Performance comparison of CNN models with different input features.
<p>Performance comparison of CNN models with different input features.</p>
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2025
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| _version_ | 1852015604315193344 |
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| author | Dongmei Liu (268523) |
| author2 | Junsong Zhang (5157851) Bingrui Zhao (17137234) Linsheng Gao (22478134) Hao Zhou (136535) Zhiheng Cheng (10159466) Liang Chen (73736) Meichen Li (8773820) |
| author2_role | author author author author author author author |
| author_facet | Dongmei Liu (268523) Junsong Zhang (5157851) Bingrui Zhao (17137234) Linsheng Gao (22478134) Hao Zhou (136535) Zhiheng Cheng (10159466) Liang Chen (73736) Meichen Li (8773820) |
| author_role | author |
| dc.creator.none.fl_str_mv | Dongmei Liu (268523) Junsong Zhang (5157851) Bingrui Zhao (17137234) Linsheng Gao (22478134) Hao Zhou (136535) Zhiheng Cheng (10159466) Liang Chen (73736) Meichen Li (8773820) |
| dc.date.none.fl_str_mv | 2025-10-22T17:49:28Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0334641.t002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Performance_comparison_of_CNN_models_with_different_input_features_/30421045 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biophysics Cell Biology Neuroscience Biological Sciences not elsewhere classified Information Systems not elsewhere classified study innovatively introduces seam boreholes generates g ., short capture comprehensive spatiotemporal 72 %, outperforming 10 different degrees network degree distribution 546 effective waveforms dimensional cnn model cnn ), integrating weak microseismic signals misjudging background noise mine microseismic signals analyze microseismic signals identical precision rate higher recall rate propagation dynamics model microseismic event identification fourier cnn model dynamic signal characteristics domain cnn model domain features enables background noise data computational communication concepts cnn model microseismic signals background noise recall rate precision rate propagation network microseismic data domain waveforms noise ratios adding noise fourier images computational propagation computational communication communication technology integrating time frequency characteristics domain images domain features xlink "> subsequent positioning snr ). performing translation new path mining field low signal large number |
| dc.title.none.fl_str_mv | Performance comparison of CNN models with different input features. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Performance comparison of CNN models with different input features.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_31eda4384d38bf5291f8fc37399d5259 |
| identifier_str_mv | 10.1371/journal.pone.0334641.t002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30421045 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Performance comparison of CNN models with different input features.Dongmei Liu (268523)Junsong Zhang (5157851)Bingrui Zhao (17137234)Linsheng Gao (22478134)Hao Zhou (136535)Zhiheng Cheng (10159466)Liang Chen (73736)Meichen Li (8773820)BiophysicsCell BiologyNeuroscienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedstudy innovatively introducesseam boreholes generatesg ., shortcapture comprehensive spatiotemporal72 %, outperforming10 different degreesnetwork degree distribution546 effective waveformsdimensional cnn modelcnn ), integratingweak microseismic signalsmisjudging background noisemine microseismic signalsanalyze microseismic signalsidentical precision ratehigher recall ratepropagation dynamics modelmicroseismic event identificationfourier cnn modeldynamic signal characteristicsdomain cnn modeldomain features enablesbackground noise datacomputational communication conceptscnn modelmicroseismic signalsbackground noiserecall rateprecision ratepropagation networkmicroseismic datadomain waveformsnoise ratiosadding noisefourier imagescomputational propagationcomputational communicationcommunication technologyintegrating timefrequency characteristicsdomain imagesdomain featuresxlink ">subsequent positioningsnr ).performing translationnew pathmining fieldlow signallarge number<p>Performance comparison of CNN models with different input features.</p>2025-10-22T17:49:28ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0334641.t002https://figshare.com/articles/dataset/Performance_comparison_of_CNN_models_with_different_input_features_/30421045CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/304210452025-10-22T17:49:28Z |
| spellingShingle | Performance comparison of CNN models with different input features. Dongmei Liu (268523) Biophysics Cell Biology Neuroscience Biological Sciences not elsewhere classified Information Systems not elsewhere classified study innovatively introduces seam boreholes generates g ., short capture comprehensive spatiotemporal 72 %, outperforming 10 different degrees network degree distribution 546 effective waveforms dimensional cnn model cnn ), integrating weak microseismic signals misjudging background noise mine microseismic signals analyze microseismic signals identical precision rate higher recall rate propagation dynamics model microseismic event identification fourier cnn model dynamic signal characteristics domain cnn model domain features enables background noise data computational communication concepts cnn model microseismic signals background noise recall rate precision rate propagation network microseismic data domain waveforms noise ratios adding noise fourier images computational propagation computational communication communication technology integrating time frequency characteristics domain images domain features xlink "> subsequent positioning snr ). performing translation new path mining field low signal large number |
| status_str | publishedVersion |
| title | Performance comparison of CNN models with different input features. |
| title_full | Performance comparison of CNN models with different input features. |
| title_fullStr | Performance comparison of CNN models with different input features. |
| title_full_unstemmed | Performance comparison of CNN models with different input features. |
| title_short | Performance comparison of CNN models with different input features. |
| title_sort | Performance comparison of CNN models with different input features. |
| topic | Biophysics Cell Biology Neuroscience Biological Sciences not elsewhere classified Information Systems not elsewhere classified study innovatively introduces seam boreholes generates g ., short capture comprehensive spatiotemporal 72 %, outperforming 10 different degrees network degree distribution 546 effective waveforms dimensional cnn model cnn ), integrating weak microseismic signals misjudging background noise mine microseismic signals analyze microseismic signals identical precision rate higher recall rate propagation dynamics model microseismic event identification fourier cnn model dynamic signal characteristics domain cnn model domain features enables background noise data computational communication concepts cnn model microseismic signals background noise recall rate precision rate propagation network microseismic data domain waveforms noise ratios adding noise fourier images computational propagation computational communication communication technology integrating time frequency characteristics domain images domain features xlink "> subsequent positioning snr ). performing translation new path mining field low signal large number |