A comprehensive deep regional electrical resistivity dataset of Taiwan from magnetotelluric surveys.

Authors

Jordi Mahardika Puntu, Ping-Yu Chang, Chow-Son Chen, Haiyina Hasbia Amania, Yekti Widyaningrum, M Syahdan Akbar Suryantara

Published in

Data in brief. Volume 67. Pages 113047. Epub Jul 09, 2026.

Abstract

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].

PMID:
42472177
Bibliographic data and abstract were imported from PubMed on 19 Jul 2026.

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