results of seismic data should conform to such spatial structure. Then, we train a deep learning model to interpret seismic stratigraphic units under the constraints of seismic spatial structure. The performance of the proposed seismic stratigraphic interpretation method is evaluated on the Netherlands F3 data. We build two scenarios to interpret the stratum: 1-D scenario for the one seismic profile and 2-D scenario for the whole seismic volume. The results on the field data demonstrate that the proposed method has better generalization ability and the interpretation results are more reasonable. Therefore, the proposed method can be a useful tool for seismic stratigraphic interpretation.