| Autor: |
Yuan, Chongxin, Wang, Kunpeng, Luo, Wei, Wang, Xiangpeng, Peng, Wei, Yue, Yunbao, Lan, Xing, Chen, Ning |
| Zdroj: |
IEEE Transactions on Geoscience and Remote Sensing; 2024, Vol. 62 Issue: 1 p1-10, 10p |
| Abstrakt: |
The calculation of magnetotelluric (MT) data gradients can be used for data sensitivity analysis, which is of great significance for actual sensitive areas. However, such calculations are highly complex and time consuming and therefore not conducive for the implementation of rapid analysis. To address this issue and improve computational efficiency, we propose a solution based on the transformer + Unet (T-Unet) neural network model to accelerate the calculation of 2-D MT gradients. First, we create a seven-channel dataset corresponding to the gradient label and then obtain the neural network weight model through network training and iteration and predict the gradient value rapidly and accurately on this basis. The experimental results indicate that, compared to traditional gradient computation, the T-Unet network not only significantly reduces computation time but also ensures high gradient prediction accuracy. This research demonstrates the potential of gradient fast prediction in sensitivity analysis and accelerated MT inversion. |
| Databáze: |
Supplemental Index |
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