This study employs seismic refraction tomography (SRT) and electrical resistivity tomography (ERT) to assess subsurface geological conditions along the proposed Porsgrunn Highway in Norway. The primary objective is to analyze SRT and ERT tomograms to identify subsurface geological structures. However, interpreting tomograms is often limited by smoothed boundaries and reduced resolution. To address these challenges, we apply k-means clustering, a machine learning technique that groups data based on similarities in physical properties, to post-process the geophysical tomograms. This study pioneers the use of k-means clustering to interpret tomograms from SRT and ERT data in complex geological settings. We first evaluate the effectiveness of clustering techniques using numerical modeling for two geological scenarios: a horizontally layered case and a layered case with undulation and a fault structure. Utilizing automated methods (Elbow and Silhouette), we objectively determine the optimal number of clusters for each geophysical tomogram. Subsequently, we compare the performance of the k-means clustering algorithm with subjective expert interpretations and the Laplacian edge detection method. Borehole data validate the clustering results and confirm the effectiveness of optimal cluster selection techniques. The findings of this study demonstrate that k-means clustering significantly enhances the detection of geological structures by establishing clearer boundaries and minimizing noise interference, enabling more accurate fault zone delineation. Compared to traditional edge detection and subjective interpretation methods, k-means clustering offers a systematic and objective approach that improves consistency and reliability across diverse geological settings. Moreover, its automated classification of geophysical data into meaningful clusters enables efficient analysis of large datasets. This study underscores the value of integrating machine learning techniques with geophysical methods such as SRT and ERT to improve interpretability and accurately identify subsurface geological structures, particularly in fault zone identification.

中文翻译：

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通过地球物理成像和机器学习改进复杂地质环境中的地下结构解释


本研究采用地震折射断层扫描 （SRT） 和电阻率断层扫描 （ERT） 来评估挪威拟建的 Porsgrunn 高速公路沿线的地下地质条件。主要目标是分析 SRT 和 ERT 断层照片以识别地下地质结构。然而，解释断层照片通常受到平滑边界和分辨率降低的限制。为了应对这些挑战，我们应用 k-means 聚类，这是一种机器学习技术，根据物理特性的相似性对数据进行分组，以对地球物理断层图进行后处理。本研究率先在复杂的地质环境中使用 k-means 聚类来解释 SRT 和 ERT 数据的断层图。我们首先评估了使用数值建模的聚类技术在两种地质情景下的有效性：水平分层情况和具有起伏和断层结构的分层情况。利用自动化方法（Elbow 和 Silhouette），我们客观地确定每个地球物理断层图的最佳聚类数量。随后，我们将 k-means 聚类算法的性能与主观专家解释和 Laplacian 边缘检测方法进行了比较。钻孔数据验证了聚类结果，并确认了最佳聚类选择技术的有效性。本研究的结果表明，k-means 聚类通过建立更清晰的边界和最大限度地减少噪声干扰，从而实现更准确的断层带划定，从而显着增强对地质结构的检测。与传统的边缘检测和主观解释方法相比，k-means 聚类提供了一种系统、客观的方法，可以提高不同地质设置的一致性和可靠性。 此外，它将地球物理数据自动分类为有意义的集群，从而能够对大型数据集进行高效分析。这项研究强调了将机器学习技术与 SRT 和 ERT 等地球物理方法相结合的价值，以提高可解释性并准确识别地下地质结构，尤其是在断层带识别方面。