APPLICATION AND IMPROVEMENT STRATEGIES OF THE SGT MODEL IN MAGNETIC SIGNAL ANOMALY DETECTION

Abstract: This report explores the application of the SGT model in the field of magnetic prospecting, with a special focus on its performance on the MGT, SNR0 and SNR5 datasets. The experimental results reveal that the SGT model suffers from high false alarm rate and large prediction bias when dealing with these datasets. To address the insufficient predictive and generalization abilities of the model, we designed a series of improvement experiments focusing on three aspects, namely, tuning parameter, optimizing the feature extraction method and modifying the continuity judgment.
Among these three improvement methods, tuning parameter achieved about 0.5% performance improvement, and the methods of feature extraction optimization and orthogonal basis judgment instead reduced the prediction effect by 20%. Through code review and logical reasoning, we found that the problem stems from feature extraction incompatibility with the model. In order to adapt to the orthogonal basis algorithm, we propose an improvement idea: introduce many different types of features, including time-domain features, frequency-domain features, and statistical features, etc., and comprehensively utilize the information of these features to construct a more complex and comprehensive SGT model. In addition, the stacking module is introduced to take the prediction results of a single model based on different features as inputs, and generate a more accurate ultimate prediction through further learning and synthesis.