Data Augmentation Using Multiscale Generative Adversarial Networks Under Few Well Conditions for Acoustic Impedance Inversion

A training set with sufficient quantity and reliable quality is crucial for achieving satisfactory results in data-driven acoustic impedance (AI) inversion. However, effective data augmentation still faces significant challenges when labeled Well data are sparse. In this study, it is proposed a novel impedance sequence augmentation strategy is proposed based on the single image GAN (SinGAN) multiscale generative adversarial networks under a few Well conditions. SinGAN requires only a single impedance sequence for training and supports two augmentation modes: Random Impedance Generation Mode (RIGM) and Controllable Impedance Generation Mode (CIGM). RIGM controls diversity between synthetic and true impedance by adjusting the Start Generation Scale (SGS), while CIGM synthesizes impedance by fusing a known low-frequency reference model with high-frequency details derived from Well data, the SGS determines the proportion of their integration. Three training sets were established through data augmentation using broadcasting, RIGM, and CIGM on the Marmousi2 model, and were subsequently fed into a CNN-GRU fusion network with identical hyperparameters. Experimental results show that the correlation coefficients ( ${R} ^{2}$ ) between the estimated and true impedance values reach 0.9111, 0.9282, and 0.9423 for the broadcasting, RIGM, and CIGM methods, respectively. Meanwhile, the CIGM-based model achieves the best overall performance, with a Mean Squared Error (MSE) of 0.006 and an SSIM of 0.966, and it accurately characterizes impedance variations across stratigraphic layers and clearly delineates the water–strata interface and associated sand bodies. These findings verify that the proposed augmentation strategy effectively expands the training sample space and enhances impedance prediction accuracy, offering a new promising approach for seismic inversion tasks with sparse labeled data.