Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT

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 compres...

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Vydáno v:Algorithms Ročník 18; číslo 1; s. 33
Hlavní autoři: Lucena, Mateus Martinez de, Ribeiro, Josafat Leal, Wagner, Matheus, Fröhlich, Antônio Augusto
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.01.2025
Témata:
Algorithms
Case studies
Compressed gas
compression algorithms
Data acquisition
Data compression
Datasets
Decomposition
Discrete Wavelet Transform
Eigenvalues
Entropy
Error analysis
Frequency ranges
Industrial applications
Information storage and retrieval
Machine learning
Noise threshold
Performance evaluation
principal component analysis
Principal components analysis
Reconstruction
Seismic surveys
Time series
Wavelet analysis
Wavelet transforms
ISSN:1999-4893, 1999-4893
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Abstract 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 4% in the frequency ranges of 0–20 Hz, 15–65 Hz, and 60–105 Hz.
AbstractList 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 4% in the frequency ranges of 0–20 Hz, 15–65 Hz, and 60–105 Hz.
Audience Academic
Author Wagner, Matheus
Ribeiro, Josafat Leal
Lucena, Mateus Martinez de
Fröhlich, Antônio Augusto
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  surname: Fröhlich
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10.20944/preprints202308.0165.v1
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SubjectTerms Algorithms
Case studies
Compressed gas
compression algorithms
Data acquisition
Data compression
Datasets
Decomposition
Discrete Wavelet Transform
Eigenvalues
Entropy
Error analysis
Frequency ranges
Industrial applications
Information storage and retrieval
Machine learning
Noise threshold
Performance evaluation
principal component analysis
Principal components analysis
Reconstruction
Seismic surveys
Time series
Wavelet analysis
Wavelet transforms
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