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Application of supervised descent method for 2D magnetotelluric data inversion
Geophysics ( IF 3.0 ) Pub Date : 2020-06-29 , DOI: 10.1190/geo2019-0409.1
Rui Guo 1 , Maokun Li 1 , Fan Yang 1 , Shenheng Xu 1 , Aria Abubakar 2
Affiliation  

The supervised descent method (SDM) is applied to 2D magnetotellurics (MT) data inversion. SDM contains offline training and online prediction. The training set is composed of the models generated according to prior knowledge and the data simulated by MT forward modeling. In the training process, a set of descent directions from an initial model to the training models is learned. In the prediction, model reconstruction is achieved by optimizing an online regularized objective function with a restart scheme, where the learned descent directions and the computed data residual are involved. SDM inversion has the advantages of (1) being more efficient than traditional gradient-descent methods because the computation of local derivatives of the objective function is avoided, (2) incorporating prior uncertain knowledge easier than deterministic inversion approach by generating training models flexibly, and (3) having high generalization ability because the physical modeling can guide the online model reconstruction. Furthermore, a way of designing general training set is introduced, which can be used for training when the prior knowledge is weak. The efficiency and accuracy of this method are validated by two numerical examples. The results indicate that the reconstructed models are consistent with prior information, and the simulated responses agree well with the data. This method also shows good potential to improve the accuracy and efficiency in field MT data inversion.

中文翻译:

监督下降法在二维大地电磁数据反演中的应用

监督下降法(SDM)应用于二维大地电磁(MT)数据反演。SDM包含离线培训和在线预测。训练集由根据先验知识生成的模型和MT正向建模模拟的数据组成。在训练过程中,学习了从初始模型到训练模型的一组下降方向。在预测中,通过使用重启方案优化在线正则化目标函数来实现模型重建,其中涉及学习的下降方向和计算出的数据残差。SDM反演具有(1)比传统的梯度下降法更有效的优点,因为避免了目标函数的局部导数的计算,(2)通过灵活地生成训练模型,比确定性反演方法更容易合并先前的不确定知识;(3)由于物理建模可以指导在线模型的重建,因此具有较高的泛化能力。此外,介绍了一种设计通用训练集的方法,该方法可用于在先验知识薄弱时进行训练。通过两个数值例子验证了该方法的效率和准确性。结果表明,所建立的模型与先验信息是一致的,仿真结果与数据吻合良好。该方法在提高现场MT数据反演的准确性和效率方面也显示出良好的潜力。(3)具有较高的泛化能力,因为物理建模可以指导在线模型的重建。此外,介绍了一种设计通用训练集的方法,该方法可用于在先验知识薄弱时进行训练。通过两个数值例子验证了该方法的效率和准确性。结果表明,所建立的模型与先验信息是一致的,仿真结果与数据吻合良好。该方法在提高现场MT数据反演的准确性和效率方面也显示出良好的潜力。(3)具有较高的泛化能力,因为物理建模可以指导在线模型的重构。此外,介绍了一种设计通用训练集的方法,该方法可用于在先验知识薄弱时进行训练。通过两个数值例子验证了该方法的效率和准确性。结果表明,所建立的模型与先验信息是一致的,仿真结果与数据吻合良好。该方法在提高现场MT数据反演的准确性和效率方面也显示出良好的潜力。结果表明,所建立的模型与先验信息是一致的,仿真结果与数据吻合良好。该方法在提高现场MT数据反演的准确性和效率方面也显示出良好的潜力。结果表明,所建立的模型与先验信息是一致的,仿真结果与数据吻合良好。该方法在提高现场MT数据反演的准确性和效率方面也显示出良好的潜力。
更新日期:2020-08-20
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