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

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.

本研究评估了多种机器学习（ML）算法和电阻率（ER）阵列在反演方面的性能，并与传统的高斯 - 牛顿数值反演方法进行了比较。使用了四种不同的ML模型和四种阵列来估计仅六个变量，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）的组合具有最高的准确性。评估表明，MLP - NN和高斯 - 牛顿方法在估计基质电阻率方面表现良好，而目标电阻率的准确性较低，并且对于实地和假设数据，MLP - NN在目标边界处产生了更鲜明的对比。两种方法在目标描述性能方面表现相当，然而在预测目标宽度和高度方面，与高斯 - 牛顿方法相比，MLP - NN的准确性有所提高，这归因于高斯 - 牛顿方法中存在的数值平滑。MLP - NN还应用于在美国能源部汉福德站点获取的实地数据集。