Different geophysical methods use different model parameterizations and inversion algorithms. Thus, combining these different inversion systems and yet adding the nonlinear cross-gradient constraint in a joint inversion framework might be a big challenge, for instance, as explained further by Moorkamp et al. in 2011, there is a complex interaction between the data misfit terms, regularization and cross-gradient terms and an imperfect fit to the data is expected. In this paper, we use a sequential algorithm for a two-dimensional joint inversion of gravity and magnetic data, which tries to avoid these issues by decoupling the gravity inversion, the magnetic inversion and the cross-gradient minimization processes. The efficiency of the algorithm and developed code is demonstrated by the joint inversion of noisy synthetic data. The results show a significant improvement in the respective models obtained by introducing the cross-gradient joint inversion over the models obtained by separate inversions for synthetic data and then for field data targeting potash ore source in the AjiChai salt deposit in north-western Iran.

中文翻译：

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通过交叉梯度约束对重磁数据进行连续联合反演

不同的地球物理方法使用不同的模型参数化和反演算法。因此，结合这些不同的反演系统并在联合反演框架中添加非线性交叉梯度约束可能是一个巨大的挑战，例如，正如 Moorkamp 等人进一步解释的那样。在 2011 年，数据错配项、正则化和交叉梯度项之间存在复杂的相互作用，预计数据不完全拟合。在本文中，我们使用顺序算法进行二维重磁数据联合反演，试图通过解耦重力反演、磁反演和交叉梯度最小化过程来避免这些问题。噪声合成数据的联合反演证明了算法和开发代码的效率。