预览加载中，请您耐心等待几秒...

基于改进的小子域滤波的重力异常及重力梯度张量边缘增强(英文).docx

预览
1/2
2/2

在线预览结束，喜欢就下载吧，查找使用更方便

下载提示 文本预览

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

基于改进的小子域滤波的重力异常及重力梯度张量边缘增强(英文) Gravityanomalymethodisawidelyusedtoolingeophysicsforinvestigatingthesubsurfacestructureoftheearth.Itplaysasignificantroleinmineralexploration,oilandgasprospecting,andgeothermalreservoiridentification.Inordertoobtainaccurateanddetailedinformationabouttheearth'ssubsurface,itisimperativetoenhanceedgedetectionandremovenoisefromgravitydata. Inthispaper,weproposeanimprovedsubdomainfilteringmethodforenhancingedgedetectioningravityanomaliesandgravitygradienttensors.Theproposedmethodisbasedonthewell-establishedsubdomainfilteringmethod,whichhasprovedtobeeffectiveinreducingnoiseingravitydata.However,thetraditionalsubdomainfilteringmethodhascertainlimitationsinedgedetection. Theproposedmethodaddressesthisissuebyincorporatinganedge-preservingfeatureintothefilteringprocess.Themethodusesarobustestimatortopreserveedgeswhileremovingnoise.Therobustestimatorisasuitablesubstituteforthemeanvalueinthetraditionalsubdomainfilteringmethod.Byusingarobustestimator,theproposedmethodpreservesthefeaturesoftheoriginalgravitydatawithoutintroducingartifactsandpreservestheedgesinthedata. Toevaluatetheeffectivenessoftheproposedmethod,webenchmarkeditagainstthetraditionalsubdomainfilteringmethodandotherpopularedgedetectionalgorithms.Thebenchmarkingresultsshowedthattheproposedmethodoutperformsthetraditionalsubdomainfilteringmethodinedgedetection. Further,weappliedtheproposedmethodtoreal-worldgravityanomalydataandobservedsignificantimprovementsinedgedetection.Themethodcaneffectivelydetectfaults,boundaries,andothergeologicalfeaturesthatareimportantforsubsurfacestructureidentification. Furthermore,weextendedtheproposedmethodtogravitygradienttensors.Thegravitygradienttensorsprovideadditionalinformation,whichisusefulintheinterpretationofsubsurfacestructures.However,thegradientdataisnoisierthanthegravityanomalydataandrequiresspecialhandling. Theproposedmethodcanenhanceedgedetectioninthegravitygradienttensorsandremovenoisewhilepreservingthefeaturesoftheoriginaldata.Theresultsobta