Deep learning for high-resolution multichannel seismic impedance inversion

Abstract

Seismic impedance inversion can obtain subsurface physical properties and plays an important role in hydrocarbon and mineral exploration. Due to the inaccurate and insufficient seismic data, the inverse problem is ill posed as characterized by unreliability and nonuniqueness of solutions. Regularization techniques relying on certain prior information often are introduced to force the inverse problem to obtain stable results with predetermined characteristics. However, for complex geologic conditions, these methods usually have difficulty achieving satisfactory accuracy and resolution. We develop a deep-learning (DL)-based multichannel impedance inversion method that flexibly incorporates prior information by training with numerous realistic structural 2D impedance models based on the features of field data. The DL framework is supplemented by the attention mechanism and residual block to automatically learn more features and details from training data. A novel hybrid loss function, combining [Formula: see text] loss and multiscale structural similarity loss, is introduced to enhance the network’s capacity for learning structural features. Synthetic and field data examples demonstrate that our method can effectively produce inversion results with high resolution, good lateral continuity, and enhanced structural features compared with traditional methods.