Seismic Signal Analysis Using Empirical Wavelet Transform for Moving Ground Target Detection and Classification

Abstract

In this article, we investigate the potential of empirical wavelet transform (EWT) as a moving ground target detection and classification technique using seismic signal modality. EWT gives the opportunity of adaptive wavelet technique to construct basis functions based on the information contained in the seismic signal. A seismic dataset is created by recording the seismic signature of moving ground targets, i.e., bus and tractor using an array of geophones. Statistical features computed using EWT based time-frequency coefficients, and subsequently, SVM and KNN classify the moving ground targets on the basis of these features. The performance of the proposed technique is analyzed using the parameters: accuracy, true positive rate (TPR), and AUC (Area under the curve) -ROC (Receiver Operating Characteristics), and subsequently, the comparison performed with an existing technique, i.e., STFT. The results are satisfactory with accuracy and TPR of the order of 90% and AUC ~ 95% for classification results between bus and noise. Similarly, for classification between tractor and noise, the accuracy, TPR, and AUC are 84%, 82%, and 90%, respectively. The performance of the EWT for ground target detection and classification is also presented in terms of the F-Score and confusion matrix. The classification using EWT and SVM as a classifier provides F-Score as 78%, 67%, and 86% for bus, tractor, and noise, respectively, which is having the average relative enhancement of about ~8% in comparison with STFT based technique.