Geoacoustic and geophysical data‐driven seafloor sediment classification through machine learning algorithms with property‐centered oversampling techniques
This study aims to classify seafloor sediments using physics‐inspired and data‐driven soil models combined with machine learning algorithms and oversampling techniques. The field data used for the input variables include porosity, S‐ and P‐wave velocities and depth. The soil information reported in...
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| Published in: | Computer-aided civil and infrastructure engineering Vol. 39; no. 14; pp. 2105 - 2121 |
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| Language: | English |
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| ISSN: | 1093-9687, 1467-8667 |
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| Abstract | This study aims to classify seafloor sediments using physics‐inspired and data‐driven soil models combined with machine learning algorithms and oversampling techniques. The field data used for the input variables include porosity, S‐ and P‐wave velocities and depth. The soil information reported in the original literature and the “six reference sediments” and effective stress‐versus‐depth models proposed by the previous study confirm the sediment type across all of the input variables. We use three machine learning algorithms and four oversampling methods to enhance the performance accuracy and overcome data imbalance in the minority class. The results show that the averaged accuracy of sediment classification with original data corresponds to 0.88 for porosity, 0.61 for S‐wave velocity, and 0.97 for P‐wave velocity. In particular, the enhanced accuracy with oversampled input variables becomes more pronounced when the depth data are considered in a dataset. The class‐oriented grouping method newly proposed in this study appears to be a robust approach to enhancing performance. Surprisingly, model‐based input variables lead to the best performance in all cases. The proposed analyses conducted using machine learning algorithms and oversampling techniques within the physics‐inspired models could be extended to obtain a first‐order assessment of marine sediment properties. |
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| AbstractList | This study aims to classify seafloor sediments using physics‐inspired and data‐driven soil models combined with machine learning algorithms and oversampling techniques. The field data used for the input variables include porosity, S‐ and P‐wave velocities and depth. The soil information reported in the original literature and the “six reference sediments” and effective stress‐versus‐depth models proposed by the previous study confirm the sediment type across all of the input variables. We use three machine learning algorithms and four oversampling methods to enhance the performance accuracy and overcome data imbalance in the minority class. The results show that the averaged accuracy of sediment classification with original data corresponds to 0.88 for porosity, 0.61 for S‐wave velocity, and 0.97 for P‐wave velocity. In particular, the enhanced accuracy with oversampled input variables becomes more pronounced when the depth data are considered in a dataset. The class‐oriented grouping method newly proposed in this study appears to be a robust approach to enhancing performance. Surprisingly, model‐based input variables lead to the best performance in all cases. The proposed analyses conducted using machine learning algorithms and oversampling techniques within the physics‐inspired models could be extended to obtain a first‐order assessment of marine sediment properties. |
| Author | Park, Junghee Yoon, Hyung‐Koo Lee, Jong‐Sub |
| Author_xml | – sequence: 1 givenname: Junghee surname: Park fullname: Park, Junghee organization: Incheon National University – sequence: 2 givenname: Jong‐Sub surname: Lee fullname: Lee, Jong‐Sub organization: Korea University – sequence: 3 givenname: Hyung‐Koo surname: Yoon fullname: Yoon, Hyung‐Koo email: hyungkoo@dju.ac.kr organization: Daejeon University |
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| Cites_doi | 10.1111/mice.12556 10.1142/S0218213014300014 10.1142/S0218213014300026 10.1190/1.1440676 10.1007/s12559-017-9485-1 10.1016/j.asoc.2019.105887 10.1007/s00521-019-04359-7 10.1016/j.enggeo.2020.105930 10.1061/(ASCE)GT.1943-5606.0002426 10.1007/s12559-015-9370-8 10.1007/978-94-011-3568-9_34 10.1162/neco.1996.8.7.1341 10.1016/j.enggeo.2022.106615 10.1016/j.enggeo.2022.106675 10.1061/(ASCE)GT.1943-5606.0001482 10.1111/mice.12772 10.1111/exsy.12357 10.1142/S0218001416390018 10.1193/1.1852561 10.1016/j.enggeo.2019.105145 10.1139/cgj-2016-0044 10.1111/mice.12662 10.1038/s41598-021-86137-x 10.1016/j.enggeo.2022.106720 10.1109/TNNLS.2017.2682102 10.1016/j.enggeo.2021.106044 10.1109/EIT.2018.8500086 |
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| Copyright | 2023 The Authors. published by Wiley Periodicals LLC on behalf of Editor. 2023. This article is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| SubjectTerms | Accuracy Algorithms Classification (sedimentation) Effectiveness Machine learning Ocean floor Oversampling Performance enhancement S waves Sediments Soil porosity Wave velocity |
| Title | Geoacoustic and geophysical data‐driven seafloor sediment classification through machine learning algorithms with property‐centered oversampling techniques |
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