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arXiv 1701.02529physics.geo-ph
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无线电和音频大地电磁数据的聚焦时移反演

Focused time-lapse inversion of radio and audio magnetotelluric data

M. Rosas Carbajal, N. Linde, T. Kalscheuer

摘要:

地电技术被广泛用于监测地下水过程,而令人惊讶的是,很少有研究认为音频(AMT)和无线电(RMT)大地电磁法用于此类目的。在这个数值研究中,我们分析了基于AMT和RMT监测数据的反演结果可以通过以下方式得到多大程度的改善:(1)时间推移差分反演;(2)纳入有关预期模型更新的统计信息(即,模型正则化是基于地质统计模型的);(3)使用替代模型范数来量化时间变化(即,使用迭代重加权最小二乘法的L1和柯西范数的近似),(4)将模型更新约束到预定义范围(即,使用拉格朗日乘子仅允许电阻率相对于背景条件的增加或减小)。要做到这一点,我们认为一个简单的说明性模型和一个更现实的测试情况下,海水入侵。结果是令人鼓舞的,并显示显着的改善时,使用非l2模型范数的时间推移差分反演。可以通过不等式约束来抑制在施加具有时间变化的区域的紧凑性时可能出现的伪影,以产生在时间变化的真实区域之外没有振荡的模型。基于这些结果,我们建议近似l1范数的解决方案,因为它们可以解决尖锐和光滑的接口在同一个模型。

英文摘要:

Geoelectrical techniques are widely used to monitor groundwater processes, while surprisingly few studies have considered audio (AMT) and radio (RMT) magnetotellurics for such purposes. In this numerical investigation, we analyze to what extent inversion results based on AMT and RMT monitoring data can be improved by (1) time-lapse difference inversion; (2) incorporation of statistical information about the expected model update (i.e., the model regularization is based on a geostatistical model); (3) using alternative model norms to quantify temporal changes (i.e., approximations of l1 and Cauchy norms using iteratively reweighted least-squares), (4) constraining model updates to predefined ranges (i.e., using Lagrange Multipliers to only allow either increases or decreases of electrical resistivity with respect to background conditions). To do so, we consider a simple illustrative model and a more realistic test case related to seawater intrusion. The results are encouraging and show significant improvements when using time-lapse difference inversion with non l2 model norms. Artifacts that may arise when imposing compactness of regions with temporal changes can be suppressed through inequality constraints to yield models without oscillations outside the true region of temporal changes. Based on these results, we recommend approximate l1-norm solutions as they can resolve both sharp and smooth interfaces within the same model.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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