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摘要: 针对传统反演方法存在依赖初始模型、反演时间较长等问题,提出一种基于改进残差网络的大地电磁反演方法。该方法首先构造不同形状和不同电阻率的地电模型,在TM模式下正演得到视电阻率数据,组成数据集;然后在经典的残差网络ResNet基础上进行改进得到一种新的反演网络iResNet(improved residual network),并使用上述数据集训练该网络;最后将视电阻率数据输入到训练好的网络中,直接得到反演结果。实验结果表明,该方法能快速、准确地反演出地电模型的位置、形态和电阻率值,具有较好的泛化能力和抗噪能力,并能有效解决大地电磁实测数据问题。Abstract: Traditional inversion techniques rely on initial models and exhibit prolonged inversion times. This study proposed a magnetotelluric inversion method based on an improved residual network. Specifically, geoelectric models of varying shapes and resistivity values were established, and apparent resistivity data were obtained using the TM mode, forming a dataset. Then, a novel inversion network-iResNet (an improved residual network)-was established by improving classic residual network ResNet, and the new network was trained using the afore-mentioned data set. Finally, the apparent resistivity data were input to trained network, directly producing inversion results. The experimental results demonstrate that the method proposed in this study can accurately determine the positions, shapes, and resistivity values of the geoelectric models through swift inversion, suggesting high generalization and anti-noise capabilities. Therefore, this method can effectively deermine measured magnetotelluric data.
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Key words:
- residual network /
- magnetotelluric /
- inversion
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