基于Sigmoid函数(SF)约束的自适应预条件共轭梯度正则化(APCGR)算法用于三维重力聚焦反演

IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Wenjin Chen, Xiaolong Tan
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引用次数: 0

摘要

本文介绍了一种新的重力聚焦反演算法,并开发了相应的软件,重点介绍了三个关键创新点。首先,提出了自适应预条件共轭梯度正则化算法,该算法能有效地自适应确定正则化参数。其次,我们引入了Sigmoid函数来稳定反演过程,显著加快了迭代收敛。第三,我们利用流行的高级交互式编程语言MATLAB,为这种新方法开发了具有图形用户界面的用户友好软件。为了促进知识共享和资源可及性,我们对软件进行了开源。为了验证我们的方法,我们在合成和真实重力数据上测试了该算法,证明了其精确重建复杂地下结构三维密度分布的卓越能力。在此基础上,对新算法、受SF约束的共轭梯度法和标准共轭梯度法进行了对比分析。结果表明,该方法迭代次数少,计算效率高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Adaptive Preconditioned Conjugate Gradient Regularization (APCGR) algorithm with Sigmoid Function (SF) constraint for efficient three-dimensional (3D) gravity focusing inversion
We introduce a novel focused gravity inversion algorithm and develop corresponding software, highlighting three key innovations. First, we propose the Adaptive Preconditioned Conjugate Gradient Regularization algorithm, which efficiently and adaptively determines the regularization parameter. Second, we incorporate the Sigmoid Function to stabilize the inversion process, significantly accelerating iterative convergence. Third, we have developed a user-friendly software with a graphical user interface for this new method, utilizing the popular high-level and interactive programming language MATLAB. To promote knowledge sharing and resource accessibility, we have made the software open-source. To validate our approach, we tested the algorithm on both synthetic and real gravity data, demonstrating its exceptional capability to accurately reconstruct the 3D density distribution of complex subsurface structures. Furthermore, we conducted a comparative analysis between the new algorithm, the conjugate gradient method constrained by SF, and the standard conjugate gradient method. The results indicate that the new method requires fewer iterations and exhibits higher computational efficiency.
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来源期刊
Computers & Geosciences
Computers & Geosciences 地学-地球科学综合
CiteScore
9.30
自引率
6.80%
发文量
164
审稿时长
3.4 months
期刊介绍: Computers & Geosciences publishes high impact, original research at the interface between Computer Sciences and Geosciences. Publications should apply modern computer science paradigms, whether computational or informatics-based, to address problems in the geosciences.
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