Seismic Excitation Processing Using Different Wavelets: A Review

Abstract

Seismic excitation processing is essential for assessing and mitigating the effects of earthquakes and ground vibrations. Traditional methods like Fourier Transform (FT) and Short-Time Fourier Transform (STFT) are limited when analyzing non-stationary seismic signals, as they cannot simultaneously provide time and frequency localization. Wavelet Transform (WT) overcomes these limitations by decomposing signals across multiple scales, making it a powerful tool for seismic data analysis. This review delves into the mathematical framework of WT, emphasizing its capability to handle transient signals common in seismic events. Key wavelets such as Haar, Daubechies, Morlet, and Mexican Hat are explored in terms of their effectiveness in seismic signal denoising, event detection, and ground motion analysis. The paper also highlights the integration of WT with advanced techniques like machine learning and hybrid signal processing, enhancing seismic hazard analysis and real-time earthquake monitoring. Applications in earthquake early warning systems (EEWS) and structural health monitoring (SHM) are discussed, demonstrating WT’s versatility. Despite its benefits, WT faces challenges such as computational complexity, wavelet selection, and managing large seismic datasets. Recent advancements in adaptive wavelet design, cloud computing, and hybrid approaches show promise in addressing these challenges, paving the way for more accurate and efficient seismic analysis.