Seismic data compression using signal alignment and PCA

Principal Component Analysis (PCA) offers an optimal dimensionality reduction while maintaining the variances. A set of seismic traces data recorded by a sensor can be compressed by projecting the data to the Principal Components (PCs). The reconstruction error can be determined by choosing number of PCs. If the traces are aligned according to some references, number of PCs becomes fewer for the same preserved eigenvalues. Since the fewer PCs are required, compression ratio becomes higher and transmission cost from each sensor becomes smaller. Maximum amplitude and crosscorrelation techniques are evaluated to perform traces alignment. In the experiments, the aligned PCA achieves 12:1 compression ratio outperforming conventional PCA with 9.9:1 preserving approximately 99% of energy with reconstruction error 0.8% and 0.68%, respectively.