Abstract
This work aims to develop approaches to processing and interpreting spectral entropy outcomes in the context of seismic data, as well as to establish a methodological foundation for subsequent integration into practical monitoring solutions. The objective of this study is to evaluate the effectiveness of the Shannon spectral entropy method in detecting and assessing short-term seismic events through a seismogram analysis. This method has demonstrated sensitivity to variations in the spectral characteristics of the registered signals. A threshold value for the increase in spectral entropy information has been pinpointed for reliable P-wave detection. The results could be applied in real-time automated seismic monitoring systems. In addition to the conventional spectral analysis techniques, the proposed methodology may serve as the input to the neural network models used in seismological applications.