摘要
为推动自然电位数据解释,本文基于二维电流密度模型开展了自然电位二维正反演研究。首先,基于有限差分法实现了二维自然电位正演计算,并通过与解析解对比,其最大误差不超过2%,验证了正演算法的有效性。在此基础上,建立了关于电流密度的线性正则化反演算法,该正则化算法通过合理选用正则化因子对数据噪声具有一定适应性。通过含5%噪声的合成数据反演测试结果显示,预测数据与合成数据拟合较好。实测数据的反演测试结果显示,反演预测数据与实测数据拟合较好,验证了反演算法的有效性。该反演算法能有效恢复地下电流密度分布,对地下场源的进一步定位提供了有效信息,将有利于进一步提升自然电位数据的解释效果。
In order to promote the interpretation of self-potential data,this study focuses on two-dimensional forward modeling and inversion of self-potential based on the two-dimensional current density model.Firstly,based on the finite difference method,the two-dimensional natural potential orthogonal computation is realized and its maximum error does not exceed 2%by comparing with the analytical solution,which verifies the effectiveness of the orthogonal algorithm.On this basis,a linear regularization inversion algorithm on current density is established,which has certain adaptability to data noise by reasonably selecting regularization factors.The results of the inversion test by synthetic data containing 5%noise show that the predicted data fit well with the synthetic data.The inversion test results of the measured data show that the inversion predicted data fit well with the measured data,verifying the effectiveness of the inversion algorithm.The inversion algorithm can effectively restore the underground current density distribution,and also provides effective information for the further positioning of underground field sources,and also helps to further improve the interpretation of self-potential data.
作者
王泓晔
汤文武
邓居智
陈辉
米小利
陈满
熊晨
刘文华
Wang Hongye;Tang Wenwu;Deng Juzhi;Chen Hui;Mi Xiaoli;Chen Man;Xiong Chen;Liu Wenhua(School of Geophysics and Measurement-Control Technology,East China University of Technology,Nanchang Jiangxi 330013,China;Bureau of Geophysical Prospecting Inc.,China National Petroleum Corporation,Zhuozhou Hebei 072751,China)
出处
《工程地球物理学报》
2024年第3期518-526,共9页
Chinese Journal of Engineering Geophysics
基金
国家自然科学基金(编号:42130811,41604086)
中国石油天然气股份有限公司科学研究与技术开发项目(编号:2021DJ5303)。
关键词
自然电位
二维正演
二维反演
有限差分法
self-potential
two-dimensional forward
two-dimensional inversion
finite difference method
