Description:

Ground Penetrating Radar (GPR) has been an
essential nondestructive geophysical tool in Saskatchewan's potash
mines for over six decades. This innovative technology, used in
conjunction with an active boring machine, facilitates real-time
data collection, particularly for imaging the immediate clay seam
(414-clay seam) above the mine roof. Its reliability is demonstrated
by the accuracy with which the roof beam thickness (mine roof to
414-clay seam) is interpreted in real-time, crucial for making
informed safety decisions during mining operations. The
imperative for a robust auto-picking algorithm tailored to handle
complexities in potash mine GPR data is emphasized.
The previously developed algorithm called the Clustered Ratio
Derivative (CRD) algorithm, employing unsupervised machine
learning for real-time GPR interpretation showcased promising
results. However, the CRD algorithm faces limitations due to
potential sensitivity to variations in input data, especially in the
presence of noise or anomalies. Geological variations, such as the
presence of "stray clays" within the roof beam, pose challenges to
the algorithm's performance and accuracy.
In response to these challenges, this paper proposes a novel
deep learning-based algorithm leveraging two distinct
Convolutional Neural Network (CNN) architectures. These CNNs
are designed to navigate the intricacies of the GPR data pattern
specific to potash mines. The presented results indicate promising
levels of accuracy, with the new method achieving 95.4% accuracy
in detecting the 414-clay seam and an average of 88% accuracy in
finding “stray clays” seen in the dataset when compared to a
geophysicist interpretation. The overall analysis suggests that this
new approach has the potential to detect mining room roof
features with a high degree of accuracy.

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Dr. Raman Paranjape

Biography