三维 CSEM 反演中的误差传播和模型更新分析

IF 2.8 3区 地球科学 Q2 GEOCHEMISTRY & GEOPHYSICS
Rahul Dehiya
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引用次数: 0

摘要

摘要 本研究利用基于高斯-牛顿算法的可控源电磁数据三维反演过程中,研究了从数据空间到模型空间的误差传播。利用各种正则化策略的高阶广义奇异值分解,获得了模型参数修正表达式。针对不同类型的噪声,采用不同的正则化方案进行反建模,以研究误差的影响。被随机噪声破坏的数据表明,由于随机噪声的高频特性,当正则化参数较小时,随机噪声主要会传播。此外,随机噪声主要在反演模型的较浅部分造成假象。然而,它对主要异常的估计影响不大,因为异常主要取决于数据的平滑变化部分。这些观察结果对各向同性和各向异性反演均有效。电阻性地质异常,如垂直堤坝或垂直裂缝,即使地下各向同性,也可能在收敛性和数据拟合方面对各向同性反演构成重大挑战。另一方面,各向异性反演在这种情况下表现出色,比各向同性反演收敛更快,数据拟合更好。在各向异性主介质的情况下,各向异性反演是不可或缺的,因为各向同性反演会产生明显的伪影,数据拟合度较差。数值实验表明,一般来说,各向异性反演产生的数据拟合效果相对更好,收敛速度更快,即使是在各向同性的地下情况下也是如此。然而,由于 CSEM 数据对薄电阻体的敏感程度不同,在解释利用各向异性反演获得的各向异性时需要谨慎。对实地数据的调查也支持利用合成实验获得的观测结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Error propagation and model update analysis in three-dimensional CSEM inversion
Summary This study examines error propagation from data space to model space during three-dimensional inversion of controlled-source electromagnetic data using a Gauss-Newton based algorithm. An expression for model parameter correction is obtained using higher-order generalised singular value decomposition for various regularisation strategies. Inverse modelling is performed for different types of noise employing distinct regularisation schemes to investigate the impact of error. Data corrupted with random noise suggests that the random noise mainly propagates when regularisation parameters are small, owing to the high-frequency nature of random noise. Furthermore, the random noise predominantly causes artefacts in the shallower part of the inverted model. However, it has little impact on the estimation of major anomalies because the anomaly primarily depends on the smoothly varying parts of data. These observations are valid for both isotropic and anisotropic inversions. Resistive geological anomalies, like vertical dyke or vertical fractures, may pose a significant challenge for isotropic inversion in terms of convergence and data fit, even if the subsurface is isotropic. On the other hand, anisotropic inversion performs remarkably well in such cases, showing faster convergence and better data fit than isotropic inversion. Anisotropic inversion is indispensable in the case of an anisotropic host medium, as isotropic inversion produces significant artefacts and poorer data fit. Numerical experiments suggest that, in general, anisotropic inversion produces relatively better data fit and faster convergence, even in the case of isotropic subsurface. However, due to the varying degree of sensitivity of CSEM data on thin resistive bodies, caution is required in interpreting an anisotropy obtained using anisotropic inversion. An investigation of field data also supports the observations obtained using synthetic experiments.
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来源期刊
Geophysical Journal International
Geophysical Journal International 地学-地球化学与地球物理
CiteScore
5.40
自引率
10.70%
发文量
436
审稿时长
3.3 months
期刊介绍: Geophysical Journal International publishes top quality research papers, express letters, invited review papers and book reviews on all aspects of theoretical, computational, applied and observational geophysics.
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