Deconvolution Program for Seismic Exploration Data Processing

Deconvolution is a widely used signal processing technique in seismic exploration data processing, primarily employed to enhance the resolution of seismic records. Its core objective is to mathematically eliminate various effects seismic waves undergo during propagation, thereby restoring the true distribution of subsurface reflection coefficients.

In seismic exploration, seismic waves emitted from the source are reflected by different subsurface strata and recorded by receivers. Due to factors such as stratigraphic absorption effects, multiple reflections, and instrument responses, raw seismic records often exhibit low resolution, making them difficult to interpret directly for geological analysis. Deconvolution techniques address this by establishing mathematical models of seismic records and employing methods like inverse filtering, least squares optimization, or sparse constraints to mitigate these interfering factors, resulting in clearer reflection signals. Key algorithm implementations may involve designing inverse filters using Wiener deconvolution or applying L1-norm regularization for sparse spike deconvolution.

MATLAB serves as an ideal platform for implementing deconvolution algorithms, leveraging its robust matrix computation capabilities and comprehensive signal processing toolbox for efficient execution. A typical implementation workflow includes: reading seismic record data (using functions like readtable or load), estimating seismic wavelets (via autocorrelation methods or homomorphic deconvolution), designing deconvolution operators (with deconvwnr for Wiener deconvolution or custom iterative solvers), applying operators to process data, and visualizing results (using plot or imagesc). The accuracy of wavelet estimation critically influences deconvolution outcomes, where common approaches like autocorrelation analysis can be coded using xcorr functions, while homomorphic techniques employ logarithmic transformations and cepstral analysis.

Through deconvolution processing, the vertical resolution of seismic profiles is significantly improved, enabling clearer visualization of thin layers and subtle geological features. This provides a more reliable data foundation for subsequent reservoir prediction and hydrocarbon detection, with MATLAB scripts allowing parameter tuning (e.g., operator length, regularization weights) to optimize results for specific geological conditions.