Three-Dimensional Inversion of Semi-Airborne Transient Electromagnetic Data Based on a Particle Swarm Optimization-Gradient Descent Algorithm
Semi-airborne transient electromagnetics (SATEM) is a geophysical survey tool known for its ability to perform three-dimensional (3D) observations and collect high-density data in large volumes. However, SATEM data processing is presently restricted to 3D model-driven inversion, which is not conduci...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-03-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/6/3042 |
| _version_ | 1797473005850329088 |
|---|---|
| author | Yiming He Guoqiang Xue Weiying Chen Zhongbin Tian |
| author_facet | Yiming He Guoqiang Xue Weiying Chen Zhongbin Tian |
| author_sort | Yiming He |
| collection | DOAJ |
| description | Semi-airborne transient electromagnetics (SATEM) is a geophysical survey tool known for its ability to perform three-dimensional (3D) observations and collect high-density data in large volumes. However, SATEM data processing is presently restricted to 3D model-driven inversion, which is not conducive to detailed surveys. This paper presents a new 3D model- and data-driven inversion algorithm using the particle swarm optimization (PSO) and gradient descent (GD) algorithms. PSO is used to suppress the multiplicity of solutions associated with inverse problems, and the GD algorithm is employed to accelerate the convergence of the inversion process. For the PSO-GD algorithm, a new model-updating equation is established and a cosine probability function is introduced as a weighting term for PSO and GD algorithms to ensure a smooth transition between the two algorithms in the iterative process. The <i>α</i>-trimmed filter function is used as a regularization constraint to smooth the model. The stability and reliability of the PSO-GD algorithm are verified through numerical simulations. Finally, the new algorithm is applied to the processing of SATEM measurements of the Qinshui coal mine in Jincheng, Shanxi Province, China. |
| first_indexed | 2024-03-09T20:09:07Z |
| format | Article |
| id | doaj.art-d36a16cc31664afb80cb42c427050e5f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:09:07Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d36a16cc31664afb80cb42c427050e5f2023-11-24T00:22:52ZengMDPI AGApplied Sciences2076-34172022-03-01126304210.3390/app12063042Three-Dimensional Inversion of Semi-Airborne Transient Electromagnetic Data Based on a Particle Swarm Optimization-Gradient Descent AlgorithmYiming He0Guoqiang Xue1Weiying Chen2Zhongbin Tian3Key Laboratory of Mineral Resources, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, ChinaKey Laboratory of Mineral Resources, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, ChinaKey Laboratory of Mineral Resources, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, ChinaShanxi Coal Geophysical Surveying Exploration Institute, Jinzhong 030600, ChinaSemi-airborne transient electromagnetics (SATEM) is a geophysical survey tool known for its ability to perform three-dimensional (3D) observations and collect high-density data in large volumes. However, SATEM data processing is presently restricted to 3D model-driven inversion, which is not conducive to detailed surveys. This paper presents a new 3D model- and data-driven inversion algorithm using the particle swarm optimization (PSO) and gradient descent (GD) algorithms. PSO is used to suppress the multiplicity of solutions associated with inverse problems, and the GD algorithm is employed to accelerate the convergence of the inversion process. For the PSO-GD algorithm, a new model-updating equation is established and a cosine probability function is introduced as a weighting term for PSO and GD algorithms to ensure a smooth transition between the two algorithms in the iterative process. The <i>α</i>-trimmed filter function is used as a regularization constraint to smooth the model. The stability and reliability of the PSO-GD algorithm are verified through numerical simulations. Finally, the new algorithm is applied to the processing of SATEM measurements of the Qinshui coal mine in Jincheng, Shanxi Province, China.https://www.mdpi.com/2076-3417/12/6/3042semi-airborne transient electromagneticsthree-dimensional inversionparticle swarm optimizationgradient descenttrim filter function |
| spellingShingle | Yiming He Guoqiang Xue Weiying Chen Zhongbin Tian Three-Dimensional Inversion of Semi-Airborne Transient Electromagnetic Data Based on a Particle Swarm Optimization-Gradient Descent Algorithm Applied Sciences semi-airborne transient electromagnetics three-dimensional inversion particle swarm optimization gradient descent trim filter function |
| title | Three-Dimensional Inversion of Semi-Airborne Transient Electromagnetic Data Based on a Particle Swarm Optimization-Gradient Descent Algorithm |
| title_full | Three-Dimensional Inversion of Semi-Airborne Transient Electromagnetic Data Based on a Particle Swarm Optimization-Gradient Descent Algorithm |
| title_fullStr | Three-Dimensional Inversion of Semi-Airborne Transient Electromagnetic Data Based on a Particle Swarm Optimization-Gradient Descent Algorithm |
| title_full_unstemmed | Three-Dimensional Inversion of Semi-Airborne Transient Electromagnetic Data Based on a Particle Swarm Optimization-Gradient Descent Algorithm |
| title_short | Three-Dimensional Inversion of Semi-Airborne Transient Electromagnetic Data Based on a Particle Swarm Optimization-Gradient Descent Algorithm |
| title_sort | three dimensional inversion of semi airborne transient electromagnetic data based on a particle swarm optimization gradient descent algorithm |
| topic | semi-airborne transient electromagnetics three-dimensional inversion particle swarm optimization gradient descent trim filter function |
| url | https://www.mdpi.com/2076-3417/12/6/3042 |
| work_keys_str_mv | AT yiminghe threedimensionalinversionofsemiairbornetransientelectromagneticdatabasedonaparticleswarmoptimizationgradientdescentalgorithm AT guoqiangxue threedimensionalinversionofsemiairbornetransientelectromagneticdatabasedonaparticleswarmoptimizationgradientdescentalgorithm AT weiyingchen threedimensionalinversionofsemiairbornetransientelectromagneticdatabasedonaparticleswarmoptimizationgradientdescentalgorithm AT zhongbintian threedimensionalinversionofsemiairbornetransientelectromagneticdatabasedonaparticleswarmoptimizationgradientdescentalgorithm |