Seismic Inversion Based on Mamba and ResUNet++

Seismic inversion is crucial for reservoir characterization. Conventional inversion methods are frequently limited by low resolution and the requirement for low-frequency models. Although deep learning has shown promising performance in seismic parameter inversion, convolution-based networks mainly rely on local receptive fields and cannot explicitly capture the long-range dependencies along seismic traces, which limits their ability to recover the overall subsurface parameter trends. To alleviate this problem, we propose a Mamba-based seismic inversion network, termed DMamUNet. Built on the ResUNet++ backbone, DMamUNet introduces Mamba modules into the encoder–decoder framework to model long-sequence contextual information and global parameter variation trends, while deformable convolution blocks are used to enhance the adaptive representation of local nonstationary features. In addition, a multitrace-to-multitrace (M2M) supervision strategy and a composite loss combining well-log parameter supervision with seismic reconstruction are adopted to further improve feature learning and generalization. Experiments on the Marmousi model, post-stack field data, and pre-stack field data demonstrate that DMamUNet outperforms conventional methods. Specifically, DMamUNet significantly enhances both inversion accuracy and lateral continuity. In the post-stack field data experiment, the proposed DMamUNet achieves a 78.22% reduction in blind well mean squared error (MSE) compared with FCRN, and a 14% MSE reduction relative to TransUNet. Additionally, DMamUNet predictions on pre-stack field data exhibit higher resolution and better spatial continuity, highlighting its superiority for improving reservoir characterization.