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InverDiff: Seismic Impedance Inversion Using a Deep Diffusion Model
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DOI：10.1109/LGRS.2025.3582929.png)
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Abstract
En 中文
Seismic impedance inversion plays a crucial role in reservoir characterization. The estimation of impedance from seismic data is generally ill-posed; nevertheless, the advent of deep learning has led to breakthroughs in this domain. Diffusion models, which are state-of-the-art deep generative models, have recently attracted considerable attention in various deep learning problems. This letter introduces InverDiff, a deep learning method that adapts a deep diffusion model for seismic impedance inversion by casting impedance prediction as a conditional impedance generation task. InverDiff defines forward and reverse processes. The forward process involves a series of steps in which the training data are gradually diffused to pure Gaussian noise. Conversely, iterative refinement inference reverses the forward process and transforms the noise back into impedance. We use InverDiff for seismic impedance inversion on synthetic and field data, demonstrating promising results compared with those of two convolutional neural networks (CNNs).
Keywords:
Deep learning
diffusion models
seismic impedance
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