{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T22:08:55Z","timestamp":1774044535907,"version":"3.50.1"},"reference-count":21,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2018,3,19]],"date-time":"2018-03-19T00:00:00Z","timestamp":1521417600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2016YFC0600505"],"award-info":[{"award-number":["2016YFC0600505"]}]},{"name":"the Scientific Research Program of the Education Department of Jilin Province","award":["(will be issued soon)"],"award-info":[{"award-number":["(will be issued soon)"]}]},{"name":"Provincial Training Program of Innovation and Entrepreneurship for Undergraduates","award":["2017S1027, 2017S1030"],"award-info":[{"award-number":["2017S1027, 2017S1030"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Using traditional time-frequency analysis methods, it is possible to delineate the time-frequency structures of ground-penetrating radar (GPR) data. A series of applications based on time-frequency analysis were proposed for the GPR data processing and imaging. With respect to signal processing, GPR data are typically non-stationary, which limits the applications of these methods moving forward. Empirical mode decomposition (EMD) provides alternative solutions with a fresh perspective. With EMD, GPR data are decomposed into a set of sub-components, i.e., the intrinsic mode functions (IMFs). However, the mode-mixing effect may also bring some negatives. To utilize the IMFs\u2019 benefits, and avoid the negatives of the EMD, we introduce a new decomposition scheme termed variational mode decomposition (VMD) for GPR data processing for imaging. Based on the decomposition results of the VMD, we propose a new method which we refer as \u201cthe IMF-slice\u201d. In the proposed method, the IMFs are generated by the VMD trace by trace, and then each IMF is sorted and recorded into different profiles (i.e., the IMF-slices) according to its center frequency. Using IMF-slices, the GPR data can be divided into several IMF-slices, each of which delineates a main vibration mode, and some subsurface layers and geophysical events can be identified more clearly. The effectiveness of the proposed method is tested using synthetic benchmark signals, laboratory data and the field dataset.<\/jats:p>","DOI":"10.3390\/rs10030476","type":"journal-article","created":{"date-parts":[[2018,3,20]],"date-time":"2018-03-20T06:57:11Z","timestamp":1521529031000},"page":"476","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["IMF-Slices for GPR Data Processing Using Variational Mode Decomposition Method"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2016-9420","authenticated-orcid":false,"given":"Xuebing","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China"}]},{"given":"Enhedelihai","family":"Nilot","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}]},{"given":"Xuan","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}]},{"given":"Qianci","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Guilin University of Technology, Guilin 541006, China"}]},{"given":"Zhijia","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. 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