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Related Experiment Video

Updated: May 16, 2026

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

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Subsurface event detection and classification using Wireless Signal Networks.

Suk-Un Yoon¹, Ehsan Ghazanfari, Liang Cheng

¹Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA. suy309@lehigh.edu

Sensors (Basel, Switzerland)

|December 4, 2012

Summary

This summary is machine-generated.

Wireless Signal Networks (WSiNs) utilize underground sensor variations in radio signal strength to detect and classify subsurface environmental changes like water intrusion or landslides. This novel approach enables effective monitoring of subterranean events.

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Last Updated: May 16, 2026

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In Situ Soil Moisture Sensors in Undisturbed Soils

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

Area of Science:

Geophysics
Wireless Sensor Networks
Environmental Monitoring

Background:

Subsurface environments pose challenges for traditional sensing methods.
Changes in soil properties due to events like landslides or water intrusion are difficult to detect.
Wireless Signal Networks (WSiNs) offer a novel approach for subsurface monitoring.

Purpose of the Study:

To investigate the impact of soil properties on radio propagation for subsurface communication.
To develop and evaluate a WSiN-based system for detecting and classifying subsurface geo-events.
To propose a robust classification method for analyzing wireless signal strength variations.

Main Methods:

Detailed experimental analysis of soil properties affecting radio propagation underground.
Deployment of actual underground sensor nodes to form WSiNs.
Development of a window-based minimum distance classifier using Bayesian decision theory for event detection and classification.
Laboratory experiments, including a water leakage test, to validate the proposed methods.

Main Results:

Calibrated wireless signal strength variations were demonstrated as reliable indicators of subsurface environmental changes.
The proposed WSiN concept effectively detected and classified subsurface events such as water intrusion and relative motion.
The window-based classifier successfully identified and categorized geo-events in experimental settings.

Conclusions:

WSiNs provide a viable and effective method for subsurface environment sensing and monitoring.
The developed classification algorithm accurately detects and classifies geo-events based on wireless signal strength.
This technology has significant potential for applications like landslide and water intrusion detection.