Fig. 16 | Scientific Reports

Fig. 16

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. 16

Scatter plot of predicted vs. actual P-impedance (with 1:1 reference line) based on 948 samples from 39 wells—3 real wells and 36 synthetic pseudo-wells. Markers are coded by data origin: real wells (A, B, C) are listed explicitly in the legend, while pseudo-wells follow the labels Pseudo.W.[A/B/C] _01_sim_XXXX (12 per real well; 36 total). Pseudo-wells were used for training/augmentation only; reported validation statistics are computed on real wells. Predictions were obtained with a DFNN (three hidden layers of nine nodes each) trained on seven seismic attributes, yielding a cross-correlation of 95.4% and an RMSE of 0.592.

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