Gravity compression forward modeling and multiscale inversion based on wavelet transform
The main problems in three-dimensional gravity inversion are the non-uniqueness of the solutions and the high computational cost of large data sets. To minimize the high computational cost, we propose a new sorting method to reduce fluctuations and the high frequency of the sensitivity matrix prior...
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| Veröffentlicht in: | Applied geophysics 2018-06, Vol.15 (2), p.342-352 |
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| creator | Sun, Si-Yuan Yin, Chang-Chun Gao, Xiu-He Liu, Yun-He Ren, Xiu-Yan |
| description | The main problems in three-dimensional gravity inversion are the non-uniqueness of the solutions and the high computational cost of large data sets. To minimize the high computational cost, we propose a new sorting method to reduce fluctuations and the high frequency of the sensitivity matrix prior to applying the wavelet transform. Consequently, the sparsity and compression ratio of the sensitivity matrix are improved as well as the accuracy of the forward modeling. Furthermore, memory storage requirements are reduced and the forward modeling is accelerated compared with uncompressed forward modeling. The forward modeling results suggest that the compression ratio of the sensitivity matrix can be more than 300. Furthermore, multiscale inversion based on the wavelet transform is applied to gravity inversion. By decomposing the gravity inversion into subproblems of different scales, the non-uniqueness and stability of the gravity inversion are improved as multiscale data are considered. Finally, we applied conventional focusing inversion and multiscale inversion on simulated and measured data to demonstrate the effectiveness of the proposed gravity inversion method. |
| doi_str_mv | 10.1007/s11770-018-0676-7 |
| format | Article |
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To minimize the high computational cost, we propose a new sorting method to reduce fluctuations and the high frequency of the sensitivity matrix prior to applying the wavelet transform. Consequently, the sparsity and compression ratio of the sensitivity matrix are improved as well as the accuracy of the forward modeling. Furthermore, memory storage requirements are reduced and the forward modeling is accelerated compared with uncompressed forward modeling. The forward modeling results suggest that the compression ratio of the sensitivity matrix can be more than 300. Furthermore, multiscale inversion based on the wavelet transform is applied to gravity inversion. By decomposing the gravity inversion into subproblems of different scales, the non-uniqueness and stability of the gravity inversion are improved as multiscale data are considered. Finally, we applied conventional focusing inversion and multiscale inversion on simulated and measured data to demonstrate the effectiveness of the proposed gravity inversion method.</description><identifier>ISSN: 1672-7975</identifier><identifier>EISSN: 1993-0658</identifier><identifier>DOI: 10.1007/s11770-018-0676-7</identifier><language>eng</language><publisher>Beijing: Chinese Geophysical Society</publisher><subject>Compression ; Compression ratio ; Computational efficiency ; Computer applications ; Computer simulation ; Data ; Earth and Environmental Science ; Earth Sciences ; Geophysics/Geodesy ; Geotechnical Engineering & Applied Earth Sciences ; Gravitation ; Gravity ; High frequency ; Methods ; Model accuracy ; Modelling ; Sensitivity ; Solutions ; Stability ; Storage requirements ; Uniqueness ; Variation ; Wavelet transforms</subject><ispartof>Applied geophysics, 2018-06, Vol.15 (2), p.342-352</ispartof><rights>Editorial Office of Applied Geophysics and Springer-Verlag GmbH Germany, part of Springer Nature 2018</rights><rights>Applied Geophysics is a copyright of Springer, (2018). All Rights Reserved.</rights><rights>Copyright © Wanfang Data Co. Ltd. 