Global PCA for In-Field Compression of Seismic Data Acquisition

This paper presents a new global approach by using the Principal Component Analysis (PCA) to compress seismic data. The proposed method is based on the idea that a global covariance matrix can be derived from local statistics of the data at each sensor. A numerical simulation is implemented to demonstrate the performance of the global PCA technique, which reaches a compression ratio 10 times higher than the traditional local PCA technique.