Comparison of machine learning and electrical resistivity arrays to inverse modeling for locating and characterizing subsurface targets

IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY
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引用次数: 0

Abstract

This study evaluates the performance of multiple machine learning (ML) algorithms and electrical resistivity (ER) arrays for inversion with comparison to a conventional Gauss-Newton numerical inversion method. Four different ML models and four arrays were used for the estimation of only six variables for locating and characterizing hypothetical subsurface targets. The combination of dipole-dipole with Multilayer Perceptron Neural Network (MLP-NN) had the highest accuracy. Evaluation showed that both MLP-NN and Gauss-Newton methods performed well for estimating the matrix resistivity while target resistivity accuracy was lower, and MLP-NN produced sharper contrast at target boundaries for the field and hypothetical data. Both methods exhibited comparable target characterization performance, whereas MLP-NN had increased accuracy compared to Gauss-Newton in prediction of target width and height, which was attributed to numerical smoothing present in the Gauss-Newton approach. MLP-NN was also applied to a field dataset acquired at U.S. DOE Hanford site.

机器学习和电阻率阵列与逆建模在地下目标定位和特征描述方面的比较
本研究评估了多种机器学习(ML)算法和电阻率(ER)阵列的反演性能,并与传统的高斯-牛顿数值反演方法进行了比较。四种不同的 ML 模型和四个阵列仅用于估算六个变量,以定位和描述假设的地下目标。偶极-偶极与多层感知器神经网络(MLP-NN)的组合精度最高。评估结果表明,MLP-NN 和高斯-牛顿方法在估计基体电阻率方面表现良好,而目标电阻率精度较低,MLP-NN 在野外数据和假设数据的目标边界处产生了更鲜明的对比。这两种方法的目标特征描述性能相当,而 MLP-NN 在预测目标宽度和高度方面的精度比高斯-牛顿方法高,这归因于高斯-牛顿方法中的数值平滑。MLP-NN 还被应用于在美国能源部汉福德基地获得的现场数据集。
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来源期刊
Journal of Applied Geophysics
Journal of Applied Geophysics 地学-地球科学综合
CiteScore
3.60
自引率
10.00%
发文量
274
审稿时长
4 months
期刊介绍: The Journal of Applied Geophysics with its key objective of responding to pertinent and timely needs, places particular emphasis on methodological developments and innovative applications of geophysical techniques for addressing environmental, engineering, and hydrological problems. Related topical research in exploration geophysics and in soil and rock physics is also covered by the Journal of Applied Geophysics.
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