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Lin, Jingrong

University of British Columbia

Unexploded ordnances (UXOs) left on or below the ground after wars or military training are posed to be great threat to public safety. Signal processing for remediation of UXOs via is generally divided into two steps, detection and discrimination. Detection aims at detecting all potential UXO targets (including UXO and clutter (fragments of exploded ordnance)), while discrimination aims at separating UXOs out of clutters and classifying different types of UXOs. This thesis focuses on the first step, detection of potential UXO targets, and aims to replace the conventional initial targets picking procedure. The conventional potential UXO target detection method employs a threshold-based design, which identifies potential targets when response signal surpasses a specified threshold. This method is straightforward but fragile due to its limitations in information density and information fusion capability. To overcome these limitations and improve the detection accuracy, this thesis proposes using Machine Learning (ML)-based method for potential UXO target detection (referred to as targets for brevity). First, I introduce the novel design of training dataset, which takes different uncertainties of the problem into account, including different types of UXO and clutter, positional error and background noise. Second, I propose a ML-based network for target detection, which is composed of a convolution neural network to extract the texture feature of the training dataset and a linear transformation for latent feature classification. Third, I apply the proposed ML-based detection network to both synthetic and field data. The results from the proposed ML-based detection method compared with the conventional target picking method, demonstrate the effectiveness and robustness of the proposed methodology.

Text

2025-01-22

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/90186

Geophysics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/