Quantifying the sensitivity of seismic facies classification to seismic attribute selection: An explainable machine-learning study

Abstract

During the past two decades, geoscientists have used machine learning to produce a more quantitative reservoir characterization and to discover hidden patterns in their data. However, as the complexity of these models increase, the sensitivity of their results to the choice of the input data becomes more challenging. Measuring how the model uses the input data to perform either a classification or regression task provides an understanding of the data-to-geology relationships which indicates how confident we are in the prediction. To provide such insight, the ML community has developed Local Interpretable Model-agnostic Explanations (LIME), and SHapley Additive exPlanations (SHAP) tools. In this study, we train a random forest architecture using a suite of seismic attributes as input to differentiate between mass transport deposits (MTDs), salt, and conformal siliciclastic sediments in a Gulf of Mexico dataset. We apply SHAP to understand how the model uses the input seismic attributes to identify target seismic facies and examine in what manner variations in the input such as adding band-limited random noise or applying a Kuwahara filter impact the models’ predictions. During our global analysis, we find that the attribute importance is dynamic, and changes based on the quality of the seismic attributes and the seismic facies analyzed. For our data volume and target facies, attributes measuring changes in dip and energy show the largest importance for all cases in our sensitivity analysis. We note that to discriminate between the seismic facies, the ML architecture learns a “set of rules” in multi-attribute space and that overlap between MTDs, salt, and conformal sediments might exist based on the seismic attribute analyzed. Finally, using SHAP at a voxel-scale, we understand why certain areas of interest were misclassified by the algorithm and perform an in-context interpretation to analyze how changes in the geology impact the model’s predictions.

A data-driven method for total organic carbon prediction based on random forests

“…For the regression problem, the average of all the regression decision tree output values is used as the prediction of the forest. Previous work in geophysics has shown that for RF classification, such as lithology and fluid identification, using several target-sensitive elastic properties obtained by pre-stack seismic inversion as input feature variables can yield good classification results (Kim et al, 2018;Lubo-Robles et al, 2022). However, for the regression of continuous numerical variable such as TOC, the influence of the number of elastic properties on the prediction results is not clear enough.…”

“…(2019) developed a robust support vector machine (SVM) learning approach to identify high TOC formations. Among different supervised learning strategies, the Random Forests (RF) has been increasingly applied in the field of geophysics (Cracknell and Reading, 2014;Kim et al, 2018;Lubo-Robles et al, 2022). The RF is an ensemble learning algorithm, which combines the idea of bagging ensemble and random feature selection, and the prediction result is determined by voting with multiple weak classifiers (Breiman, 2001).…”