{"title":"非电缆地震仪地震数据的快速压缩算法","authors":"F. Zheng, Shufen Liu","doi":"10.1109/WICT.2012.6409260","DOIUrl":null,"url":null,"abstract":"Considering data storage characteristics of noncable seismograph, accommodating to the requirement of further processing, a new parallel seismic data compression algorithm is developed based on the integer wavelet transform. First, separate the valid file header and extract the valid data to 4 one-dimensional matrixes. Then compress the four matrixes in parallel: the time signals are converted into transform domain by using the integer wavelet transform, where low frequency wavelet coefficients are retained and high frequency ones are scalar quantized. Lossless encoding is then conducted to deliver compression output. Experiments have shown that, at the same compression ratio, the proposed algorithm compresses 30% faster than traditional approach which directly uses two-dimensional wavelet transform to process two-dimensional matrixes, and yields good compression results.","PeriodicalId":445333,"journal":{"name":"2012 World Congress on Information and Communication Technologies","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"A fast compression algorithm for seismic data from non-cable seismographs\",\"authors\":\"F. Zheng, Shufen Liu\",\"doi\":\"10.1109/WICT.2012.6409260\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Considering data storage characteristics of noncable seismograph, accommodating to the requirement of further processing, a new parallel seismic data compression algorithm is developed based on the integer wavelet transform. First, separate the valid file header and extract the valid data to 4 one-dimensional matrixes. Then compress the four matrixes in parallel: the time signals are converted into transform domain by using the integer wavelet transform, where low frequency wavelet coefficients are retained and high frequency ones are scalar quantized. Lossless encoding is then conducted to deliver compression output. Experiments have shown that, at the same compression ratio, the proposed algorithm compresses 30% faster than traditional approach which directly uses two-dimensional wavelet transform to process two-dimensional matrixes, and yields good compression results.\",\"PeriodicalId\":445333,\"journal\":{\"name\":\"2012 World Congress on Information and Communication Technologies\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 World Congress on Information and Communication Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WICT.2012.6409260\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 World Congress on Information and Communication Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WICT.2012.6409260","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A fast compression algorithm for seismic data from non-cable seismographs
Considering data storage characteristics of noncable seismograph, accommodating to the requirement of further processing, a new parallel seismic data compression algorithm is developed based on the integer wavelet transform. First, separate the valid file header and extract the valid data to 4 one-dimensional matrixes. Then compress the four matrixes in parallel: the time signals are converted into transform domain by using the integer wavelet transform, where low frequency wavelet coefficients are retained and high frequency ones are scalar quantized. Lossless encoding is then conducted to deliver compression output. Experiments have shown that, at the same compression ratio, the proposed algorithm compresses 30% faster than traditional approach which directly uses two-dimensional wavelet transform to process two-dimensional matrixes, and yields good compression results.