MATLAB-Based Seismic Processing Software

MATLAB-based seismic processing software plays a crucial role in geophysical research and education, particularly tools developed by research institutions such as the University of Alberta, which often integrate theoretical research with practical application requirements. These software solutions are typically used for processing seismic data, including tasks such as data acquisition, noise filtering, signal enhancement, and phase identification.

In geophysics research, MATLAB serves as an ideal platform due to its powerful matrix operations and rich toolboxes (e.g., Signal Processing Toolbox). Such seismic processing software generally includes the following core functions:

Data Preprocessing: Implementing denoising, filtering, or baseline correction on raw seismic waveforms to improve the accuracy of subsequent analyses, often utilizing MATLAB's built-in functions like filtfilt for zero-phase filtering or wavelet denoising algorithms.

Phase Picking: Automatically or semi-automatically identifying P-wave and S-wave arrival times, which is essential for earthquake localization and focal mechanism analysis. This may involve algorithms such as short-term average/long-term average (STA/LTA) triggers or machine learning approaches implemented via MATLAB's Classification Learner app.

Spectral Analysis: Studying frequency-domain characteristics of seismic signals through Fourier transforms or wavelet transforms, helping identify frequency features of different seismic events using functions like fft for Fast Fourier Transform or cwt for Continuous Wavelet Transform.

Visualization Tools: Providing waveform comparison, profile plots, or 3D imaging to help researchers intuitively understand underground structures or source characteristics, leveraging MATLAB's advanced plotting capabilities through functions like plot3 for three-dimensional visualizations and subplot for multi-panel displays.

Additionally, such software may support data interaction with other geophysical software (e.g., SAC or ObsPy) or possess scripting extension capabilities, enabling users to customize algorithm workflows according to specific needs. For students and researchers, this serves not only as a data processing tool but also as a practical platform for learning and validating seismological theories through hands-on code implementation.