Fig. 15 | Scientific Reports

Fig. 15

From: A physics-informed deep learning approach for 3D acoustic impedance estimation from seismic data: application to an offshore field in the Southwest Iran

Fig. 15

Predicted 3D acoustic impedance volume generated by a DFNN, trained using seismic attributes and augmented well data. The output cube covers the range of Inline 54–612, Crossline 1600–1900, and TWT from 730 to 790 ms, and encompasses the interval of interest from Ghar to Ghar-D. The model, leveraging both real and pseudo-well logs, demonstrates spatially consistent impedance variations that reflect the geological structure across the seismic volume.

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