dc.description.abstract | Geoelectrical and Ground Penetrating Radar (GPR) methods have been in use for over a century. In fact, the first attempt at geoelectrical research took place in the 1920s, focusing on electrical conductivity at different depths in a one-dimensional (1D) model. On the other hand, GPR was initially introduced as a tool for evaluating glacier thickness in the 1930s. Over the years, these methods have evolved significantly, resulting in more powerful and efficient equipment and data acquisition instruments. With well-developed instruments now available, the primary challenge that demands more attention is how to effectively manage, analyze, and interpret the acquired data from these methods. Several issues related to data analysis have persisted for an extended period, often overlooked by researchers.
Firstly, the majority of inversion software comes at a steep cost, rendering it unaffordable for many researchers to conduct their studies. Secondly, challenges persist in analyzing and interpreting primary and secondary data derived from geoelectrical measurements, particularly when dealing with time series data, conducting forward modeling, and transforming acquired data into meaningful information, such as lithology or other physical parameters. Thirdly, the field lacks a well-developed technique for effectively integrating multi-geoelectrical data. Many researchers still rely on outdated methods, such as comparing results from various measurement techniques side by side or overlaying each other′s images to find links between them. Fourthly, there are issues concerning the accuracy of three-dimensional (3D) modeling. While researchers often turn to commercial software for handling 3D models, these tools can introduce problems like overfitting and excessive interpolation of the data. Furthermore, such software sometimes struggles to adequately address the model′s boundary conditions, resulting in inflexibility and misinterpretation. Fifthly, there is a significant demand for time and labor-intensive data processing and interpretation, especially when dealing with numerous datasets and attempting to identify multiple distinct features simultaneously, such as locating reinforcement bars or rebar in radargrams. Advanced methods need to be explored to effectively address this issue. Finally, there is a significant deficiency in quantitative methods for data analysis. Many previous studies primarily rely on visual interpretations of results obtained from two-dimensional (2D) data, such as delineating structures, anomalies, or targets, without incorporating quantitative approaches. This oversight is significant since, as geophysicists, having numerical values along with their corresponding physical meanings is of utmost importance in conducting research.
To address the identified challenges, this study aims to introduce sophisticated processing steps and data analysis through the integration of Machine Learning (ML) with Python-based modeling and visualization.
Firstly, a Python-based approach coupled with machine learning is proposed to tackle the high-cost inversion software. Supervised Machine Learning (SML) techniques, such as Random Forest, Support Vector Machine, and Linear Regression, are employed. The results are compared with the conventional Least Square Method, revealing that the Random Forest algorithm exhibits promise with lower RMSE and higher R2 for both forward modeling simulation data and field data. Secondly, to enhance the interpretation of resistivity data, Unsupervised Machine Learning (UML) methods, including Hierarchical Agglomerative Clustering (HAC) and K-means algorithms, are applied to both primary and secondary resistivity data. In the primary dataset, the inverted resistivity section for both field and forward modeling data is interpreted, while in the secondary dataset, resistivity data transformed into groundwater parameters reveal unique patterns in the survey area. This approach leads to a novel understanding of groundwater behavior within seasonal fluctuations. Thirdly, the study successfully integrates multi-geoelectrical data using data assimilation in statistical methods, moving beyond the traditional direct comparison approach that neglects the resistivity range of each measurement. Fourthly, by employing Python-based modeling and visualization with a detailed step-by-step procedure, a 3D resistivity model is developed without overfitting or excessive interpolation. This is in contrast to commercial software, where the boundary and topography of the model are predefined. Moreover, utilizing SML, the 3D resistivity model is converted into a 3D apparent geological model, where borehole data act as ground truth data. Fifthly, the ML procedures developed for Electrical Resistivity Imaging (ERI) are extended to Ground Penetrating Radar (GPR) data analysis. Automation of radargram interpretation, including the detection of lining, voids, and rebar, is achieved. A 3D model for GPR data is constructed, addressing phase differences for improved visualization. Finally, data analysis is enhanced quantitatively for both ERI and GPR data. For ERI data, Unsupervised Machine Learning is applied to delineate structure boundaries in 2D data, confirming the success of this method through 2D forward modeling simulation. For GPR data, this method is successfully implemented for quantitative groundwater studies, estimating soil water content and groundwater table using various statistical models.
In summary, this dissertation provides valuable solutions to the challenges encountered in employing geoelectrical and GPR methods for over the years. The advanced procedures applied to field data are validated through forward modeling simulation, offering insights that could address past issues in geoelectrical and GPR studies. Ultimately, these findings contribute to navigating future research endeavors in the field. | en_US |