Application of supervised descent method for 2D magnetotelluric inversion and its application
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摘要: 传统的大地电磁二维反演方法较为成熟, 但仍存在反演结果依赖初始模型和正则化参数选取、容易陷入局部极小值等问题。监督下降法是一种学习平均下降方向来预测数据残差的机器学习算法。本文尝试采用监督下降法解决传统的大地电磁二维反演存在的问题, 基于监督下降法理论开发了大地电磁二维反演算法, 设计理论模型合成算例验证了算法的正确性, 并对西藏高原实测数据进行反演, 检验了监督下降法的实用性。理论模型合成数据和实测数据反演结果表明, 相较于传统的非线性共轭梯度反演, 基于监督下降法的反演具有收敛速度快、反演效果好、抗噪能力强等特点。Abstract: Traditional two-dimensional inversion methods of magnetotelluric are mature, but there are still some problems, such as reliance on the initial model, reliance on regularization parameter selection, and easy to fall into local minimum. In order to solve the above problems, this paper adopts the supervised descent method to improve the effect of two-dimensional inversion of magnetotelluric. The supervised descent method is a machine learning algorithm that learns the average descending direction to predict the residual of data. Based on the theory of supervised descent method, this paper develops the two-dimensional inversion algorithm of magnetotelluric, designs the theoretical model synthesis example to verify the correctness of the algorithm, and inverts the measured data on the Tibet Plateau to test the practicability of the supervised descent method. The inversion results of the theoretical model synthesis data and the measured data show that, compared with the traditional nonlinear conjugate gradient inversion, the inversion based on the supervised descent method has the characteristics of fast convergence speed, good inversion effect, and strong anti-noise ability.
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