MATLAB Implementation for Seismic Data Processing

In seismic data processing, the Wiggle transform serves as a time-frequency analysis method widely used to examine how seismic signal frequency characteristics evolve over time. This transformation enables clearer visualization of frequency distribution patterns at different temporal segments of seismic waves, providing critical support for seismic data interpretation and subsequent processing.

The fundamental approach of the Wiggle transform involves decomposing seismic signals into time-frequency representations, typically implemented through Short-Time Fourier Transform (STFT) or Continuous Wavelet Transform (CWT). MATLAB, with its robust Signal Processing Toolbox and flexible programming capabilities, proves to be an ideal platform for implementing this transformation.

In MATLAB, the implementation workflow for Wiggle transform primarily includes: Data Loading and Preprocessing: Reading seismic data files (e.g., using `readtable` or `load`), followed by potential noise reduction (e.g., wavelet denoising) and normalization (e.g., `zscore` function). Time-Frequency Analysis: Computing time-frequency representations using `spectrogram` for STFT-based analysis (with customizable window length and overlap) or `cwt` for wavelet-based analysis (selecting appropriate wavelet families like 'morse' or 'amor'). Wiggle Plot Visualization: Generating time-frequency plots using `imagesc` or `contourf` functions, with axis adjustments (e.g., `axis tight`) to match seismic data display conventions. Post-Processing and Interpretation: Enhancing colormaps (`colormap jet`), contrast adjustments (`brighten`), and emphasizing valid signal features through thresholding techniques for improved analytical outcomes.

This technique finds extensive applications in seismic exploration and microseismic monitoring, assisting researchers in identifying formation reflection characteristics or anomalous signal patterns through quantitative time-frequency analysis.