TY - JOUR
T1 - A machine learning-based approach for constructing a 3D apparent geological model using multi-resistivity data
AU - Puntu, Jordi Mahardika
AU - Chang, Ping Yu
AU - Amania, Haiyina Hasbia
AU - Lin, Ding Jiun
AU - Suryantara, M. Syahdan Akbar
AU - Tsai, Jui Pin
AU - Yu, Hwa Lung
AU - Chang, Liang Cheng
AU - Zeng, Jun Ru
AU - Kassie, Lingerew Nebere
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/12
Y1 - 2024/12
N2 - This study presents a comprehensive approach for constructing a 3D Apparent Geological Model (AGM) by integrating multi-resistivity data using statistical methods, supervised machine learning (SML), and Python-based modeling techniques. Demonstrated through a case study in the Choushui River Alluvial Fan (CRAF) in Taiwan, the methodology enhances data coverage significantly, from 62 to 386 points, by incorporating resistivity data sets from Vertical Electrical Sounding (VES), Transient Electromagnetic (TEM), and borehole information. A key contribution of this work is the rigorous harmonization of these data sets, ensuring consistent resistivity values across different methods before constructing the 3D resistivity model, addressing a gap in previous studies that typically handled these data sets separately, either building models individually or comparing results side-by-side without fully integrating the data. Furthermore, python-based modeling and radial basis function interpolation were employed to construct the 3D resistivity model for greater flexibility and effectiveness than conventional software. Subsequently, this model was transformed into a 3D AGM using the SML technique. Four algorithms, namely, random forest (RF), decision tree (DT), support vector machine (SVM), and extreme gradient boosting (XGBoost) were implemented. Following evaluation via confusion matrix analysis, evaluation metrics, and examination of receiver operating characteristics curve, it emerged that the RF algorithm exhibits superior performance when applied to our multi-resistivity data set. The results from the 3D AGM unveil distinct resistivity anomalies correlated with sediment types. The clay layer exhibited low resistivity (≤ 59.98 Ωm), while the sand layer displayed medium resistivity (59.98 < ρ < 136.14 Ωm), and the gravel layer is characterized by high resistivity (≥ 136.14 Ωm). Notably, in the proximal fan, gravel layers predominate, whereas the middle fan primarily consists of sandy clay layers. Conversely, the distal fan, located in the western coastal area, predominantly comprises clayey sand. To conclude, the findings of this study provide valuable insights for researchers to construct the 3D AGM from the resistivity data, applicable not only to the CRAF but also to other target areas.
AB - This study presents a comprehensive approach for constructing a 3D Apparent Geological Model (AGM) by integrating multi-resistivity data using statistical methods, supervised machine learning (SML), and Python-based modeling techniques. Demonstrated through a case study in the Choushui River Alluvial Fan (CRAF) in Taiwan, the methodology enhances data coverage significantly, from 62 to 386 points, by incorporating resistivity data sets from Vertical Electrical Sounding (VES), Transient Electromagnetic (TEM), and borehole information. A key contribution of this work is the rigorous harmonization of these data sets, ensuring consistent resistivity values across different methods before constructing the 3D resistivity model, addressing a gap in previous studies that typically handled these data sets separately, either building models individually or comparing results side-by-side without fully integrating the data. Furthermore, python-based modeling and radial basis function interpolation were employed to construct the 3D resistivity model for greater flexibility and effectiveness than conventional software. Subsequently, this model was transformed into a 3D AGM using the SML technique. Four algorithms, namely, random forest (RF), decision tree (DT), support vector machine (SVM), and extreme gradient boosting (XGBoost) were implemented. Following evaluation via confusion matrix analysis, evaluation metrics, and examination of receiver operating characteristics curve, it emerged that the RF algorithm exhibits superior performance when applied to our multi-resistivity data set. The results from the 3D AGM unveil distinct resistivity anomalies correlated with sediment types. The clay layer exhibited low resistivity (≤ 59.98 Ωm), while the sand layer displayed medium resistivity (59.98 < ρ < 136.14 Ωm), and the gravel layer is characterized by high resistivity (≥ 136.14 Ωm). Notably, in the proximal fan, gravel layers predominate, whereas the middle fan primarily consists of sandy clay layers. Conversely, the distal fan, located in the western coastal area, predominantly comprises clayey sand. To conclude, the findings of this study provide valuable insights for researchers to construct the 3D AGM from the resistivity data, applicable not only to the CRAF but also to other target areas.
KW - 3D apparent geological model
KW - 3D resistivity model
KW - Choushui river alluvial fan
KW - Python-based modeling
KW - Supervised machine learning
UR - http://www.scopus.com/inward/record.url?scp=85210157120&partnerID=8YFLogxK
U2 - 10.1186/s40562-024-00368-0
DO - 10.1186/s40562-024-00368-0
M3 - 期刊論文
AN - SCOPUS:85210157120
SN - 2196-4092
VL - 11
JO - Geoscience Letters
JF - Geoscience Letters
IS - 1
M1 - 54
ER -