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基于曲波变换和压缩感知的煤岩惰质组分类.docx

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基于曲波变换和压缩感知的煤岩惰质组分类 Title:Coal-RockInertiniteGroupClassificationBasedonCurveletTransformandCompressedSensing Abstract: Coal-rockclassificationplaysacrucialroleincoalminingandexplorationasithelpsindeterminingthequalityandusabilityofcoaldeposits.Inthispaper,weproposeanovelapproachtoclassifycoal-rockinertinitegroupsusingtheCurveletTransformandCompressedSensingtechniques.Theproposedmethodaimstoimprovetheaccuracyandefficiencyofexistingclassificationmethods. 1.Introduction: Coal-rockclassificationisachallengingtaskduetothecomplexcompositionalandstructuralcharacteristicsofcoaldeposits.Traditionalclassificationmethodsrelyonmanualidentificationandvisualinterpretation,leadingtosubjectiveandtime-consuminganalysis.Therefore,thereisastrongneedforautomatedandobjectiveclassificationtechniques. 2.Background: TheCurveletTransformisapowerfultoolforanalyzingthestructureandtextureofdigitalimages.Itisknownforitsabilitytocapturelocaldirectionalinformation,makingitsuitableforcoal-rockanalysis.CompressedSensingisasignalprocessingtechniquethatenablesthereconstructionofsparsesignalswithareducednumberofmeasurements,whichcansignificantlyspeeduptheclassificationprocess. 3.Methodology: Theproposedmethodconsistsofthefollowingsteps: 3.1DataAcquisition: High-resolutioncoal-rockimagesarecollectedusingadvancedimagingtechniquessuchasscanningelectronmicroscopy(SEM)oropticalmicroscopy.Theseimagesserveastheinputfortheclassificationprocess. 3.2CurveletTransform: TheacquiredimagesaredecomposedusingtheCurveletTransform.Thistransformcapturesthemulti-scaledirectionalinformationinthecoal-rockimages,enablingamorerobustfeatureextractioncomparedtotraditionalmethods. 3.3FeatureExtraction: FromtheCurveletcoefficients,statisticalfeaturessuchasmean,standarddeviation,skewness,andkurtosisareextractedtorepresentthelocalstructureandtexturepropertiesofthecoal-rocksamples.Thesefeaturesserveastheinputfortheclassificationalgorithm. 3.4CompressedSensing: Toreducethedimensionalityofthefeaturespaceandfurtherenhancetheclassifica