Conventional linear iterative methods for magnetotelluric sounding (MT) suffer from considerable limitations related to difficulties in selecting the initial model and local optima. On the other hand, conventional intelligent nonlinear methods exhibit slow convergence and low accuracy. In this study, we propose an inversion strategy based on the deep learning (DL) deep belief network (DBN) to realise the instantaneous inversion of MT data. A scaled momentum learning rate is introduced to improve the convergence performance of the restricted Boltzmann machine during the DBN pre-training stage, and a novel activation function (DSoft) is introduced to enhance the global optimisation capability during the DBN fine-tuning stage. To address the difficulty in designing the sample data when prior information is lacking, we employ the k-means++ algorithm to cluster the MT field data and use the clustering results as the prior information to guide the construction of the sample dataset. Then, based on the proposed DBN, we ensure end-to-end mapping directly from the apparent resistivity to the resistivity model. We implement two groups of experiments to demonstrate the validity of both improvements on the DBN. We consider six types of geoelectric model from the test set to demonstrate the feasibility and effectiveness of the proposed DBN method for MT 2D inversion, which we further compare with the well-known least-square regularisation method for several extended geoelectric models and field data. The qualitative and quantitative analyses show that the DL inversion method is promising as it can accurately delineate the subsurface structures and perform rapid inversion.

中文翻译：

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大地电磁探测数据的二维深度学习反演

用于大地电磁探测 (MT) 的传统线性迭代方法受到与选择初始模型和局部最优值困难相关的相当大的限制。另一方面，传统的智能非线性方法收敛速度慢，精度低。在这项研究中，我们提出了一种基于深度学习（DL）深度信念网络（DBN）的反演策略来实现MT数据的瞬时反演。引入缩放动量学习率以提高受限玻尔兹曼机在 DBN 预训练阶段的收敛性能，并引入新的激活函数（DSoft）以增强 DBN 微调阶段的全局优化能力。为了解决在缺乏先验信息时设计样本数据的困难，我们采用k-means++算法对MT现场数据进行聚类，并将聚类结果作为先验信息来指导样本数据集的构建。然后，基于所提出的 DBN，我们确保直接从视电阻率到电阻率模型的端到端映射。我们实施了两组实验来证明对 DBN 的两种改进的有效性。我们从测试集中考虑了六种类型的地电模型，以证明所提出的 DBN 方法用于 MT 2D 反演的可行性和有效性，我们进一步将其与几种扩展地电模型和现场数据的众所周知的最小二乘正则化方法进行了比较。