Numerical Modelling of Ground Penetrating Radar for Potash Mine Safety

Summary This paper presents a software tool which simulates the geological stratigraphy of a potash mine which is then used with gprMax (public domain Ground Penetrating Radar (GPR) simulation software) to examine and evaluate the effectiveness of auto-picking algorithms. The system is used to simulate the GPR response from clay seams in the roof of potash mining rooms. As it is extremely onerous to obtain in-situ data that captures all possible normal and anomalous geological conditions present in the mine roof, earth models are generated which accurately represents the geology of the mine. In particular, random clays in the mine roof can negatively affect the performance of auto-picking algorithms. These earth model simulations can be used to present these random clays accurately. gprMax is an open source software that simulates Electro-Magnetic (EM) wave propagation in materials in order to support a better understanding of the use of GPR in various applications. Currently, GPR systems are in use in potash mines to assist with monitoring of the roof status of mining rooms. The goal of this paper is to validate the ability of using gprMax with effective earth models to generate realistic GPR signals that are used to test and evaluate auto-picking algorithms. The use of simulated data in comparison to the experimental (actual physical) data and generation of test bed models for an auto-picking algorithm has many benefits. Synthetic data is generated by gprMax using the Finite Difference Time Domain (FDTD) methodology. An effective methodology to develop and test robust auto-picking algorithms is created using simulated GPR signals because the ground truth is known from the earth models. Additionally, in this work results from both an industry standard auto-picking algorithm and a newly developed auto-picking algorithm, called Clustered Ratio Derivative (CRD), are presented for this mine roof monitoring application. Finally, in this work we take advantage of cloud computing resources in order to execute this work.