共查询到19条相似文献，搜索用时 125 毫秒
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SEED数据压缩率的比较 总被引：1，自引：0，他引：1
简要介绍了SEED交换文件格式中Steim2数据压缩算法的原理,并且分别对周期信号、随机信号、地脉动数据和实际地震数据进行压缩分析比较,得出Steim2压缩算法的特点,并进一步说明Steim2算法是一种效率高、优异的压缩算法,但对于大地震数据的压缩率较低甚至不能压缩,建议对于区域性台网,在通过Steim2算法进行压缩传输时,为了防止在大地震发生时的传输阻塞,系统应该保留一定的带宽裕量。 相似文献
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徐嘉隽 《地震地磁观测与研究》2013,34(3)
根据不同移动网络数据传输的特点,结合地震数据的压缩算法,分析不同采样频率和数据长度下使用移动网络进行地震数据传输的带宽需求,并给出计算公式.根据计算分析,得到平静期和发生大地震时不同的数据压缩率和带宽需求.给出利用移动网络数据传输数据的影响因素,总结各种类型的无线网络传输地震数据的可行性. 相似文献
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《地震地磁观测与研究》1994,(2)
附录Ｂ压缩算法１Ｓｔｅｉｍｌ数据压缩原理台站安装的数字化地震数据采集系统输出的原始数据样本为二进制补码，４字节３２位有符号整数时间序列。设一个数据记录的原始样本序列为Ｘ＿０……Ｘ＿ｎ据差值公式 Ｄ＿ｎ＝Ｘ＿ｎ－Ｘ＿（ｎ－１）可得第一差值序列Ｄ０＝ｘ０... 相似文献
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利用C++与MATLAB相结合,对NOAA网站上公开可下载的数据进行与地震监测相关的气象三要素数据提取;我国境内共有1076个台站的数据可用,数据可追溯到20世纪50年代。通过本次编写的数据提取软件,可对文件中气温、气压、降雨(采样率3小时/次)进行自动提取,并对错误数据进行自动识别;为地震前兆观测中气象干扰的剔除提供了丰富的源数据。 相似文献
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Ａｒｅａｌ－ｔｉｍｅａｌｇｏｒｉｔｈｍｆｏｒｂｒｏａｄｂａｎｄｈｉｇｈｄｙｎａｍｉｃｓｅｉｓｍｉｃｄａｔａｃｏｍｐｒｅｓｓｉｏｎＳｈａ－ＢａｉＬＩ１）（李沙白）;Ｑｉ－ＹｕａｎＬＩＵ２）（刘启元）ａｎｄＬｉ－ＲｅｎＳＨＥＮ２）（沈立人）（Ｉｎｓｔｉｔ... 相似文献
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本文探讨了二种地震数据实时自适应压缩方法。自适应采样是我们在总结传统的采样方法的基础上,结合地震信号频率变化特点,对地震信号所采取的一种波形压缩方法。自适应增量编码ADPCM法则在自适应采样压缩的基础上将地震数据又压缩了二倍,进一步提高了压缩效果。两种方法有力配合,使短、中长、长周期地震记录分别压缩了12倍、30倍、10倍以上。压缩恢复波形无论在时域分析(相关系数达0.997以上)还是在频域分析,失真都非常小,基本达到无畸变压缩。上述两种方法算法简单,计算量小,易于地震数据的高速采集、传输及记录。自适应采样法目前已应用于国家地震局地球物理所六室研制的BSO系统中。 相似文献
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随着数字地震台站数量的增加、数据传输手段的多样化以及地震预警系统的发展,对地震数据实现有效的实时压缩对数据传输显得十分重要.本文提出一种支持低时间延迟(数据传输间隔小于1 s)数据传输要求的实时数据压缩算法.通过理论估算和实际地震数据检验,该方法高效、易实现,适用于地震预警等领域. 相似文献
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Due to the large scale and complexity of civil infrastructures, structural health monitoring typically requires a substantial number of sensors, which consequently generate huge volumes of sensor data. Innovative sensor data compression techniques are highly desired to facilitate efficient data storage and remote retrieval of sensor data. This paper presents a vibration sensor data compression algorithm based on the Differential Pulse Code Modulation (DPCM) method and the consideration of effects of signal distortion due to lossy data compression on structural system identification. The DPCM system concerned consists of two primary components: linear predictor and quantizer. For the DPCM system considered in this study, the Least Square method is used to derive the linear predictor coefficients and Jayant quantizer is used for scalar quantization. A 5-DOF model structure is used as the prototype structure in numerical study. Numerical simulation was carried out to study the performance of the proposed DPCM-based data compression algorithm as well as its effect on the accuracy of structural identification including modal parameters and second order structural parameters such as stiffness and damping coefficients. It is found that the DPCM-based sensor data compression method is capable of reducing the raw sensor data size to a significant extent while having a minor effect on the modal parameters as well as second order structural parameters identified from reconstructed sensor data. 相似文献
