A new method of balanced edge detection based on curvature for gravity data
Abstract The edge detection method can be used to delineate the position of the structural boundary of the geological body, and it occupies an important position in the processing of gravity data. The shortcomings of traditional edge detection methods are that the output boundary is divergent, the S...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Dandan, Jiang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Acta geophysica - Warsaw : De Gruyter Open, 2006, 71(2022), 4 vom: 27. Dez., Seite 1705-1715 |
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| Übergeordnetes Werk: |
volume:71 ; year:2022 ; number:4 ; day:27 ; month:12 ; pages:1705-1715 |
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| DOI / URN: |
10.1007/s11600-022-00995-1 |
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| Katalog-ID: |
SPR051979004 |
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| 245 | 1 | 2 | |a A new method of balanced edge detection based on curvature for gravity data |
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| 520 | |a Abstract The edge detection method can be used to delineate the position of the structural boundary of the geological body, and it occupies an important position in the processing of gravity data. The shortcomings of traditional edge detection methods are that the output boundary is divergent, the Strength and weak anomalies cannot be balanced, or false boundaries appear in the recognition results. There is a close connection between gravity anomaly and its curvatures and subsurface geological formations. Thus they can be used to describe the geometry of subsurface formations. In this paper, a new method of balanced edge detection based on curvature is proposed by combining the fusion formula of average curvature, vertical curvature, and the degree of curvature. Model tests show that the method proposed in this paper has obvious advantages compared with the recognition effects of several traditional curvature and equalization filtering methods. The edge detection results of the proposed method have good convergence. When both positive and negative anomalies exist, this method is less affected and has anti-noise capabilities. Applying the method to real field data, the results show that the identified geological body structural boundaries are clearer and more accurate, and can be applied to more complex geological environments. | ||
| 650 | 4 | |a Gravity data |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Edge detection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Curvature |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Geological structure |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Gravity exploration |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Qi, Zhang |4 aut | |
| 700 | 1 | |a Hairong, Zhang |4 aut | |
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10.1007/s11600-022-00995-1 doi (DE-627)SPR051979004 (SPR)s11600-022-00995-1-e DE-627 ger DE-627 rakwb eng Dandan, Jiang verfasserin aut A new method of balanced edge detection based on curvature for gravity data 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The edge detection method can be used to delineate the position of the structural boundary of the geological body, and it occupies an important position in the processing of gravity data. The shortcomings of traditional edge detection methods are that the output boundary is divergent, the Strength and weak anomalies cannot be balanced, or false boundaries appear in the recognition results. There is a close connection between gravity anomaly and its curvatures and subsurface geological formations. Thus they can be used to describe the geometry of subsurface formations. In this paper, a new method of balanced edge detection based on curvature is proposed by combining the fusion formula of average curvature, vertical curvature, and the degree of curvature. Model tests show that the method proposed in this paper has obvious advantages compared with the recognition effects of several traditional curvature and equalization filtering methods. The edge detection results of the proposed method have good convergence. When both positive and negative anomalies exist, this method is less affected and has anti-noise capabilities. Applying the method to real field data, the results show that the identified geological body structural boundaries are clearer and more accurate, and can be applied to more complex geological environments. Gravity data (dpeaa)DE-He213 Edge detection (dpeaa)DE-He213 Curvature (dpeaa)DE-He213 Geological structure (dpeaa)DE-He213 Gravity exploration (dpeaa)DE-He213 Qi, Zhang aut Hairong, Zhang aut Enthalten in Acta geophysica Warsaw : De Gruyter Open, 2006 71(2022), 4 vom: 27. Dez., Seite 1705-1715 (DE-627)51061843X (DE-600)2231673-5 1895-7455 nnns volume:71 year:2022 number:4 day:27 month:12 pages:1705-1715 https://dx.doi.org/10.1007/s11600-022-00995-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 71 2022 4 27 12 1705-1715 |
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10.1007/s11600-022-00995-1 doi (DE-627)SPR051979004 (SPR)s11600-022-00995-1-e DE-627 ger DE-627 rakwb eng Dandan, Jiang verfasserin aut A new method of balanced edge detection based on curvature for gravity data 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The edge detection method can be used to delineate the position of the structural boundary of the geological body, and it occupies an important position in the processing of gravity data. The shortcomings of traditional edge detection methods are that the output boundary is divergent, the Strength and weak anomalies cannot be balanced, or false boundaries appear in the recognition results. There is a close connection between gravity anomaly and its curvatures and subsurface geological formations. Thus they can be used to describe the geometry of subsurface formations. In this paper, a new method of balanced edge detection based on curvature is proposed by combining the fusion formula of average curvature, vertical curvature, and the degree of curvature. Model tests show that the method proposed in this paper has obvious advantages compared with the recognition effects of several traditional curvature and equalization filtering methods. The edge detection results of the proposed method have good convergence. When both positive and negative anomalies exist, this method is less affected and has anti-noise capabilities. Applying the method to real field data, the results show that the identified geological body structural boundaries are clearer and more accurate, and can be applied to more complex geological environments. Gravity data (dpeaa)DE-He213 Edge detection (dpeaa)DE-He213 Curvature (dpeaa)DE-He213 Geological structure (dpeaa)DE-He213 Gravity exploration (dpeaa)DE-He213 Qi, Zhang aut Hairong, Zhang aut Enthalten in Acta geophysica Warsaw : De Gruyter Open, 2006 71(2022), 4 vom: 27. Dez., Seite 1705-1715 (DE-627)51061843X (DE-600)2231673-5 1895-7455 nnns volume:71 year:2022 number:4 day:27 month:12 pages:1705-1715 https://dx.doi.org/10.1007/s11600-022-00995-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 71 2022 4 27 12 1705-1715 |
