Focusing geophysical inversion images
Focusing geophysical inversion images
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DOI：
10.1190/1.1444596
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发表时间：
1999-05-01
期刊：
影响因子：
3.3
通讯作者：
Zhdanov, MS
中科院分区：
文献类型：
--
作者：
Portniaguine, O;Zhdanov, MS
地球物理数据反演的一个关键问题是开发一种稳定的反演问题解决方案，可以同时解决复杂的地质结构。获得稳定解的传统方法是基于最大平滑度准则。然而，这种方法提供了真实地电结构的平滑未聚焦图像。最近，基于全变分稳定泛函开发了一种新的图像重建方法。然而，在地球物理应用中，它仍然会产生扭曲的图像。在本文中，我们开发了一种新技术来解决这个问题，我们称之为聚焦反转图像。它基于专门选择的稳定泛函，称为最小梯度支持 (MGS) 泛函，可最大限度地减少模型参数变化和不连续性发生的区域。我们证明，与传统的最大平滑度或总变差泛函相比，MGS 泛函与惩罚函数相结合有助于生成更清晰、更集中的地质结构图像。该方法已成功在合成模型上进行了测试，并应用于真实的重力数据。
A critical problem in inversion of geophysical data is developing a stable inverse problem solution that can simultaneously resolve complicated geological structures. The traditional way to obtain a stable solution is based on maximum smoothness criteria. This approach, however, provides smoothed unfocused images of real geoelectrical structures. Recently, a new approach to reconstruction of images has been developed based on a total variational stabilizing functional. However, in geophysical applications it still produces distorted images. In this paper we develop a new technique to solve this problem which we call focusing inversion images. It is based on specially selected stabilizing functionals, called minimum gradient support (MGS) functionals, which minimize the area where strong model parameter variations and discontinuity occur. We demonstrate that the MGS functional, in combination with the penalization function, helps to generate clearer and more focused images for geological structures than conventional maximum smoothness or total variation functionals. The method has been successfully tested on synthetic models and applied to real gravity data.