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分析了等效源曲面延拓方法积分方程核函数的自相似性、冗余性,根据Fredholm积分方程核函数特征,提出了一种小波余弦非线性阈值压缩算法,实现了大型Fredholm积分方程的降阶,使得Bhattacharyya等（1977年）提出的等效源曲面延拓方法能够处理大面积、大数据量的资料. 理论模型结果表明,当压缩比为405%时硬阈值压缩方法的曲面延拓可以达到很高的精度,当压缩比为811%时, 硬阈值压缩方法精度降低,而采用我们提出的余弦非线性阈值可以明显提高曲面延拓的精度. 将硬阈值、软阈值和非线性阈值压缩三种不同方法用于川东北气田MT－1线资料的处理,当压缩比达79%时,三种不同方法曲化平结果都可以达到很好的效果,但非线性阈值压缩的曲化平结果失真最小,它能够客观地反映杨家河局部隆起,为在该区寻找与油气有关的局部构造提供重要依据. 相似文献
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Introduction With the development of the seismological observation technique and deep-going of seismicdata application fields, especially the digitization of data in earthquake station networks, theimprovement of the precision, the data quantity increases as geometric order, which bringdifficulty to saving and transfering these data. To keep all information, seismic data, like medicalimages, should be compressed without error in many applications. In generally, traditionalcompression meth… 相似文献
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Mallat算法在数字地震信号压缩中的应用 总被引：1，自引：1，他引：0
王军 《地震地磁观测与研究》2017,38(5):133-138
地震台站多、数据采集量大,日产出数据量庞大,研究数字地震信号的压缩方法成为行业热门课题。尝试将Mallat算法应用于数字地震波形数据压缩。选取不同的小波分解函数,对不同类型的数字地震信号进行3—5层的小波分解,将得到的小波系数进行分层硬阈值重构运算,对原始信号和处理信号进行压缩。分析可知,Mallat算法压缩比更高,与原始信号相比,重构信号不失真、能量保留系数高。 相似文献
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Due to the particularity of the seismic data, they must be treated by lossless compression algorithm in some cases. In the
paper, based on the integer wavelet transform, the lossless compression algorithm is studied. Comparing with the traditional
algorithm, it can better improve the compression rate. CDF (2, n) biorthogonal wavelet family can lead to better compression ratio than other CDF family, SWE and CRF, which is owe to its
capability in canceling data redundancies and focusing data characteristics. CDF (2, n) family is suitable as the wavelet function of the lossless compression seismic data.
Contribution No.04FE1019, Institute of Geophysics, China Earthquake Administration. 相似文献
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This paper presents a linear predictor (LP)‐based lossless sensor data compression algorithm for efficient transmission, storage and retrieval of seismic data. Auto‐Regressive with eXogenous input (ARX) model is selected as the model structure of LP. Since earthquake ground motion is typically measured at the base of monitored structures, the ARX model parameters are calculated in a system identification framework using sensor network data and measured input signals. In this way, sensor data compression takes advantage of structural system information to maximize the sensor data compression performance. Numerical simulation results show that several factors including LP order, measurement noise, input and limited sensor number affect the performance of the proposed lossless sensor data compression algorithm concerned. Generally, the lossless data compression algorithm is capable of reducing the size of raw sensor data while causing no information loss in the sensor data. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献