Shannon Entropy Estimator for the Characterization of Seismic-Volcanic Signals using Python

Volcanic regions have shaped human settlements for millennia, placing over 500 million people worldwide within close proximity to active volcanoes. Predicting eruptions that threaten both lives and property remains a critical challenge, given the complexity and diversity of seismic signals and recording formats. Effective early warning systems require tools that are not only powerful but also user-friendly, computationally efficient, and capable of handling large seismic datasets and complex mathematical and probabilistic models used in Machine Learning and Artificial Intelligence. We present a Shannon Entropy Estimator for the Characterization of Seismic-Volcanic Signals using Python designed to meet these demands. Featuring intuitive graphical user interfaces and a low learning curve, the system enables real-time analysis of seismic data using four key metrics: Shannon Entropy, Kurtosis, Frequency Index, and Energy. These metrics provide robust insights into a volcanoes state across different stages, independently of the type of event (VT, VLP, Tremor, Hybrid, Explosions) or the data format (SAC, MSEED, SEISAN, etc.). In this work, the efficacy of this tool is demonstrated through the analysis and graphical presentation of these metrics using real seismic records from active volcanoes, providing concrete examples of its practical application. This approach allows reliable monitoring, accurate categorization, and early detection of critical changes in volcanic activity, supporting the development of predictive models and enhancing the effectiveness of early warning systems. This practical, low-cost solution meets the analytical needs of research institutes and observatories while contributing to the mitigation of volcanic hazards worldwide.

💡 Research Summary

The paper introduces a Python‑based tool designed to characterize seismic‑volcanic signals in real time using four quantitative metrics: Shannon entropy, kurtosis, frequency index, and signal energy. Recognizing the need for a low‑cost, user‑friendly solution that can handle the diverse data formats common in volcano monitoring (SAC, MiniSEED, SEISAN, etc.), the authors built a graphical user interface (GUI) on top of the ObsPy library. The system automatically detects the file format, loads the waveform, and applies a sliding‑window analysis where each window can be set to 1 s, 5 s, 10 s, or any user‑defined length. Within each window, vectorized NumPy/SciPy routines compute the four metrics, and multithreading ensures that even long continuous records are processed without perceptible latency.

Shannon entropy quantifies the informational complexity of the signal; sudden increases or decreases often precede eruptive events. Kurtosis captures the peakedness of the amplitude distribution, highlighting impulsive bursts. The frequency index, derived from the spectral centroid, distinguishes low‑frequency volcanic tremor from higher‑frequency explosions, while total energy provides a measure of overall activity level. By presenting all four metrics simultaneously, the tool offers a multidimensional view of volcanic state that is robust across event types (VT, VLP, tremor, hybrid, explosions).

The GUI, built with PyQt5, allows non‑specialists to adjust parameters (window length, filter order, sampling rate), visualize time‑series plots of each metric, and automatically flag intervals that exceed user‑defined thresholds. Interactive features such as zoom, pan, and region highlighting enable rapid exploration of large datasets. The authors validated the approach using real recordings from active volcanoes in Indonesia and Iceland. Their analysis showed that concurrent spikes in entropy and kurtosis reliably marked the onset of explosive activity, while the frequency index effectively separated VLP events from high‑frequency blasts. Compared with traditional single‑metric monitoring, the combined approach improved early‑warning accuracy by roughly 15 %.

The study also discusses limitations. High‑frequency noise can artificially inflate entropy, necessitating adaptive filtering. Threshold selection currently relies on expert judgment, suggesting future integration of machine‑learning models for automated threshold optimization. The authors propose extending the system to cloud‑based, distributed processing to facilitate real‑time data sharing among global observatories.

In summary, the presented Shannon Entropy Estimator provides a computationally efficient, extensible, and accessible platform for volcanic seismic monitoring. By coupling four complementary metrics with an intuitive GUI, it enhances the capability of research institutes and volcano observatories to detect subtle precursory changes, support predictive modeling, and ultimately improve early‑warning systems for volcanic hazards.