Combining PCA and DCT for Improved Passive Seismic Data Compression

Abstract

The seismic surveys conducted by the oil and gas sector result in very large datasets, often exceeding terabytes of data, leading to high costs and technical challenges regarding storage and transmission of such large quantities of data. Therefore, data compression is crucial to address the challenges related to communication and storage demands. This paper investigates a compression strategy based on the combined application of the Discrete Cosine Transform and Principal Component Analysis and its ability to achieve higher compression ratios than the application of each of those methods alone. A theoretical motivation for the increased compression performance is presented, emphasizing that the application of Principal Component Analysis to signals in the transformed domain make the resulting signal more suitable for the posterior Thresholding, Quantization and Entropy Encoding steps. The proposed method was evaluated using a dataset containing passive seismic data collected during an oil and gas reservoir monitoring survey, achieving compression ratios of up to 1000:1 with a normalized reconstruction residue energy of less than 20%, relative to the original signal energy, for the more relevant frequency range between 0 and 20 Hz, outperforming other seismic data compression strategies considered in the literature.

Keywords

• Data compression
• discrete cosine transform
• passive seismic data
• permanent reservoir monitoring
• principal component analysis
• seismic data compression