3D pseudo-lithologic modeling via iterative weighted k-means++ algorithm from Tengger Desert cover area, China

Abstract

The bedrock beneath the Tengger Desert is covered by Quaternary deposits, making it difficult to directly observe the underlying geological information using traditional geological methods. In areas with limited prior geological information, employing geophysical methods to obtain deep-seated information, constructing a multi-source geophysical dataset, and performing three-dimensional modeling can significantly enhance our understanding of the underground geological structures. Cluster analysis is a fundamental unsupervised machine learning technique employed in data mining to investigate the data structure within the feature space. This paper proposes an iterative weighted distance-based extension to the k-means clustering algorithm, referred to as the Iterative Weighted Distance K-means (IW k-means++) algorithm. It incorporates the farthest distance method to select the initial centroid, performs iterative centroid updates based on weighted distance, and dynamically adjusts feature weights during training. The Davies-Bouldin index shows that the performance of IW k-means ++ clustering algorithm is better than the traditional K-Meme ++ clustering algorithm in 3D pseudo-lithology modeling.