{"title":"Spiking deconvolution for seismic waves using the Fractional Fourier Transform","authors":"S. Sud","doi":"10.1109/SECON.2017.7925350","DOIUrl":null,"url":null,"abstract":"This paper applies the Fractional Fourier Transform (FrFT) to a collected seismic trace to estimate the Earth's reflectivity function. This is done by computing the FrFT domain ‘a’ in which the source wavelet is as close to a delta (or spiking) function as possible, mimicking the concept of spiking deconvolution. We show by simulation that the proposed method outperforms conventional spiking deconvolution (SD) and time domain deconvolution (TDD) by nearly an order of magnitude over signal-to-noise ratios (SNRs) of −10 to 20 dB.","PeriodicalId":368197,"journal":{"name":"SoutheastCon 2017","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SoutheastCon 2017","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SECON.2017.7925350","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper applies the Fractional Fourier Transform (FrFT) to a collected seismic trace to estimate the Earth's reflectivity function. This is done by computing the FrFT domain ‘a’ in which the source wavelet is as close to a delta (or spiking) function as possible, mimicking the concept of spiking deconvolution. We show by simulation that the proposed method outperforms conventional spiking deconvolution (SD) and time domain deconvolution (TDD) by nearly an order of magnitude over signal-to-noise ratios (SNRs) of −10 to 20 dB.
本文将分数阶傅立叶变换(FrFT)应用于采集到的地震道,估计地球的反射率函数。这是通过计算FrFT域' a '来完成的,其中源小波尽可能接近delta(或尖峰)函数,模仿尖峰反卷积的概念。我们通过仿真表明,所提出的方法优于传统的尖峰反卷积(SD)和时域反卷积(TDD),在信噪比(SNRs)为- 10至20 dB的范围内提高了近一个数量级。