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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2025-01-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/1/33 |
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| author | Mateus Martinez de Lucena Josafat Leal Ribeiro Matheus Wagner Antônio Augusto Fröhlich |
| author_facet | Mateus Martinez de Lucena Josafat Leal Ribeiro Matheus Wagner Antônio Augusto Fröhlich |
| author_sort | Mateus Martinez de Lucena |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4</mn><mo>%</mo></mrow></semantics></math></inline-formula> in the frequency ranges of 0–20 Hz, 15–65 Hz, and 60–105 Hz. |
| format | Article |
| id | doaj-art-01d19f3047e74190b18034cdcea8a4e2 |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-01d19f3047e74190b18034cdcea8a4e22025-01-24T13:17:33ZengMDPI AGAlgorithms1999-48932025-01-011813310.3390/a18010033Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWTMateus Martinez de Lucena0Josafat Leal Ribeiro1Matheus Wagner2Antônio Augusto Fröhlich3Software/Hardware Integration Lab, Federal University of Santa Catarina, Florianópolis 88040-900, BrazilSoftware/Hardware Integration Lab, Federal University of Santa Catarina, Florianópolis 88040-900, BrazilSoftware/Hardware Integration Lab, Federal University of Santa Catarina, Florianópolis 88040-900, BrazilSoftware/Hardware Integration Lab, Federal University of Santa Catarina, Florianópolis 88040-900, BrazilIndustrial 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4</mn><mo>%</mo></mrow></semantics></math></inline-formula> in the frequency ranges of 0–20 Hz, 15–65 Hz, and 60–105 Hz.https://www.mdpi.com/1999-4893/18/1/33compression algorithmsprincipal component analysisdiscrete wavelet transform |
| spellingShingle | Mateus Martinez de Lucena Josafat Leal Ribeiro Matheus Wagner Antônio Augusto Fröhlich Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT Algorithms compression algorithms principal component analysis discrete wavelet transform |
| title | Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT |
| title_full | Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT |
| title_fullStr | Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT |
| title_full_unstemmed | Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT |
| title_short | Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT |
| title_sort | multi component temporal correlation seismic data compression algorithm based on the pca and dwt |
| topic | compression algorithms principal component analysis discrete wavelet transform |
| url | https://www.mdpi.com/1999-4893/18/1/33 |
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