Magnetic anomaly inversion through the novel barnacles mating optimization algorithm
Abstract Dealing with the ill-posed and non-unique nature of the non-linear geophysical inverse problem via local optimizers requires the use of some regularization methods, constraints, and prior information about the Earth's complex interior. Another difficulty is that the success of local se...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-12-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-26265-0 |
| _version_ | 1797973658304512000 |
|---|---|
| author | Hanbing Ai Khalid S. Essa Yunus Levent Ekinci Çağlayan Balkaya Hongxing Li Yves Géraud |
| author_facet | Hanbing Ai Khalid S. Essa Yunus Levent Ekinci Çağlayan Balkaya Hongxing Li Yves Géraud |
| author_sort | Hanbing Ai |
| collection | DOAJ |
| description | Abstract Dealing with the ill-posed and non-unique nature of the non-linear geophysical inverse problem via local optimizers requires the use of some regularization methods, constraints, and prior information about the Earth's complex interior. Another difficulty is that the success of local search algorithms depends on a well-designed initial model located close to the parameter set providing the global minimum. On the other hand, global optimization and metaheuristic algorithms that have the ability to scan almost the entire model space do not need an assertive initial model. Thus, these approaches are increasingly incorporated into parameter estimation studies and are also gaining more popularity in the geophysical community. In this study we present the Barnacles Mating Optimizer (BMO), a recently proposed global optimizer motivated by the special mating behavior of barnacles, to interpret magnetic anomalies. This is the first example in the literature of BMO application to a geophysical inverse problem. After performing modal analyses and parameter tuning processes, BMO has been tested on simulated magnetic anomalies generated from hypothetical models and subsequently applied to three real anomalies that are chromite deposit, uranium deposit and Mesozoic dike. A second moving average (SMA) scheme to eliminate regional anomalies from observed anomalies has been examined and certified. Post-inversion uncertainty assessment analyses have been also implemented to understand the reliability of the solutions achieved. Moreover, BMO’s solutions for convergence rate, stability, robustness and accuracy have been compared with the solutions of the commonly used standard Particle Swarm Optimization (sPSO) algorithm. The results have shown that the BMO algorithm can scan the model parameter space more extensively without affecting its ability to consistently approach the unique global minimum in this presented inverse problem. We, therefore, recommend the use of competitive BMO in model parameter estimation studies performed with other geophysical methods. |
| first_indexed | 2024-04-11T04:07:46Z |
| format | Article |
| id | doaj.art-c98c707ded864f0f97b752dcb2cde984 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-11T04:07:46Z |
| publishDate | 2022-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-c98c707ded864f0f97b752dcb2cde9842023-01-01T12:18:40ZengNature PortfolioScientific Reports2045-23222022-12-0112112710.1038/s41598-022-26265-0Magnetic anomaly inversion through the novel barnacles mating optimization algorithmHanbing Ai0Khalid S. Essa1Yunus Levent Ekinci2Çağlayan Balkaya3Hongxing Li4Yves Géraud5Laboratory for Fundamental Science on Radioactive Geology and Exploration Technology, East China University of TechnologyGeophysics Department, Faculty of Science, Cairo UniversityDepartment of Art History, Bitlis Eren UniversityDepartment of Geophysical Engineering, Süleyman Demirel UniversityLaboratory for Fundamental Science on Radioactive Geology and Exploration Technology, East China University of TechnologyGeoRessources Laboratory, University of LorraineAbstract Dealing with the ill-posed and non-unique nature of the non-linear geophysical inverse problem via local optimizers requires the use of some regularization methods, constraints, and prior information about the Earth's complex interior. Another difficulty is that the success of local search algorithms depends on a well-designed initial model located close to the parameter set providing the global minimum. On the other hand, global optimization and metaheuristic algorithms that have the ability to scan almost the entire model space do not need an assertive initial model. Thus, these approaches are increasingly incorporated into parameter estimation studies and are also gaining more popularity in the geophysical community. In this study we present the Barnacles Mating Optimizer (BMO), a recently proposed global optimizer motivated by the special mating behavior of barnacles, to interpret magnetic anomalies. This is the first example in the literature of BMO application to a geophysical inverse problem. After performing modal analyses and parameter tuning processes, BMO has been tested on simulated magnetic anomalies generated from hypothetical models and subsequently applied to three real anomalies that are chromite deposit, uranium deposit and Mesozoic dike. A second moving average (SMA) scheme to eliminate regional anomalies from observed anomalies has been examined and certified. Post-inversion uncertainty assessment analyses have been also implemented to understand the reliability of the solutions achieved. Moreover, BMO’s solutions for convergence rate, stability, robustness and accuracy have been compared with the solutions of the commonly used standard Particle Swarm Optimization (sPSO) algorithm. The results have shown that the BMO algorithm can scan the model parameter space more extensively without affecting its ability to consistently approach the unique global minimum in this presented inverse problem. We, therefore, recommend the use of competitive BMO in model parameter estimation studies performed with other geophysical methods.https://doi.org/10.1038/s41598-022-26265-0 |
| spellingShingle | Hanbing Ai Khalid S. Essa Yunus Levent Ekinci Çağlayan Balkaya Hongxing Li Yves Géraud Magnetic anomaly inversion through the novel barnacles mating optimization algorithm Scientific Reports |
| title | Magnetic anomaly inversion through the novel barnacles mating optimization algorithm |
| title_full | Magnetic anomaly inversion through the novel barnacles mating optimization algorithm |
| title_fullStr | Magnetic anomaly inversion through the novel barnacles mating optimization algorithm |
| title_full_unstemmed | Magnetic anomaly inversion through the novel barnacles mating optimization algorithm |
| title_short | Magnetic anomaly inversion through the novel barnacles mating optimization algorithm |
| title_sort | magnetic anomaly inversion through the novel barnacles mating optimization algorithm |
| url | https://doi.org/10.1038/s41598-022-26265-0 |
| work_keys_str_mv | AT hanbingai magneticanomalyinversionthroughthenovelbarnaclesmatingoptimizationalgorithm AT khalidsessa magneticanomalyinversionthroughthenovelbarnaclesmatingoptimizationalgorithm AT yunusleventekinci magneticanomalyinversionthroughthenovelbarnaclesmatingoptimizationalgorithm AT caglayanbalkaya magneticanomalyinversionthroughthenovelbarnaclesmatingoptimizationalgorithm AT hongxingli magneticanomalyinversionthroughthenovelbarnaclesmatingoptimizationalgorithm AT yvesgeraud magneticanomalyinversionthroughthenovelbarnaclesmatingoptimizationalgorithm |