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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Joint Inversion of Gravity and Magnetotelluric Data for the Depth-to-Basement Estimation
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Joint Inversion of Gravity and Magnetotelluric Data for the Depth-to-Basement Estimation

机译：重力和大地电磁数据的联合反演，用于深度到基底的估计

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摘要

It is well known that both gravity and magnetolluric (MT) methods can be used for the depth-to-basement estimation due to the density and conductivity contrast between the sedimentary basin and the underlaid basement rocks. In this case, the primary targets for both methods are the interface between the basement and sedimenary rocks as well as the physical properties of the rocks (density and conductivity). The solution of this inverse problem is typically nonunique and unstable, especially for gravity inversion. In order to overcome this difficulty and provide a more robust solution, we have developed a method of joint inversion to recover both the depth to the basement and the physical properties of the sediments and basement using gravity and MT data simultaneously. The joint inversion algorithm is based on the regularized conjugate gradient method. To speed up the inversion, we use an effective forward modeling method based on the surface Cauchy-type integrals for the gravity field and the surface integral equation representations for the MT field, respectively. We demonstrate the effectiveness of the developed method using several realistic model studies.
机译：众所周知，由于沉积盆地和地下基底岩石之间的密度和电导率对比，重力法和磁流体法（MT）均可用于深度到基底的估算。在这种情况下，这两种方法的主要目标都是基底和沉积岩之间的界面以及岩石的物理性质（密度和电导率）。该反问题的解决方案通常是非唯一且不稳定的，尤其是对于重力反演而言。为了克服这一困难并提供更可靠的解决方案，我们开发了一种联合反演方法，可同时利用重力和MT数据恢复地下室的深度以及沉积物和地下室的物理特性。联合反演算法基于正则共轭梯度法。为了加快反演速度，我们分别使用基于重力场的表面柯西型积分和MT场的表面积分方程表示的有效前向建模方法。我们使用几种现实模型研究证明了开发方法的有效性。

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