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10.1007/s11600-022-00995-1 doi (DE-627)SPR051979004 (SPR)s11600-022-00995-1-e DE-627 ger DE-627 rakwb eng Dandan, Jiang verfasserin aut A new method of balanced edge detection based on curvature for gravity data 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The edge detection method can be used to delineate the position of the structural boundary of the geological body, and it occupies an important position in the processing of gravity data. The shortcomings of traditional edge detection methods are that the output boundary is divergent, the Strength and weak anomalies cannot be balanced, or false boundaries appear in the recognition results. There is a close connection between gravity anomaly and its curvatures and subsurface geological formations. Thus they can be used to describe the geometry of subsurface formations. In this paper, a new method of balanced edge detection based on curvature is proposed by combining the fusion formula of average curvature, vertical curvature, and the degree of curvature. Model tests show that the method proposed in this paper has obvious advantages compared with the recognition effects of several traditional curvature and equalization filtering methods. The edge detection results of the proposed method have good convergence. When both positive and negative anomalies exist, this method is less affected and has anti-noise capabilities. Applying the method to real field data, the results show that the identified geological body structural boundaries are clearer and more accurate, and can be applied to more complex geological environments. Gravity data (dpeaa)DE-He213 Edge detection (dpeaa)DE-He213 Curvature (dpeaa)DE-He213 Geological structure (dpeaa)DE-He213 Gravity exploration (dpeaa)DE-He213 Qi, Zhang aut Hairong, Zhang aut Enthalten in Acta geophysica Warsaw : De Gruyter Open, 2006 71(2022), 4 vom: 27. Dez., Seite 1705-1715 (DE-627)51061843X (DE-600)2231673-5 1895-7455 nnns volume:71 year:2022 number:4 day:27 month:12 pages:1705-1715 https://dx.doi.org/10.1007/s11600-022-00995-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 71 2022 4 27 12 1705-1715 |
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10.1007/s11600-022-00995-1 doi (DE-627)SPR051979004 (SPR)s11600-022-00995-1-e DE-627 ger DE-627 rakwb eng Dandan, Jiang verfasserin aut A new method of balanced edge detection based on curvature for gravity data 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The edge detection method can be used to delineate the position of the structural boundary of the geological body, and it occupies an important position in the processing of gravity data. The shortcomings of traditional edge detection methods are that the output boundary is divergent, the Strength and weak anomalies cannot be balanced, or false boundaries appear in the recognition results. There is a close connection between gravity anomaly and its curvatures and subsurface geological formations. Thus they can be used to describe the geometry of subsurface formations. In this paper, a new method of balanced edge detection based on curvature is proposed by combining the fusion formula of average curvature, vertical curvature, and the degree of curvature. Model tests show that the method proposed in this paper has obvious advantages compared with the recognition effects of several traditional curvature and equalization filtering methods. The edge detection results of the proposed method have good convergence. When both positive and negative anomalies exist, this method is less affected and has anti-noise capabilities. Applying the method to real field data, the results show that the identified geological body structural boundaries are clearer and more accurate, and can be applied to more complex geological environments. Gravity data (dpeaa)DE-He213 Edge detection (dpeaa)DE-He213 Curvature (dpeaa)DE-He213 Geological structure (dpeaa)DE-He213 Gravity exploration (dpeaa)DE-He213 Qi, Zhang aut Hairong, Zhang aut Enthalten in Acta geophysica Warsaw : De Gruyter Open, 2006 71(2022), 4 vom: 27. Dez., Seite 1705-1715 (DE-627)51061843X (DE-600)2231673-5 1895-7455 nnns volume:71 year:2022 number:4 day:27 month:12 pages:1705-1715 https://dx.doi.org/10.1007/s11600-022-00995-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 71 2022 4 27 12 1705-1715 |
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10.1007/s11600-022-00995-1 doi (DE-627)SPR051979004 (SPR)s11600-022-00995-1-e DE-627 ger DE-627 rakwb eng Dandan, Jiang verfasserin aut A new method of balanced edge detection based on curvature for gravity data 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The edge detection method can be used to delineate the position of the structural boundary of the geological body, and it occupies an important position in the processing of gravity data. The shortcomings of traditional edge detection methods are that the output boundary is divergent, the Strength and weak anomalies cannot be balanced, or false boundaries appear in the recognition results. There is a close connection between gravity anomaly and its curvatures and subsurface geological formations. Thus they can be used to describe the geometry of subsurface formations. In this paper, a new method of balanced edge detection based on curvature is proposed by combining the fusion formula of average curvature, vertical curvature, and the degree of curvature. Model tests show that the method proposed in this paper has obvious advantages compared with the recognition effects of several traditional curvature and equalization filtering methods. The edge detection results of the proposed method have good