In this study, we apply three advanced methodologies—Dynamic Fourier Transform, Discriminant Analysis, and Clustering—to effectively discriminate between seismic signals generated by natural earthquakes and mining explosions in the Getchell Mine, Nevada, USA. By leveraging frequency-domain analysis, discriminant analysis, and clustering techniques, our approach identifies distinct characteristics of seismic signals, enabling precise classification. The Dynamic Fourier Transform reveals differences in frequency spectra, while Discriminant Analysis employs metrics like Kullback–Leibler Divergence and Chernoff Distance to distinguish event classes. Clustering using Partitioning Around Medoids (PAM) further confirms separability. Results demonstrate that the three methods effectively differentiate between seismic events, with potential applications in other geophysical and econophysics studies. Our findings highlight the methodologies’ utility for real-world applications, including disaster management and financial market analysis.