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Geophys</addtitle><description>The main problems in three-dimensional gravity inversion are the non-uniqueness of the solutions and the high computational cost of large data sets. To minimize the high computational cost, we propose a new sorting method to reduce fluctuations and the high frequency of the sensitivity matrix prior to applying the wavelet transform. Consequently, the sparsity and compression ratio of the sensitivity matrix are improved as well as the accuracy of the forward modeling. Furthermore, memory storage requirements are reduced and the forward modeling is accelerated compared with uncompressed forward modeling. The forward modeling results suggest that the compression ratio of the sensitivity matrix can be more than 300. Furthermore, multiscale inversion based on the wavelet transform is applied to gravity inversion. By decomposing the gravity inversion into subproblems of different scales, the non-uniqueness and stability of the gravity inversion are improved as multiscale data are considered. Finally, we applied conventional focusing inversion and multiscale inversion on simulated and measured data to demonstrate the effectiveness of the proposed gravity inversion method.</description><subject>Compression</subject><subject>Compression ratio</subject><subject>Computational efficiency</subject><subject>Computer applications</subject><subject>Computer simulation</subject><subject>Data</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Geophysics/Geodesy</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Gravitation</subject><subject>Gravity</subject><subject>High frequency</subject><subject>Methods</subject><subject>Model accuracy</subject><subject>Modelling</subject><subject>Sensitivity</subject><subject>Solutions</subject><subject>Stability</subject><subject>Storage requirements</subject><subject>Uniqueness</subject><subject>Variation</subject><subject>Wavelet transforms</subject><issn>1672-7975</issn><issn>1993-0658</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kM9LwzAUx4MoOKd_gLeCB0_Vl2bpa44ydAqCFwVvIWteR0ebbkm3sv_ezAo7eXk_eJ_vN-HL2C2HBw6Aj4FzREiBFynkmKd4xiZcKRE3WZzHOccsRYXykl2FsAbIRZbPJux74c2-7g9J2bUbTyHUnUuqzg_G26TtLDW1WyXGxWXX9HUoTUNJ7fbkf8mlCWSTOAxmTw31Se-NC1HfXrOLyjSBbv76lH29PH_OX9P3j8Xb_Ok9NQJ5nxaKW2tNtiSwUgokksaCUBKEBJIcVamA23glqmaVKgqLRSWkRcRS5iim7H70HYyrjFvpdbfzLr6oDwe7HZosRgKxqEjejeTGd9sdhf6EZqAUqlhnkeIjVfouBE-V3vi6Nf6gOehj1HqMWkdffYxaH_-QjZoQWbcif3L-X_QDjAmCPA</recordid><startdate>20180601</startdate><enddate>20180601</enddate><creator>Sun, Si-Yuan</creator><creator>Yin, Chang-Chun</creator><creator>Gao, Xiu-He</creator><creator>Liu, Yun-He</creator><creator>Ren, Xiu-Yan</creator><general>Chinese Geophysical Society</general><general>Springer Nature B.V</general><general>College of GeoExploration Science and Technology, Jilin University, Changchun 130026, China</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TG</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H8D</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>L7M</scope><scope>M2P</scope><scope>P5Z</scope><scope>P62</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>2B.</scope><scope>4A8</scope><scope>92I</scope><scope>93N</scope><scope>PSX</scope><scope>TCJ</scope></search><sort><creationdate>20180601</creationdate><title>Gravity compression forward modeling and multiscale inversion based on wavelet transform</title><author>Sun, Si-Yuan ; 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Geophys</stitle><date>2018-06-01</date><risdate>2018</risdate><volume>15</volume><issue>2</issue><spage>342</spage><epage>352</epage><pages>342-352</pages><issn>1672-7975</issn><eissn>1993-0658</eissn><abstract>The main problems in three-dimensional gravity inversion are the non-uniqueness of the solutions and the high computational cost of large data sets. To minimize the high computational cost, we propose a new sorting method to reduce fluctuations and the high frequency of the sensitivity matrix prior to applying the wavelet transform. Consequently, the sparsity and compression ratio of the sensitivity matrix are improved as well as the accuracy of the forward modeling. Furthermore, memory storage requirements are reduced and the forward modeling is accelerated compared with uncompressed forward modeling. The forward modeling results suggest that the compression ratio of the sensitivity matrix can be more than 300. 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| subjects | Compression Compression ratio Computational efficiency Computer applications Computer simulation Data Earth and Environmental Science Earth Sciences Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Gravitation Gravity High frequency Methods Model accuracy Modelling Sensitivity Solutions Stability Storage requirements Uniqueness Variation Wavelet transforms |
| title | Gravity compression forward modeling and multiscale inversion based on wavelet transform |
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