We have developed a new algorithm for the inversion of magnetotelluric (MT) data. The developed algorithm is built to be fast, versatile, and accurate. A fast inversion algorithm has to include a fast forward-modeling routine. To achieve that, a hybrid approach consisting of finite-difference (FD) and finite-element (FE) methods is used to benefit from the speed of the FD method and the flexibility to add topographic features of the FE method. To reduce the number of cells, and thus reducing the size of the system to be solved in the forward and pseudoforward solutions, different meshes for various groups of frequencies are used. Then, these are mapped onto the inversion mesh by a mesh-decoupling technique to further accelerate the inversion. To build a versatile inversion algorithm, the capability of using different data types is implemented. In addition to the impedance tensor and the magnetic transfer function, the algorithm also computes the phase tensor and phase vector, which are distortion-free forms of MT data. It is also possible to invert intersite data and their respective phase tensors using the developed code. Furthermore, the distortion matrix can also be estimated as a parameter. The new code is tested with different noisy and distorted synthetic data measured on a surface with topography to evaluate the inversion accuracy and computational efficiency. The results indicate that the code is accurate and that the runtimes are reasonable for the large 3D models considered. Using four graphics processing units, the hybrid forward-modeling approach and the mesh-decoupling technique together result in a 12 times speedup for the examples presented in this study.

中文翻译：

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大地电磁数据的3D反演通过混合正演方法和网格解耦

我们已经开发了一种用于反演大地电磁（MT）数据的新算法。开发的算法被构建为快速，通用和准确的。快速反演算法必须包括快速正向建模例程。为此，使用了由有限差分（FD）和有限元（FE）方法组成的混合方法，以受益于FD方法的速度以及添加FE方法的地形特征的灵活性。为了减少小区的数量，从而减少要在正向和伪正向解中求解的系统的大小，针对各种频率组使用了不同的网格。然后，通过网格解耦技术将它们映射到反演网格上，以进一步加速反演。为了构建通用的反演算法，需要实现使用不同数据类型的能力。除了阻抗张量和磁传递函数，该算法还计算相位张量和相位矢量，它们是MT数据的无失真形式。也可以使用开发的代码反转站点间数据及其各自的相位张量。此外，失真矩阵也可以被估计为参数。使用在地形图上测得的不同噪声和失真的合成数据对新代码进行测试，以评估反演精度和计算效率。结果表明，该代码是准确的，并且对于所考虑的大型3D模型而言，运行时是合理的。使用四个图形处理单元，混合正向建模方法和网格解耦技术共同为本研究中介绍的示例提供了12倍的加速。