Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT - Mateus Martinez de Lucena; Josafat Leal Ribeiro; Matheus Wagner; Antônio Augusto Fröhlich (Algorithms)
Industrial application data acquisition systems can be sources of vast amounts of data. The seismic surveys conducted by oil and gas companies result in enormous datasets, often exceeding terabytes of data. The storage and communication demands these data require can only be achieved through compression. Careful consideration must be given to minimize the reconstruction error of compressed data caused by lossy compression. This paper investigates the combination of principal component analysis (PCA), discrete wavelet transform (DWT), thresholding, quantization, and entropy encoding to compress such datasets. The proposed method is a lossy compression algorithm tuned by evaluating the reconstruction error in frequency ranges of interest, namely 0–20 Hz and 15–65 Hz. The PCA compression and decompression acts as a noise filter while the DWT drives the compression. The proposed method can be tuned through threshold and quantization percentages and the number of principal components to achieve compression rates of up to 31:1 with reconstruction residues energy of less than <semantics>4%</semantics> in the frequency ranges of 0–20 Hz, 15–65 Hz, and 60–105 Hz.