正则化MT反演中正则化参数选择方法的比较

Yang Xiang, P. Yu, Xiao Chen, Xu Zhang, Rui Tang
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引用次数: 0

摘要

地球物理反演是不适定的。通过高分辨率的观测数据,我们可以得到稳定的结果,并且利用正则化方法加入稳定泛函来增加解的稳定性。在Occam反演中采用共轭梯度法,提高了反演算子的效率。通过建立分层电模型,利用l曲线、GCV(广义交叉验证)和upe(无偏预测风险估计)选择最优正则化参数。通过分析各方法的特点,l曲线法非常稳定,结果可观,GCV或UPRE的结果也很好,但有过拟合的趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Comparison of different methods for choosing regularization parameter in regularized MT inversion
Geophysical inversion is ill-posed. We can get a stable result not only from high resolution observed data, but also using the regularization methods to add stabilizing functional to increase the stability of the solution. The conjugate gradient method is used in Occam's inversion to improve the efficiency of inversion operator. By establishing a layered electric model, we use L-curve, GCV (Generalized Cross Validation) and UPRE (Unbiased Predictive Risk Estimator) to select the optimal regularized parameter. Through analyzing the characteristic of each ways, L-curve method is very stable and the result is appreciable, the result of GCV or UPRE is also well and tends to overfit the data slightly.
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