A New Approach to Model Parameter Determination of Self-Potential Data using Memory-based Hybrid Dragonfly Algorithm
| Main Authors: | Ramadhani, Irwansyah; Department of Physics, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo Surabaya-60111, Indonesia, Sungkono, Sungkono; Department of Physics, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo Surabaya-60111, Indonesia |
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| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
International Journal on Advanced Science, Engineering and Information Technology
, 2019
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| Subjects: | |
| Online Access: |
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/6587 http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/6587/pdf_1236 |
| ctrlnum |
article-6587 |
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| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title lang="en-US">A New Approach to Model Parameter Determination of Self-Potential Data using Memory-based Hybrid Dragonfly Algorithm</title><creator>Ramadhani, Irwansyah; Department of Physics, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo Surabaya-60111, Indonesia</creator><creator>Sungkono, Sungkono; Department of Physics, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo Surabaya-60111, Indonesia</creator><subject lang="en-US">memory-based hybrid dragonfly algorithm; posterior distribution model; model uncertainty; self-potential.</subject><description lang="en-US">A new approach based on global optimization technique is applied to invert Self-Potential (SP) data which is a highly nonlinear inversion problem. This technique is called Memory-based Hybrid Dragonfly Algorithm (MHDA). This algorithm is proposed to balance out the high exploration behavior of Dragonfly Algorithm (DA), which causes a low convergence rate and often leads to the local optimum solution. MHDA was developed by adding internal memory and iterative level hybridization into DA which successfully balanced the exploration and exploitation behaviors of DA. In order to assess the performance of MHDA, it is firstly implemented to invert the single and multiple noises contaminated in synthetic SP data, which were caused by several simple geometries of buried anomalies: sphere and inclined sheet. MHDA is subsequently implemented to invert the field SP data for several cases: buried metallic drum, landslide, and Lumpur Sidoarjo (LUSI) embankment anomalies. As a stochastic method, MHDA is able to provide Posterior Distribution Model (PDM), which contains possible solutions of the SP data inversion. PDM is obtained from the exploration behavior of MHDA. All accepted models as PDM have a lower misfit value than the specified tolerance value of the objective function in the inversion process. In this research, solutions of the synthetic and field SP data inversions are estimated by the median value of PDM. Furthermore, the uncertainty value of obtained solutions can be estimated by the standard deviation value of PDM. The inversion results of synthetic and field SP data show that MHDA is able to estimate the solutions and the uncertainty values of solutions well. It indicates that MHDA is a good and an innovative technique to be implemented in solving the SP data inversion problem.</description><publisher lang="en-US">International Journal on Advanced Science, Engineering and Information Technology</publisher><contributor lang="en-US"/><date>2019-10-31</date><type>Journal:Article</type><type>Other:info:eu-repo/semantics/publishedVersion</type><type>Other:</type><type>File:application/pdf</type><identifier>http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/6587</identifier><identifier>10.18517/ijaseit.9.5.6587</identifier><source lang="en-US">International Journal on Advanced Science, Engineering and Information Technology; Vol 9, No 5 (2019); 1772-1782</source><source>2460-6952</source><source>2088-5334</source><language>eng</language><relation>http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/6587/pdf_1236</relation><rights lang="en-US">Authors who publish with this journal agree to the following terms:Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).</rights><recordID>article-6587</recordID></dc>
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| language |
eng |
| format |
Journal:Article Journal Other:info:eu-repo/semantics/publishedVersion Other Other: File:application/pdf File Journal:eJournal |
| author |
Ramadhani, Irwansyah; Department of Physics, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo Surabaya-60111, Indonesia Sungkono, Sungkono; Department of Physics, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo Surabaya-60111, Indonesia |
| title |
A New Approach to Model Parameter Determination of Self-Potential Data using Memory-based Hybrid Dragonfly Algorithm |
| publisher |
International Journal on Advanced Science, Engineering and Information Technology |
| publishDate |
2019 |
| topic |
memory-based hybrid dragonfly algorithm posterior distribution model model uncertainty self-potential |
| url |
http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/6587 http://insightsociety.org/ojaseit/index.php/ijaseit/article/view/6587/pdf_1236 |
| contents |
A new approach based on global optimization technique is applied to invert Self-Potential (SP) data which is a highly nonlinear inversion problem. This technique is called Memory-based Hybrid Dragonfly Algorithm (MHDA). This algorithm is proposed to balance out the high exploration behavior of Dragonfly Algorithm (DA), which causes a low convergence rate and often leads to the local optimum solution. MHDA was developed by adding internal memory and iterative level hybridization into DA which successfully balanced the exploration and exploitation behaviors of DA. In order to assess the performance of MHDA, it is firstly implemented to invert the single and multiple noises contaminated in synthetic SP data, which were caused by several simple geometries of buried anomalies: sphere and inclined sheet. MHDA is subsequently implemented to invert the field SP data for several cases: buried metallic drum, landslide, and Lumpur Sidoarjo (LUSI) embankment anomalies. As a stochastic method, MHDA is able to provide Posterior Distribution Model (PDM), which contains possible solutions of the SP data inversion. PDM is obtained from the exploration behavior of MHDA. All accepted models as PDM have a lower misfit value than the specified tolerance value of the objective function in the inversion process. In this research, solutions of the synthetic and field SP data inversions are estimated by the median value of PDM. Furthermore, the uncertainty value of obtained solutions can be estimated by the standard deviation value of PDM. The inversion results of synthetic and field SP data show that MHDA is able to estimate the solutions and the uncertainty values of solutions well. It indicates that MHDA is a good and an innovative technique to be implemented in solving the SP data inversion problem. |
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IOS1116.article-6587 |
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Indonesian Society for Knowledge and Human Development |
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International Journal on Advanced Science, Engineering and Information Technology |
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Program Komputer dan Teknologi Informasi |
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DKI JAKARTA |
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