{"title":"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","authors":"Wenjin Chen, Xiaolong Tan","doi":"10.1016/j.cageo.2025.106014","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 106014"},"PeriodicalIF":4.2000,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Adaptive Preconditioned Conjugate Gradient Regularization (APCGR) algorithm with Sigmoid Function (SF) constraint for efficient three-dimensional (3D) gravity focusing inversion\",\"authors\":\"Wenjin Chen, Xiaolong Tan\",\"doi\":\"10.1016/j.cageo.2025.106014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":55221,\"journal\":{\"name\":\"Computers & Geosciences\",\"volume\":\"205 \",\"pages\":\"Article 106014\"},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2025-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers & Geosciences\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0098300425001645\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Geosciences","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0098300425001645","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
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.
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.