A Unique Long-term Monitoring Transient Electromagnetic Dataset

We present the first long-term monitoring dataset collected using a transient electromagnetic (TEM) instrument specifically designed for geophysical monitoring applications. The dataset consists of 762 measurements acquired between January 19<sup>th</sup>, 2023, and September 05<sup>th</sup>, 2025, from the island of Falster, southwestern part of Denmark, an area where saltwater intrusion is a recurring issue. The dataset displays a high consistency throughout the period, which is the foundation for a reliable interpretation over time. The dataset provides a valuable opportunity for researchers to test and validate advanced TEM inversion algorithms, including deterministic, Monte Carlo or probabilistic inversion algorithms, thereby contributing to the advancement of time-lapse studies using TEM methods.

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Publication Details

Journal
Scientific Data
Published
2025-12-10
DOI
https://doi.org/10.1038/s41597-025-06386-7
Primary Topic
Geophysical and Geoelectrical Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

A Unique Long-term Monitoring Transient Electromagnetic Dataset

Juan Carlos Zamora‐Luria, Anders Vest Christiansen
Scientific Data
Geophysical and Geoelectrical Methods
article

A Unique Long-term Monitoring Transient Electromagnetic Dataset

Juan Carlos Zamora‐Luria, Anders Vest Christiansen
article en

Abstract

We present the first long-term monitoring dataset collected using a transient electromagnetic (TEM) instrument specifically designed for geophysical monitoring applications. The dataset consists of 762 measurements acquired between January 19<sup>th</sup>, 2023, and September 05<sup>th</sup>, 2025, from the island of Falster, southwestern part of Denmark, an area where saltwater intrusion is a recurring issue. The dataset displays a high consistency throughout the period, which is the foundation for a reliable interpretation over time. The dataset provides a valuable opportunity for researchers to test and validate advanced TEM inversion algorithms, including deterministic, Monte Carlo or probabilistic inversion algorithms, thereby contributing to the advancement of time-lapse studies using TEM methods.

Scientific DataVol. 13(1)
Aarhus University (DK)
Innovationsfonden
Openalex Percentile: Top 60%
Geophysical and Geoelectrical Methods
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