简介:传统的f-x域经验模态分解法(Empiricalmodedecomposition,EMD)能够有效地对主要由水平同相轴构成的地震记录进行随机噪声衰减。然而,当同相轴倾斜时,f-x域经验模态分解法在衰减随机噪声的同时去除大部分有效信号。本文提出了一种基于f-x域经验模态分解法的改进算法。我们通过局部相似度对所去除的噪声信号中的有效信号进行提取。局部相似度可以用来检测噪声信号中的有效信号点并用来构造一权重算子进行信号提取。新方法与f-x域经验模态分解法、f-x域预测滤波法以及f-x域经验模态分解预测滤波法相比能够在衰减随机噪声的同时保留更多的有用信号。数值模拟实验以及实际地震资料处理结果均表明该方法能更为有效地去噪。
简介:High-qualityseismicgeometryisthekeytoobtainhigh-qualityseismicdata,andcanaffecttheaccuracyofdataprocessingandimaging.Basedontheanalysisoftherelationshipbetweenthequalityofthegeometryandthefouracquisitionparameters(thenumberoftraces,shotlinespacing,andthespaceandnumberofreceiverlines),aqualityevaluationmethodofthegeometrybasedoncomprehensivequalityfactor(CQF)isproposed,andtherelationshipbetweenthegeometryqualityandthefourparametersisgiven.WeusefielddatacollectedinanoilfieldinWesternChinawithcomplexgeology:Firstweuseawideazimuthgeometry.Then,wecalculatetherelationshipcurvebetweengeometryanddataqualitybyvaryingeachparameterwhilekeepingtherestfixed.andtheanalysisresultsaregivenbyusingtheCQFevaluationmethod.Theresultsshowthattheshot-linespacinghasthegreatesteffectonthequalityofthegeometry,andtheincreaseofthereceiverlinespacingcanappropriatelyimprovethequalityofthegeometry,andtheincreaseofthenumberofreceivingtracescanimprovethegeometryquality.Thedifferentacquisitionparametershavedifferenteffectsontheimagingqualityofshallowanddeepevents.Themodelforwardandprestackdepthmigrationareusedtogenerateprestackdepthmigrationprofileswithdifferentacquisitionparameters.Theimagingresultsareconsistentwiththeabovecalculatedresults.Accordingtothedepthofthetargetlayer,thequalityfactorevaluationmethodisappliedtoguidethedesignofthegeometryandoptimizetheacquisitionparameterstoimprovetheimagingaccuracyofseismicdata.