Geophysical-Machine Learning Tool for Continuous Subsurface Geomaterials Characterization
- Research
Researchers utilize 2D electrical resistivity imaging and borehole data to estimate the N60-value of soils with k-means clustering technique
Traditional geotechnical investigations provide data only at discrete borehole locations, leaving vast areas uncharacterized. This spatial gap often leads to unforeseen ground conditions during construction, causing costly delays, design modifications, and occasionally catastrophic failures. Now, a novel integrated geophysical-machine learning approach, using k-means clustering technique, by a team of researchers from Shibaura Institute of Technology provides continuous subsurface characterization, enabling evidence-based decision-making throughout project lifecycles.
Title: 2D ERI integrated with drilling data
Thailand's northern regions, characterized by complex geology and active fault systems, experience frequent landslides that threaten both lives and critical infrastructure. In 2022, a slope failure occurred along Highway No. 1088 in Chiang Mai Province, northern Thailand. When a research team led by Professor Shinya Inazumi from Shibaura Institute of Technology conducted geotechnical investigations to determine the collapse's cause, they encountered a critical limitation. While 2D electrical resistivity imaging covered the entire slope area, borehole data were available only along certain survey lines, leaving substantial portions of the site uncharacterized. This gap created significant uncertainties in identifying soil layer boundaries, distinguishing different geomaterials, and assessing relative soil stiffness across the failure zone. The team recognized that while drilling provides precise data at specific points, its high cost and time requirements make comprehensive site coverage impractical, particularly in challenging mountainous terrain.
In this regard, advances in machine learning offer new possibilities for pattern recognition in geophysical data.
Now, the team presents a breakthrough methodology that significantly reduces the need for expensive and time-consuming borehole drilling in geotechnical investigations. Their findings were made available online and have been published in the Journal of Rock Mechanics and Geotechnical Engineering on 26 September 2025.
By combining 2D electrical resistivity imaging with machine learning, specifically k-means clustering analysis, this study successfully estimates soil strength parameters (N60-values) across large areas using data from just four strategically placed boreholes. The key innovation lies in developing a robust power equation (R2 = 0.9467) that correlates subsurface electrical resistivity with soil stiffness, validated with high accuracy (MAE = 3.94, RMSE = 5.21).
“Notably, our machine learning algorithm successfully classified subsurface materials into three distinct competency levels: low-competency materials such as loose sand, moderate-competency materials such as medium-stiff clay, and high-competency materials such as stiff-hard clay,” remarks Prof. Inazumi.
The proposed approach dramatically reduces the number of required boreholes while maintaining reliable subsurface characterization, as well as minimizing ground disturbance, surface degradation, and groundwater contamination risks associated with extensive drilling. Moreover, it provides continuous subsurface information across entire study areas, not just discrete borehole points, enabling comprehensive identification of weak zones for landslide mitigation and slope stability analysis. This integrated geophysical-geotechnical-machine learning framework offers a scalable, practical solution for foundation design, slope stability assessments, and infrastructure planning, particularly valuable in challenging terrains where traditional drilling is difficult or impractical.
Prof. Inazumi highlights the potential real-life applications of their work. “Highway departments can assess soil stability along roadways and identify weak zones prone to slope failure, as demonstrated in this study's investigation of Highway No. 1088 in northern
Thailand following a 2022 landslide. Construction companies can optimize foundation designs by mapping subsurface soil strength variations across building sites, reducing overdesign in strong soil areas while ensuring adequate support in weaker zones.”
Furthermore, this work can enable government agencies to conduct comprehensive landslide hazard mapping in mountainous regions, identifying vulnerable slopes for proactive stabilization measures. It can also aid engineers in assessing seepage pathways and embankment integrity in existing dams without compromising structural stability through excessive drilling, as well as help municipal planners in conducting preliminary site investigations across large development areas cost-effectively, prioritizing detailed drilling only in critical locations identified through resistivity surveys.
Lastly, emergency management agencies can create regional subsurface strength maps for
Overall, the development of an integrated framework that can extend limited borehole data across entire investigation areas is expected to make comprehensive subsurface characterization both feasible and affordable.
Reference
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Title of original paper: |
Using 2D electrical resistivity imaging and borehole data to estimate N60-value of soils with k-means clustering for subsurface geomaterials categorization |
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Journal: |
Journal of Rock Mechanics and Geotechnical Engineering |
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DOI: |
10.1016/j.jrmge.2025.05.030 |
Additional infotmation for EurekAlert
| Latest Article Publication Date: | 26 September 2025 |
| Method of Research: |
Computational simulation/modeling |
| Subject of Research: | Not Applicable |
| Conflicts of Interest Statement: | The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
Authors
About Professor Shinya Inazumi from SIT, Japan
Professor Shinya Inazumi is a distinguished professor at the College of Engineering, Shibaura Institute of Technology (SIT), Japan. He earned his Doctor of Engineering degree from Kyoto University in 2003. Renowned for his contributions to geotechnical and geo-disaster engineering, his research spans social infrastructure engineering, geo-information studies, and disaster mitigation. Prof. Inazumi has authored over 300 scholarly publications and actively serves on numerous academic and professional committees. His pioneering work has earned him multiple awards, recognizing his leadership and innovation in the field.
Funding Information
Not Available