We aim to develop a comprehensive tunnel lining detection method and clustering technique for semi-automatic rebar identification in order to investigate the ten tunnels along the South-link Line Railway of Taiwan (SLRT). We used the Ground Penetrating Radar (GPR) instrument with a 1000 MHz antenna frequency, which was placed on a versatile antenna holder that is flexible to the tunnel’s condition. We called it a Vehicle-mounted Ground Penetrating Radar (VMGPR) system. We detected the tunnel lining boundary according to the Fresnel Reflection Coefficient (FRC) in both A-scan and B-scan data, then estimated the thinning lining of the tunnels. By applying the Hilbert Transform (HT), we extracted the envelope to see the overview of the energy distribution in our data. Once we obtained the filtered radargram, we used it to estimate the Two-dimensional Forward Modeling (TDFM) simulation parameters. Specifically, we produced the TDFM model with different random noise (0–30%) for the rebar model. The rebar model and the field data were identified with the Hierarchical Agglomerative Clustering (HAC) in machine learning and evaluated using the Silhouette Index (SI). Taken together, these results suggest three boundaries of the tunnel lining i.e., the air–second lining boundary, the second–first lining boundary, and the first–wall rock boundary. Among the tunnels that we scanned, the Fangye 1 tunnel is the only one in category B, with the highest percentage of the thinning lining, i.e., 13.39%, whereas the other tunnels are in category A, with a percentage of the thinning lining of 0–1.71%. Based on the clustered radargram, the TDFM model for rebar identification is consistent with the field data, where k = 2 is the best choice to represent our data set. It is interesting to observe in the clustered radargram that the TDFM model can mimic the field data. The most striking result is that the TDFM model with 30% random noise seems to describe our data well, where the rebar response is rough due to the high noise level on the radargram.

This data article presents electrical resistivity imaging (ERI) data and inverted models with the objectives of hydrogeological characterization, land subsidence studies, and geological structural detections in Taiwan. The ERI data for hydrogeological studies includes 5 ERI profiles from Changhua, 33 from Yunlin, 36 from Yilan, 23 from Taichung, 23 from Chiayi and Tainan, and 23 from Taipei basins. In addition, time-lapse ERI profiles are presented for 10 ERI from Yilan, 10 ERI from Pingtung, 11 ERI from Taichung, and 31 ERI from Minzu basins. Moreover, 10 ERI data were used to detect the Rusui Fault, 12 for the Qishan Fault, 13 for the Yuli Fault, and 25 for the Shanyi Fault. This data article contains 265 ERI profiles with a total survey length of 59,905 m. A single ERI profile contains hundreds to thousands of subsurface apparent resistivity data points. The data was collected between 2010 and 2022 from different regions of Taiwan. The main findings from the ERI data consisted here were reported by Lin et al. [1] for the Pingtung basin, Chang et al. [2] for the Minzu basin, and Jordi et al. [3] for the Taichung basin in order to estimate hydraulic parameters and characterize the aquifer systems. The ERI data presented here can be used for a variety of hydrogeological, geological, engineering, and environmental applications, and it can be further interpreted using machine learning and statistical methods. Therefore, the ERI data will helps in various subsurface applications, academic research, and educational purposes.

In this study, we attempt to establish an alternative method for estimating the groundwater levels and the specific yields of an unconfined aquifer for the evaluation of potential groundwater reservoirs. We first converted the inverted resistivity into the normalized water content. Then, we inverted the parameters of the Brooks-Corey model from the vertical profiles of the water content by assuming that the suction head was in proportion to the elevation regarding a predefined base level. Lastly, we estimated the groundwater level, the theoretical specific yield, and the specific yield capacity from the Brooks-Corey parameters at every survey site in the study area. The contour maps of the time-lapse groundwater levels show that the groundwater flows downstream, with a higher hydraulic gradient near the river channel than in the area away from the main channel. We conclude that the estimated maximum specific yield capacities are consistent with that derived from the pumping tests in the nearby observation well. Additionally, the specific yield capacities are only three quarters to two thirds of the theoretical specific yields derived from the difference between the residual and saturated water contents in the Brooks-Corey model. We conclude that the distribution pattern of the specific yields had been subjected to the distribution of natural river sediments in the Minzu Basin, since the modern channel was artificially modified. Although we had to make some simple assumptions for the estimations, the results show that the surface resistivity surveys provide reasonable estimations of the hydraulic parameters for a preliminary assessment in an area with few available wells.

