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Multiarray Data Joint Super-Resolution Inversion for Electrical Resistivity Tomography
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DOI：10.1109/TGRS.2025.3639099.png)
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Abstract
En 中文
In electrical resistivity tomography (ERT), the anomaly effects of different electrode arrays vary depending on the geological model. The appropriate combination of different electrode arrays can optimize detection performance and enhance the reliability of interpretation results. However, traditional inversion methods, constrained by single-array data, sparse observations, and ill-posed problem-solving, often yield low-resolution or inaccurate results. To address the resolution challenges in ERT inversion, inspired by the outstanding fusion and nonlinear mapping capabilities of multimodal deep learning (DL) image methods, we propose the super-resolution ERT fusion network (SRERTF-Net), which utilizes traditional inversion results of multiarray as the initial models, efficiently leveraging and integrating prior physical information to achieve multiarray data joint super-resolution inversion. In SRERTF-Net, different downsampling paths are employed to process the inversion results of various electrode arrays, while Inception modules are introduced to enhance feature extraction. In addition, dense connections are implemented both within and across paths to effectively integrate complementary information from different arrays, ensuring robust multimodal feature fusion. Finally, we designed training samples that include randomly generated typical structural models and comprehensive complex models, in order to enhance the practicality and adaptability of the network. Experiments on synthetic and field-measured data indicate that SRERTF-Net outperforms other methods in terms of resistivity accuracy, resolution, and background performance.
Keywords:
Deep learning (DL)
electrical resistivity tomography (ERT)
inversion problem
multimodal imaging
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W
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