Two-Dimensional Magnetotelluric Parallel-Constrained-Inversion Using Artificial-Fish-Swarm Algorithm
An important way to improve the resolution of electromagnetic exploration is by using known seismic and logging data. Based on previous work, 2D magnetotelluric (MT) parallel-constrained-inversion, based on an artificial-fish-swarm algorithm is further developed. The finite-difference (FD) method wi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Magnetochemistry |
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| Online Access: | https://www.mdpi.com/2312-7481/9/2/34 |
| _version_ | 1797619750830866432 |
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| author | Zuzhi Hu Yanling Shi Xuejun Liu Zhanxiang He Ligui Xu Xiaoli Mi Juan Liu |
| author_facet | Zuzhi Hu Yanling Shi Xuejun Liu Zhanxiang He Ligui Xu Xiaoli Mi Juan Liu |
| author_sort | Zuzhi Hu |
| collection | DOAJ |
| description | An important way to improve the resolution of electromagnetic exploration is by using known seismic and logging data. Based on previous work, 2D magnetotelluric (MT) parallel-constrained-inversion, based on an artificial-fish-swarm algorithm is further developed. The finite-difference (FD) method with paralleling frequency is used for 2D MT-forward-modeling, to improve computational efficiency. The results of the FD and finite-element (FE) methods show that the accuracy of FD is comparable to FE in the case of suitable mesh-generation; however, the calculation speed is ten times faster than that of the FE. The artificial-fish-swarm algorithm is introduced and applied to parallel-constrained-inversion of 2D MT data. The results of the synthetic-model test show that the artificial-fish-swarm-inversion based on paralleling forward can recover the model well and effectively improve the inversion speed. The processing and interpretation results of the field data are verified by drilling, which shows that the proposed inversion-method has good practicability. |
| first_indexed | 2024-03-11T08:31:19Z |
| format | Article |
| id | doaj.art-3ea12d31b6c64ed2b1e4185387430c58 |
| institution | Directory Open Access Journal |
| issn | 2312-7481 |
| language | English |
| last_indexed | 2024-03-11T08:31:19Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Magnetochemistry |
| spelling | doaj.art-3ea12d31b6c64ed2b1e4185387430c582023-11-16T21:46:52ZengMDPI AGMagnetochemistry2312-74812023-01-01923410.3390/magnetochemistry9020034Two-Dimensional Magnetotelluric Parallel-Constrained-Inversion Using Artificial-Fish-Swarm AlgorithmZuzhi Hu0Yanling Shi1Xuejun Liu2Zhanxiang He3Ligui Xu4Xiaoli Mi5Juan Liu6BGP Inc., CNPC, Zhuozhou 072751, ChinaBGP Inc., CNPC, Zhuozhou 072751, ChinaBGP Inc., CNPC, Zhuozhou 072751, ChinaGuangdong Provincial Key Laboratory of Geophysical High-Resolution Imaging Technology, SUSTech, Shenzhen 518055, ChinaBGP Inc., CNPC, Zhuozhou 072751, ChinaBGP Inc., CNPC, Zhuozhou 072751, ChinaBGP Inc., CNPC, Zhuozhou 072751, ChinaAn important way to improve the resolution of electromagnetic exploration is by using known seismic and logging data. Based on previous work, 2D magnetotelluric (MT) parallel-constrained-inversion, based on an artificial-fish-swarm algorithm is further developed. The finite-difference (FD) method with paralleling frequency is used for 2D MT-forward-modeling, to improve computational efficiency. The results of the FD and finite-element (FE) methods show that the accuracy of FD is comparable to FE in the case of suitable mesh-generation; however, the calculation speed is ten times faster than that of the FE. The artificial-fish-swarm algorithm is introduced and applied to parallel-constrained-inversion of 2D MT data. The results of the synthetic-model test show that the artificial-fish-swarm-inversion based on paralleling forward can recover the model well and effectively improve the inversion speed. The processing and interpretation results of the field data are verified by drilling, which shows that the proposed inversion-method has good practicability.https://www.mdpi.com/2312-7481/9/2/34finite-difference methodartificial fish swarmmagnetotelluricparallel-constrained-inversion |
| spellingShingle | Zuzhi Hu Yanling Shi Xuejun Liu Zhanxiang He Ligui Xu Xiaoli Mi Juan Liu Two-Dimensional Magnetotelluric Parallel-Constrained-Inversion Using Artificial-Fish-Swarm Algorithm Magnetochemistry finite-difference method artificial fish swarm magnetotelluric parallel-constrained-inversion |
| title | Two-Dimensional Magnetotelluric Parallel-Constrained-Inversion Using Artificial-Fish-Swarm Algorithm |
| title_full | Two-Dimensional Magnetotelluric Parallel-Constrained-Inversion Using Artificial-Fish-Swarm Algorithm |
| title_fullStr | Two-Dimensional Magnetotelluric Parallel-Constrained-Inversion Using Artificial-Fish-Swarm Algorithm |
| title_full_unstemmed | Two-Dimensional Magnetotelluric Parallel-Constrained-Inversion Using Artificial-Fish-Swarm Algorithm |
| title_short | Two-Dimensional Magnetotelluric Parallel-Constrained-Inversion Using Artificial-Fish-Swarm Algorithm |
| title_sort | two dimensional magnetotelluric parallel constrained inversion using artificial fish swarm algorithm |
| topic | finite-difference method artificial fish swarm magnetotelluric parallel-constrained-inversion |
| url | https://www.mdpi.com/2312-7481/9/2/34 |
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