Application of Electrical Resistivity Tomography (ERT) in Detecting Abandoned Mining Tunnels Along Expressway

The settlement and deformation of abandoned mining tunnels can lead to cracking, deformation, or even the collapse of surface structures. Recently, a dual-direction, four-lane expressway, designed a speed of 100 km/h, is planned to be constructed between Yuanling County and Chenxi County. This expressway will pass through a long-abandoned refractory clay mining area in Chenxi County. This study focuses on this abandoned mining area and employs the Electrical Resistivity Tomography (ERT) method to investigate the underground conditions, aiming to determine the location and scale of the subterranean goaf. A total of five survey lines were deployed for the investigation. The inversion results indicate the presence of five low-resistivity anomalies in the underground structure (with six low-resistivity anomalies identified along line L1). These low-resistivity anomalies are preliminarily interpreted as subsurface cavities. Subsequent borehole verification revealed that the five low-resistivity anomalies correspond to a total of eight water-filled cavities, including six abandoned mining tunnels and two karst caves. At the location K33+260~K33+350, a large low-resistivity anomaly was identified which actually consisted of three closely spaced water-filled abandoned mining tunnels. Additionally, the surrounding strata primarily consisted of fractured mudstone, which has a high water content and thus exhibits low resistivity. These two factors combined resulted in the three water-filled abandoned mining tunnels appearing as a single large low-resistivity anomaly in the inversion profile. Meanwhile, at K33+50~K33+110, two water-filled abandoned mining tunnels were found. These tunnels are far apart along line L1 but are relatively close to each other on the other four survey lines. Consequently, in the inversion results, line L1 displays these as two separate low-resistivity anomalies, while the other four survey lines show them as a single large low-resistivity anomaly. Based on the 2D inversion results, a 3D model of the study area was constructed. This model provides a more intuitive visualization of the underground cavity structures in the study area. The findings not only serve as a reference for the subsequent remediation of the goaf area but also offer new insights into the detection of abandoned mining tunnels.

Authors

Institutions

Publication Details

Journal
Applied Sciences
Published
2025-02-20
DOI
https://doi.org/10.3390/app15052289
Citations
3
Primary Topic
Geophysical Methods and Applications
Type
article
Field-Weighted Citation Impact
3.44

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Application of Electrical Resistivity Tomography (ERT) in Detecting Abandoned Mining Tunnels Along Expressway

Rong Liu, Tongsheng Li, Chuanghua Cao, Mengyu Sun et al.
3 citations
Applied Sciences
Geophysical Methods and Applications
3.44
article

Application of Electrical Resistivity Tomography (ERT) in Detecting Abandoned Mining Tunnels Along Expressway

Rong Liu, Tongsheng Li, Chuanghua Cao, Mengyu Sun, Jian Ou
article en
3 citations

Abstract

The settlement and deformation of abandoned mining tunnels can lead to cracking, deformation, or even the collapse of surface structures. Recently, a dual-direction, four-lane expressway, designed a speed of 100 km/h, is planned to be constructed between Yuanling County and Chenxi County. This expressway will pass through a long-abandoned refractory clay mining area in Chenxi County. This study focuses on this abandoned mining area and employs the Electrical Resistivity Tomography (ERT) method to investigate the underground conditions, aiming to determine the location and scale of the subterranean goaf. A total of five survey lines were deployed for the investigation. The inversion results indicate the presence of five low-resistivity anomalies in the underground structure (with six low-resistivity anomalies identified along line L1). These low-resistivity anomalies are preliminarily interpreted as subsurface cavities. Subsequent borehole verification revealed that the five low-resistivity anomalies correspond to a total of eight water-filled cavities, including six abandoned mining tunnels and two karst caves. At the location K33+260~K33+350, a large low-resistivity anomaly was identified which actually consisted of three closely spaced water-filled abandoned mining tunnels. Additionally, the surrounding strata primarily consisted of fractured mudstone, which has a high water content and thus exhibits low resistivity. These two factors combined resulted in the three water-filled abandoned mining tunnels appearing as a single large low-resistivity anomaly in the inversion profile. Meanwhile, at K33+50~K33+110, two water-filled abandoned mining tunnels were found. These tunnels are far apart along line L1 but are relatively close to each other on the other four survey lines. Consequently, in the inversion results, line L1 displays these as two separate low-resistivity anomalies, while the other four survey lines show them as a single large low-resistivity anomaly. Based on the 2D inversion results, a 3D model of the study area was constructed. This model provides a more intuitive visualization of the underground cavity structures in the study area. The findings not only serve as a reference for the subsequent remediation of the goaf area but also offer new insights into the detection of abandoned mining tunnels.

Applied SciencesVol. 15(5)
Central South University (CN), Shanghai Institute of Geological Survey (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 9%
Geophysical Methods and Applications
3.44
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.