基于FCM模型约束的二维直流电阻率正则化反演
Two-dimensional DC resistivity regularization inversion based on FCM model constraint
魏丽 1汤洪志 1郭晶 2张志勇1
作者信息
- 1. 东华理工大学地球物理与测控技术学院,南昌 330013
- 2. 核工业南京工程勘察院,南京 211102
- 折叠
摘要
本文开展了二维直流电阻率模糊C均值聚类(FCM)约束反演研究.在经典最小结构模型正则化的基础上,将L1范数与L2范数模型约束反演进行了对比,可以看出L1范数反演结果相较L2范数边界更清晰、物性呈块状分布更明显.在此基础上,联合使用FCM模型约束,将精确岩石物性测量值作为参考聚类中心,引导反演物性向聚类中心发展,FCM聚类约束的应用可进一步提高反演的效果.合成数据与实测数据反演的结果表明,基于精确岩石物性引导的FCM模型约束与L,范数最小结构模型联合,可以实现高精度的直流电阻率反演.
Abstract
This article presents a study on Fuzzy C-Means(FCM)constrained inversion of 2D DC resistivity.Based on the minimum structure model constraint regularized inversion,we compared the L,-norm with L2-norm model constraints.It is observed that the L1-norm inversion yields clearer boundaries and more pronounced block-like distribution of physical properties compared to the L2-norm inversion.Furthermore,by incorporating FCM model constraints,accurate rock property measurements are utilized as reference cluster centers to guide the inversion towards those centers.The application of FCM clustering constraints further enhances the inversion results.Both synthetic and field data inversions demonstrate that the combination of FCM model constraints guided by accurate rock property measurements and L1-norm minimum structure model enables high-precision DC resistivity inversion.
关键词
L,范数/正则化反演/FCM聚类/直流电阻率Key words
L,norm/Regularization inversion/Fuzzy C-Means(FCM)clustering constraint/Direct current resistivity引用本文复制引用
基金项目
国家自然科学基金(42164008)
江西省自然科学基金(20192BAB202012)
出版年
2024
NETL
NSTL
万方数据