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基于Python的开采沉陷预计算法
Title:APython-basedAlgorithmforSubsidencePredictioninMiningOperations
Abstract:
Thepredictionandmanagementoflandsubsidenceiscrucialforeffectiveminingoperations.ThispaperpresentsanovelPython-basedalgorithmforpredictingandcalculatingsubsidenceinundergroundminingenvironments.Theproposedalgorithmutilizesvariousgeospatialdata,includinggeologicalcharacteristics,miningparameters,andsoilproperties,toestimatethepotentialsubsidencelevels.Byintegratingthesedatasourcesandemployingadvancedgeospatialanalysistechniques,thealgorithmprovidesaccuratepredictionsofsubsidence,enablingminingcompaniestomakeinformeddecisionsregardinginfrastructureplanningandresourceextraction.
1.Introduction
Miningactivities,particularlyundergroundmining,canresultinsignificantlandsubsidenceduetotheextractionofmineralsfrombeneaththeearth'ssurface.Subsidenceposesriskstosurfacestructures,infrastructure,andtheenvironment.Therefore,accuratepredictionofsubsidenceiscrucialforensuringthesafetyandsustainabilityofminingoperations.ThispaperpresentsaPython-basedalgorithmthatleveragesgeospatialdataandadvancedanalysistechniquestoforecastandcalculatesubsidenceinminingareas.
2.LiteratureReview
Severalexistingmethodshavebeendevelopedforsubsidenceprediction,includingempiricalmodels,numericalsimulations,anddata-drivenapproaches.Empiricalmodelsrelyonhistoricaldataandpreviouslyobservedsubsidencepatterns,whilenumericalsimulationsusecomplexmathematicalequationstomodelthesubsidenceprocess.Data-drivenapproaches,suchasmachinelearningandartificialneuralnetworks,utilizelargedatasetstotrainpredictivemodels.However,theseexistingmethodsoftensufferfromlimitationsregardingaccuracy,computationalefficiency,andscalability.TheproposedPython-basedalgorithmaddressestheselimitationsbyprovidinganintegratedandcustomizablesolutionforsubsidenceprediction.
3.Methodology
ThePython-basedalgorithmforsubsidencepredictioninminingoperationsconsistsofseveralsteps:
3.1DataAcquisition
Thealgorithmacquiresrelevantgeospatialdata,includinggeolo