Near-surface Interface Detection for Coal Mining Applications using Bispectral Features and GPR

Abstract

The use of ground penetrating radar (GPR) for detecting the presence of near-surface interfaces is a scenario of special interest to the underground coal mining industry. The problem is difficult to solve in practice because the radar echo from the near-surface interface is often dominated by unwanted components such as antenna crosstalk and ringing, ground-bounce effects, clutter, and severe attenuation. These nuisance components are also highly sensitive to subtle variations in ground conditions, rendering the application of standard signal pre-processing techniques such as background subtraction largely ineffective in the unsupervised case. As a solution to this detection problem, we develop a novel pattern recognition-based algorithm which utilizes a neural network to classify features derived from the bispectrum of 1D early time radar data. The binary classifier is used to decide between two key cases, namely whether an interface is within, for example, 5 cm of the surface or not. This go/no-go detection capability is highly valuable for underground coal mining operations, such as longwall mining, where the need to leave a remnant coal section is essential for geological stability. The classifier was trained and tested using real GPR data with ground truth measurements. The real data was acquired from a testbed with coalclay, coal-shale and shale-clay interfaces, which represents a test mine site. We show that, unlike traditional second order correlation based methods such as matched filtering which can fail even in known conditions, the new method reliably allows the detection of interfaces using GPR to be applied in the near-surface region. In this work, we are not addressing the problem of depth estimation, rather confining ourselves to detecting an interface within a particular depth range. KEY WORDSGPR, bispectrum, interface detection, horizon control, coal mining.

Automated coal seam detection using a modulated specific energy measure in a monitor-while-drilling context

“…A detailed survey of related topics is beyond the scope of this paper. To investigate further, interested readers are referred to [24,25] on the application of geophysics techniques to coal mining, including the use of seismic reflection and electromagnetic measurements; and [26] on using probabilistic graphical models to learn 3D geological structures from drill data.…”

Coal seam thickness estimation using GPR and higher order statistics - the near-surface case

“…The ground was modeled as two layers where the top and bottom layers were coal and shale respectively. The electrical parameters (permittivity and conductivity) used in the FDTD algorithm for these layers were measured from real coal and shale samples [1]. In each simulation, the upper layer had a constant…”

“…The noise power was timevarying in nature with a peak of 0.2 mW and zero mean. These noise statistics were measured from the real GPR system adapted for underground coal mining applications described in [1] and [5].…”

“…In [1], GPR was used to detect coal seam interfaces within 5 cm. In this paper, the interface detection task is adapted to estimate coal layer thickness within the near-surface range of 1-6 cm.…”

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ABSTRACTA novel pattern recognition-based approach to detect near-surface interfaces using ground penetrating radar (GPR) has been reported in [1]. The approach was used to successfully detect interfaces within 5 cm of the ground surface.

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