From Tremors to Blasts: MACREE’s Modern Lens on Earth’s Movements

Abstract

Seismic event classification, especially differentiating between natural earthquakes and human-made explosions, remains a critical task in geophysics, disaster management, and international security. Traditional seismic monitoring systems often struggle to accurately distinguish between these two types of events due to their similar seismic wave signatures. To address these challenges, this paper presents MACREE (Modular Analysis for Classification and Refined Event Evaluation), a novel framework designed to enhance the accuracy of seismic event classification using advanced signal processing and machine learning techniques. MACREE combines adaptive preprocessing, time-frequency analysis, and feature extraction to refine the raw seismic data, followed by a robust machine learning classification engine that improves event detection accuracy. The system’s modular design allows it to effectively analyze a wide variety of seismic events in real-time, reducing false positives and improving classification reliability. The paper discusses MACREE's architecture, algorithms, and performance evaluations, highlighting its potential to revolutionize seismic event monitoring in a range of applications, from earthquake detection to nuclear test verification.