简介：Asglobaltemperaturesincreaseanddissolvedoxygen(DO)contentdecreasesinmarinesystems,indicesassessingsedimentDOcontentinbenthichabitatsarebecomingincreasinglyuseful.Onesuchmeasureisthedepthtotheapparentredoxpotentialdiscontinuity(aRPD),atransitionofsedimentcolorthatservesasarelativemeasureofsedimentDOcontent.WeexaminedspatiotemporalvariationofaRPDdepth,andthenatureoftherelationshipsbetweenaRPDdepthandbiotic(infaunaandepibenthicpredators)andabioticvariables(sedimentproperties),aswellastheavailabilityofresources(chlorophyllaconcentration,andorganicmattercontent)intheintertidalmudflatsoftheBayofFundy,Canada.aRPDdepthvariedsignificantlythroughspaceandtime,andacombinationofbiotic(sessileanderrantinfauna,aswellasepibenthicpredators),andabiotic(exposuretimeofaplot,sedimentparticlesize,penetrability,andwatercontent)variables,aswellastheavailabilityofresources(sedimentorganicmattercontent,andchlorophyllaconcentration)werecorrelatedwithaRPDdepth.Assuch,knowledgeofbothbioticandabioticvariablesarerequiredforaholisticunderstandingofsedimentDOconditions.AbioticvariableslikelydictateasuiteofpotentialaRPDdepthconditions,whilebiotaandresourceavailability,viabioturbationandrespiration,stronglyinfluencetheobservedaRPDdepth.AsDOconditionsinmarinesystemswillcontinuetochangeduetoglobalclimatechange,elucidatingtheserelationshipsareakeyfirststepinpredictingtheinfluencedecreasingDOcontentmayhaveuponmarinebenthos.&2017InternationalResearchandTrainingCentreonErosionandSedimentation/theWorldAssociationforSedimentationandErosionResearch.PublishedbyElsevierB.V.Allrightsreserved.

简介：F-X域经验模态分解去噪方法在处理非稳态地震数据时存在两个局限,一是单纯剔除第一个固有模态分量将导致有效信号缺失及去噪能力偏弱问题,二是分解复信号时对实部和虚部分别分解存在分解数目不一致的风险。本文对上述两个方面进行了改进,提出了一种新的F-X域投影法复数经验模态分解预测滤波方法,首先采用基于空间投影的复数经验模态分解将F-X域地震数据直接分解为不同的复固有模态分量,然后再对这些分量分别进行F-X域预测滤波。合成记录及实际资料测试表明,本文的新方法能更好地衰减随机噪声,更有效地保持地震信号。

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。