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.

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.

This article presents a comprehensive electrical resistivity dataset of Taiwan derived from a multi-decadal compilation of magnetotelluric (MT) observations. The dataset integrates 406 MT sounding stations acquired between 1995 and 2026, providing island-wide spatial coverage of the Taiwanese orogenic belt. To ensure consistency across historical and recent measurements, all raw MT data were reprocessed using a standardized workflow based on one-dimensional (1D) inversion, generating a consistent set of resistivity-depth profiles for each station. The processed 1D results were further integrated to construct a regional three-dimensional (3D) resistivity model of Taiwan. Data processing and model generation were performed using an open-source Python-based workflow. Visualization and grid construction were implemented using the PyVista library, while interactive 3D exploration was enabled through ParaView. The dataset covers an area of approximately 38,019 km² with a perimeter of about 919 km and extends to a maximum investigation depth of 60 km. The model grid has a horizontal resolution of d_x,d_y = 5 km and a vertical resolution of d_z = 0.5 km, resulting in approximately 181,440 cells. The dataset is provided in formats compatible with commonly used geophysical and geoscientific software, including both the individual 1D inversion models and the derived regional 3D resistivity framework. These data provide a valuable resource for studies of regional tectonics, geothermal systems, and crustal structure in Taiwan. Previous scientific interpretations derived from portions of this MT dataset have been reported in Chang, et al. [1], Amania, et al. [2], Chang, et al. [3], Chen, et al. [4], Chen, et al. [5], Chen, et al. [6], Chen and Chen [7], Chiang, et al. [8], Chiang, et al. [9], Bertrand, et al. [10] and Bertrand, et al. [11].