基于Res-UNet网络的井地电阻率法反演  

A Res-UNet network-based method for borehole-to-surface electrical resistivity inversion

作　　者：周楠 王智[1] 方思南 张宇哲 ZHOU Nan;WANG Zhi;FANG Si-Nan;ZHANG Yu-Zhe(School of Electronic Information and Electrical Engineering,Yangtze University,Jingzhou 434023,China;College of Geophysics and Petroleum Resources,Yangtze University,Wuhan 430100,China)

机构地区：[1]长江大学电子信息与电气工程学院,湖北荆州434023 [2]长江大学地球物理与石油资源学院,湖北武汉430100

出　　处：《物探与化探》2025年第1期73-81,共9页Geophysical and Geochemical Exploration

基　　金：国家自然科学基金(41604093);国家自然科学基金青年项目(42204127)。

摘　　要：为了解决传统电阻率反演方法依赖反演初始模型的选择、反演过程容易陷入局部极小且反演耗时较长的缺点,本文提出了一种基于Res-UNet神经网络的井地电阻率实时反演方法,通过Gmsh软件获得大幅扩展的正演响应数据集,并针对数据特性选取合适的网络参数进行反演实验。实验结果表明,Res-UNet算法能充分挖掘数据特性,快速获得符合地层电性特征的电阻率成像结果,在电阻率正演数据集上的预测值和正演响应的均方误差为0.01944,在测试集上的均方根误差为0.0758,与传统反演方法相比,成像结果有显著提升。基于Res-UNet网络的井地电阻率反演方法在仿真模型的反演计算中得到了较好的结果,能快速、准确地反演出地下异常体的位置和形态,且具有较好的抗噪声能力,为电阻率数据和真实地电结构之间的映射关系提供了一种新的方法和思路。Traditional resistivity inversion methods tend to rely on the initial inversion model selected,get stuck in local minima,and be time-consuming.To address these issues,this study proposed a real-time resistivity inversion method based on the Res-UNet neural network.First,a significantly expanded forward response dataset was generated using the Gmsh software.Then,inversion experiments were carried out based on appropriate network parameters determined according to data characteristics.The experimental results indicate that the Res-UNet algorithm can fully dig the data characteristics and rapidly produce resistivity images that align with the electrical properties of strata.The experiments on the dataset for resistivity forward modeling yielded a mean squared error between the predicted values and the forward responses of 0.01944,and those on the test set yielded a mean squared error of 0.0758,suggesting improved imaging results compared to traditional inversion methods.Furthermore,the proposed method achieved encouraging results in the inversion calculations of simulation models,enabling rapid and accurate inversion of the location and morphologies of subsurface anomalies while exhibiting a strong noise resistance.This study provides a new method and philosophy for mapping the relationship between resistivity data and the actual geoelectric structures.

关 键 词：Res-UNet Gmsh 残差块 批量建模 井地电阻率反演 

分 类 号：P631[天文地球—地质矿产勘探]