Gravity Gradient Vector Field Pairs for Tunnel Detection
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Solution Overview
Problem
Current technologies for detecting underground tunnels face challenges such as excessive clutter, signal loss, and high false positives/negatives due to soil/rock inhomogeneities, leading to unreliable and time-consuming interpretations.
Innovation Solution
A method involving the calculation of gravity gradient vector field pairs and probability tomography analysis to estimate source pole occurrence probability, reducing clutter and enhancing tunnel signature detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional seismic-acoustic or electromagnetic methods are used with boreholes, then detection capability is improved, but time and expense increase
Solution Approach 1:
The patent extracts the detection capability from the borehole requirement by using satellite-based gravimetric measurements. Instead of placing sensors in boreholes, the system uses satellite gravity data to detect subsurface voids, eliminating the time-consuming borehole drilling and sensor deployment while maintaining detection capability.
Solution Approach 2:
The patent introduces satellite gravimetric measurements as an intermediary between surface observations and subsurface tunnel detection. The satellite gravity data serves as a mediator that indirectly reveals tunnel locations without requiring direct contact with the subsurface through boreholes.
2Loss of time
If traditional radar or gravity methods are used without boreholes, then time and expense are reduced, but clutter and false positives increase
Solution Approach 1:
The patent segments the gravity gradient tensor into distinct components (vertical, radial, and tangential gradients) and processes each component separately. This segmentation allows the system to identify unique gravitational fingerprints of tunnels versus other subsurface features, reducing clutter and false positives while maintaining rapid satellite-based detection.
Solution Approach 2:
The patent applies local quality analysis by examining the specific spatial distribution and orientation of gravity gradient components at each measurement location. By analyzing the local gravitational field characteristics and comparing them against known tunnel signatures, the system distinguishes actual tunnels from other subsurface anomalies with high reliability.
3Measurement precision
If visual inspection by trained analysts is used, then interpretation is performed, but processing speed decreases and false positives increase
Solution Approach 1:
The patent implements self-service by developing an automated algorithmic system that performs the interpretation function traditionally requiring human analysts. The system uses computational methods to automatically process satellite gravity data, identify tunnel signatures, and generate detection results without human intervention, achieving both high accuracy and rapid processing speeds.
Solution Approach 2:
The patent replaces the mechanical system of human visual inspection with an automated computational system. Instead of relying on human analysts to visually interpret gravity data, the system uses algorithmic processing of gravity gradient components to automatically detect and classify subsurface features, dramatically increasing processing speed while maintaining or improving accuracy.
4Measurement precision
If signal processing is performed on raw gravity data, then detection sensitivity is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary action by calculating the gravity gradient tensor components before conducting the actual tunnel detection analysis. By pre-computing the vertical, radial, and tangential gravity gradients from satellite data, the system prepares processed information that simplifies subsequent tunnel identification and reduces the complexity of the final detection algorithm.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces clutter, improves detection speed, and provides a more reliable and recognizable tunnel signature, distinguishing actual tunnels from surface features.
Implementation Method 1
a method for detecting underground tunnels is presented having a series of steps that comprises first calculating a plurality of gravity gradient vector field pairs for an area
Data Source
AI summary
A method for detecting tunnels within an area comprising in one embodiment the steps of providing horizontal tensor gravity gradients for the area, calculating a plurality of gravity gradient vector field pairs from the horizontal tensor gravity gradients, each gravity gradient vector field pair including one gravity gradient vector field and a respective orthogonal gravity gradient vector field, detecting dipolar points and polar source points in each of the one and respective orthogonal gravity gradient vector fields for each gravity gradient vector field pair, identifying the polar source points that are present in one but not in both of the one gravity gradient vector field and the respective orthogonal gravity gradient vector field for each gravity gradient vector field pair to identify endpoints, and distinguishing the endpoints resulting from surface features from the endpoints resulting from tunnels. The polar source points form the endpoints of the tunnels.