Soil water content estimation by using ground penetrating radar data full waveform inversion with grey wolf optimizer algorithm
Abstract Soil water content (SWC) estimation is important for many areas including hydrology, agriculture, soil science, and environmental science. Ground penetrating radar (GPR) is a promising geophysical method for SWC estimation. However, at present, most of the studies are based on partial infor...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-01-01
|
| Series: | Vadose Zone Journal |
| Online Access: | https://doi.org/10.1002/vzj2.20379 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850079304828846080 |
|---|---|
| author | M. H. Zhang X. Feng M. Bano C. Liu Q. Liu X. Wang |
| author_facet | M. H. Zhang X. Feng M. Bano C. Liu Q. Liu X. Wang |
| author_sort | M. H. Zhang |
| collection | DOAJ |
| description | Abstract Soil water content (SWC) estimation is important for many areas including hydrology, agriculture, soil science, and environmental science. Ground penetrating radar (GPR) is a promising geophysical method for SWC estimation. However, at present, most of the studies are based on partial information of GPR, like travel time or amplitude information, to invert the SWC. Full waveform inversion (FWI) can use the information of the entire waveform, which can improve the accuracy of parameter estimation. This study proposes a novel SWC estimation scheme by using the FWI of GPR, optimized by the grey wolf optimizer (GWO) algorithm. The proposed scheme includes a petrophysical relationship to link the SWC with the relative dielectric permittivity, 1D GPR forward modeling, and a GWO optimization algorithm. First, numerical modeling was carried out, and the proposed scheme was applied to both noise‐free and noisy data to verify its applicability. Then, the proposed method was applied to data collected from a field experimental site. These results, derived from both synthetic and real datasets, show that the proposed inversion scheme resulted in a good match between the observed and calculated GPR data. In the numerical modeling, it was observed that the SWC could be inverted accurately, even when noise was present in the data. These demonstrate that the GWO method can be applied for the quantitative interpretation of GPR data. The proposed scheme shows potential for SWC estimation by using GPR full waveform data. |
| format | Article |
| id | doaj-art-c7117bce80714c7a924927de630efa8a |
| institution | DOAJ |
| issn | 1539-1663 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Vadose Zone Journal |
| spelling | doaj-art-c7117bce80714c7a924927de630efa8a2025-08-20T02:45:15ZengWileyVadose Zone Journal1539-16632025-01-01241n/an/a10.1002/vzj2.20379Soil water content estimation by using ground penetrating radar data full waveform inversion with grey wolf optimizer algorithmM. H. Zhang0X. Feng1M. Bano2C. Liu3Q. Liu4X. Wang5College of Geo‐Exploration Science and Technology Jilin University Changchun ChinaCollege of Geo‐Exploration Science and Technology Jilin University Changchun ChinaITES UMR‐7063, EOST University of Strasbourg Strasbourg FranceCollege of Geo‐Exploration Science and Technology Jilin University Changchun ChinaState Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics Chinese Academy of Sciences Wuhan ChinaCollege of Geo‐Exploration Science and Technology Jilin University Changchun ChinaAbstract Soil water content (SWC) estimation is important for many areas including hydrology, agriculture, soil science, and environmental science. Ground penetrating radar (GPR) is a promising geophysical method for SWC estimation. However, at present, most of the studies are based on partial information of GPR, like travel time or amplitude information, to invert the SWC. Full waveform inversion (FWI) can use the information of the entire waveform, which can improve the accuracy of parameter estimation. This study proposes a novel SWC estimation scheme by using the FWI of GPR, optimized by the grey wolf optimizer (GWO) algorithm. The proposed scheme includes a petrophysical relationship to link the SWC with the relative dielectric permittivity, 1D GPR forward modeling, and a GWO optimization algorithm. First, numerical modeling was carried out, and the proposed scheme was applied to both noise‐free and noisy data to verify its applicability. Then, the proposed method was applied to data collected from a field experimental site. These results, derived from both synthetic and real datasets, show that the proposed inversion scheme resulted in a good match between the observed and calculated GPR data. In the numerical modeling, it was observed that the SWC could be inverted accurately, even when noise was present in the data. These demonstrate that the GWO method can be applied for the quantitative interpretation of GPR data. The proposed scheme shows potential for SWC estimation by using GPR full waveform data.https://doi.org/10.1002/vzj2.20379 |
| spellingShingle | M. H. Zhang X. Feng M. Bano C. Liu Q. Liu X. Wang Soil water content estimation by using ground penetrating radar data full waveform inversion with grey wolf optimizer algorithm Vadose Zone Journal |
| title | Soil water content estimation by using ground penetrating radar data full waveform inversion with grey wolf optimizer algorithm |
| title_full | Soil water content estimation by using ground penetrating radar data full waveform inversion with grey wolf optimizer algorithm |
| title_fullStr | Soil water content estimation by using ground penetrating radar data full waveform inversion with grey wolf optimizer algorithm |
| title_full_unstemmed | Soil water content estimation by using ground penetrating radar data full waveform inversion with grey wolf optimizer algorithm |
| title_short | Soil water content estimation by using ground penetrating radar data full waveform inversion with grey wolf optimizer algorithm |
| title_sort | soil water content estimation by using ground penetrating radar data full waveform inversion with grey wolf optimizer algorithm |
| url | https://doi.org/10.1002/vzj2.20379 |
| work_keys_str_mv | AT mhzhang soilwatercontentestimationbyusinggroundpenetratingradardatafullwaveforminversionwithgreywolfoptimizeralgorithm AT xfeng soilwatercontentestimationbyusinggroundpenetratingradardatafullwaveforminversionwithgreywolfoptimizeralgorithm AT mbano soilwatercontentestimationbyusinggroundpenetratingradardatafullwaveforminversionwithgreywolfoptimizeralgorithm AT cliu soilwatercontentestimationbyusinggroundpenetratingradardatafullwaveforminversionwithgreywolfoptimizeralgorithm AT qliu soilwatercontentestimationbyusinggroundpenetratingradardatafullwaveforminversionwithgreywolfoptimizeralgorithm AT xwang soilwatercontentestimationbyusinggroundpenetratingradardatafullwaveforminversionwithgreywolfoptimizeralgorithm |