This study aims to apply geophysical methods to determine the Specific Yield (Sy) and Groundwater Level (GWL) in an unconfined aquifer of the Pingtung Plain in South Taiwan. Sy is an important hydraulic parameter for assessing groundwater potential. Obtaining specific yield for a large area is impractical due to the limited coverage and the high cost of the pumping test, which limits the potential evaluation of regional groundwater. Therefore, we used time-lapse Electrical Resistivity Imaging (ERI) to determine the Sy and GWL. Seasonal variations were considered when measuring time-lapse resistivity for five different months in 2019. We calculated the Sy and GWL from inverted resistivity data using empirical formulas and the soil–water characteristic curve (SWCC). We first used Archie’s law to calculate the relative saturation change with depth for each ERI profile, and then we used the Van Genuchten (VG) and Brooks–Corey (BC) empirical equations to estimate Sy and GWL. Finally, we compared the obtained GWL to the existing observation well to verify the findings of our study. The results showed that the VG and BC are able to predict Sy and GWL; however, the BC result is less consistent with the observation well result. In the study area, the dry season GWL ranged from 24.5 m to 35.2 m for the VG results and from 25.7 m to 35.5 m for the BC results. The wet season GWL ranged from 26.5 m to 38.9 m for the VG and from 26.4 m to 38.2 m for the BC results. The spatial distribution of the GWL shows a high gradient of GWL in the northeastern region, induced by significant proximal fan recharge. The determined spatial distribution of Sy varies from 0.15 to 0.21 for the VG and 0.14 to 0.20 for the BC results, indicating the study area has significant potential for groundwater resources. Therefore, nondestructive resistivity imaging can be used to aid in the determination of hydraulic parameters.

We used transient electromagnetic (TEM) to map the hydrogeological structures in the Choushui River Alluvial Fan in Yunlin County of central Taiwan. A total of 63 TEM measurements were collected using the FASTSNAP system with 50 × 50 m in-loop configurations in the middle and distal fan. The 1D model, based on prior information, was constructed from the inverted soundings. Results showed a thin, resistive shallow layer and a 40 m low-resistive (6–42 ohm-m) zone beneath it. High-resistive zones (50–170 ohm-m) were found from 50–120 m depth, and low-resistive zones were revealed below 120 m in some areas. Results were consistent with resistivity and lithology logs from nearby wells. The inverted TEM models provide reliable subsurface information when prior informations of vertical electrical sounding and TEM were considered. We interpolated resistivity at 10 m, 50 m, 100 m, and 160 m depth from the 1D model results to produce a slice map of the area, which indicated variations, trends, and depths of the sediment deposits. The TEM method successfully identified the hydrogeological structures, showing that the upper 40 m of sediment acts as a confining layer for the aquifer structure from 50 m to 120 m depth.

This study presents the results of the Magnetotelluric (MT) survey aimed at resolving the subsurface structures in the northern part of the Pingtung Plain. Data analysis was conducted using ten local observation stations and one remote reference station. Due to the significant noise of the urban environment, the process of obtaining high-quality results proved to be challenging. The impact of such noise on the transfer function estimation is demonstrated, emphasizing the need for careful data selection and processing to mitigate its effects. The results reveal a distinct low–high–low-resistivity trend in the subsurface, with the Quaternary–Neogene sediment boundary estimated to be up to 500 m deep. Additionally, this study maps depths of up to 4 km, where it indicates possible faulting structures below the study area, which may be related to the previously assumed structures south of the study area. Given the limited, available deep subsurface information of the study area, these findings offer a preliminary understanding of the subsurface characteristics of the northern Pingtung Plain, which may contribute to ongoing research on the geological characteristics of the region while taking into account the importance of addressing urban noise when interpreting MT data.