convergence. When both positive and negative anomalies exist, this method is less affected and has anti-noise capabilities. Applying the method to real field data, the results show that the identified geological body structural boundaries are clearer and more accurate, and can be applied to more complex geological environments. Gravity data (dpeaa)DE-He213 Edge detection (dpeaa)DE-He213 Curvature (dpeaa)DE-He213 Geological structure (dpeaa)DE-He213 Gravity exploration (dpeaa)DE-He213 Qi, Zhang aut Hairong, Zhang aut Enthalten in Acta geophysica Warsaw : De Gruyter Open, 2006 71(2022), 4 vom: 27. Dez., Seite 1705-1715 (DE-627)51061843X (DE-600)2231673-5 1895-7455 nnns volume:71 year:2022 number:4 day:27 month:12 pages:1705-1715 https://dx.doi.org/10.1007/s11600-022-00995-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 71 2022 4 27 12 1705-1715 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The edge detection method can be used to delineate the position of the structural boundary of the geological body, and it occupies an important position in the processing of gravity data. The shortcomings of traditional edge detection methods are that the output boundary is divergent, the Strength and weak anomalies cannot be balanced, or false boundaries appear in the recognition results. There is a close connection between gravity anomaly and its curvatures and subsurface geological formations. Thus they can be used to describe the geometry of subsurface formations. 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new method of balanced edge detection based on curvature for gravity data |
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A new method of balanced edge detection based on curvature for gravity data |
| abstract |
Abstract The edge detection method can be used to delineate the position of the structural boundary of the geological body, and it occupies an important position in the processing of gravity data. The shortcomings of traditional edge detection methods are that the output boundary is divergent, the Strength and weak anomalies cannot be balanced, or false boundaries appear in the recognition results. There is a close connection between gravity anomaly and its curvatures and subsurface geological formations. Thus they can be used to describe the geometry of subsurface formations. In this paper, a new method of balanced edge detection based on curvature is proposed by combining the fusion formula of average curvature, vertical curvature, and the degree of curvature. Model tests show that the method proposed in this paper has obvious advantages compared with the recognition effects of several traditional curvature and equalization filtering methods. The edge detection results of the proposed method have good convergence. When both positive and negative anomalies exist, this method is less affected and has anti-noise capabilities. Applying the method to real field data, the results show that the identified geological body structural boundaries are clearer and more accurate, and can be applied to more complex geological environments. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract The edge detection method can be used to delineate the position of the structural boundary of the geological body, and it occupies an important position in the processing of gravity data. The shortcomings of traditional edge detection methods are that the output boundary is divergent, the Strength and weak anomalies cannot be balanced, or false boundaries appear in the recognition results. There is a close connection between gravity anomaly and its curvatures and subsurface geological formations. Thus they can be used to describe the geometry of subsurface formations. In this paper, a new method of balanced edge detection based on curvature is proposed by combining the fusion formula of average curvature, vertical curvature, and the degree of curvature. Model tests show that the method proposed in this paper has obvious advantages compared with the recognition effects of several traditional curvature and equalization filtering methods. The edge detection results of the proposed method have good convergence. When both positive and negative anomalies exist, this method is less affected and has anti-noise capabilities. Applying the method to real field data, the results show that the identified geological body structural boundaries are clearer and more accurate, and can be applied to more complex geological environments. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract The edge detection method can be used to delineate the position of the structural boundary of the geological body, and it occupies an important position in the processing of gravity data. The shortcomings of traditional edge detection methods are that the output boundary is divergent, the Strength and weak anomalies cannot be balanced, or false boundaries appear in the recognition results. There is a close connection between gravity anomaly and its curvatures and subsurface geological formations. Thus they can be used to describe the geometry of subsurface formations. In this paper, a new method of balanced edge detection based on curvature is proposed by combining the fusion formula of average curvature, vertical curvature, and the degree of curvature. Model tests show that the method proposed in this paper has obvious advantages compared with the recognition effects of several traditional curvature and equalization filtering methods. The edge detection results of the proposed method have good convergence. When both positive and negative anomalies exist, this method is less affected and has anti-noise capabilities. Applying the method to real field data, the results show that the identified geological body structural boundaries are clearer and more accurate, and can be applied to more complex geological environments. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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A new method of balanced edge detection based on curvature for gravity data |
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https://dx.doi.org/10.1007/s11600-022-00995-1 |
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Qi, Zhang Hairong, Zhang |
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10.1007/s11600-022-00995-1 |
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2024-07-04T00:43:42.650Z |
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| score |
7.3980484 |