MATLAB Code Implementation for Seismic Data Processing

Seismic data processing involves multiple sequential steps implemented through specialized algorithms. First, velocity analysis is performed using techniques like semblance calculation or stacking velocity analysis to accurately interpret seismic data by determining the propagation speed of seismic waves through subsurface formations. Second, diffraction analysis is conducted through migration algorithms (such as Kirchhoff migration) to detect wave reflections, refractions, and propagation directions within the Earth's interior, typically implemented using finite-difference or wave-equation methods. Additionally, gain adjustment is necessary through amplitude compensation functions (like automatic gain control or time-variant gain) to modify signal strength and emphasize the most significant seismic signals while maintaining data integrity. The entire processing workflow often utilizes MATLAB's signal processing toolbox and parallel computing capabilities for efficient implementation. In summary, seismic data processing represents a complex procedure requiring precise technical implementation and specialized geophysical knowledge, commonly structured through modular MATLAB functions for velocity analysis, diffraction processing, and amplitude optimization.