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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">gscience</journal-id><journal-title-group><journal-title xml:lang="en">Mining Science and Technology (Russia)</journal-title><trans-title-group xml:lang="ru"><trans-title>Горные науки и технологии</trans-title></trans-title-group></journal-title-group><issn pub-type="epub">2500-0632</issn><publisher><publisher-name>The National University of Science and Technology MISiIS (NUST MISIS)</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.17073/2500-0632-2023-05-118</article-id><article-id custom-type="elpub" pub-id-type="custom">gscience-829</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>MINING ROCK PROPERTIES. ROCK MECHANICS AND GEOPHYSICS</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>СВОЙСТВА ГОРНЫХ ПОРОД. ГЕОМЕХАНИКА И ГЕОФИЗИКА</subject></subj-group></article-categories><title-group><article-title>Model of time-distance curve of electromagnetic waves diffracted on a local feature in the georadar study of permafrost zone rock layers</article-title><trans-title-group xml:lang="ru"><trans-title>Модель годографа электромагнитных волн, дифрагированных на локальном объекте при георадиолокационном изучении слоев горных пород криолитозоны</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-4179-9619</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Соколов</surname><given-names>К. О.</given-names></name><name name-style="western" xml:lang="en"><surname>Sokolov</surname><given-names>K. О.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Кирилл Олегович Соколов – кандидат технических наук, старший научный сотрудник лаборатории георадиолокации</p><p>Scopus ID 56457950500, ResearcherID P-8843-2016</p><p>г. Якутск</p></bio><bio xml:lang="en"><p>Kirill O. Sokolov – Cand. Sci. (Eng.), Senior Researcher</p><p>Scopus ID 56457950500, ResearcherID P-8843-2016</p><p>Yakutsk</p></bio><email xlink:type="simple">k.sokolov@ro.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru">Институт горного дела Севера им. Н. В. Черского СО РАН<country>Россия</country></aff><aff xml:lang="en">N.V. Chersky Mining Institute of the North of the Siberian Branch of the RAS<country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2024</year></pub-date><pub-date pub-type="epub"><day>17</day><month>10</month><year>2024</year></pub-date><volume>9</volume><issue>3</issue><fpage>199</fpage><lpage>205</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Sokolov K.О., 2024</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="ru">Соколов К.О.</copyright-holder><copyright-holder xml:lang="en">Sokolov K.О.</copyright-holder><license license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://mst.misis.ru/jour/article/view/829">https://mst.misis.ru/jour/article/view/829</self-uri><abstract><p>In GPR (georadar) studies, one of the most popular procedures for determining electromagnetic waves propagation velocity in a rock mass is the selection of theoretical hyperbolic time-distance curves and subsequent comparison with the time-distance curve obtained from a GPR measurement. This procedure is based on the model of homogeneous medium, but nowadays the subject of GPR study is often inhomogeneous media, such as horizontally layered media characteristic of loose permafrost zone sediments. The paper presents the findings of studying the formation of hyperbolic time-distance curves of georadar impulses in a horizontally layered medium without taking into account the dispersion and absorption of electromagnetic waves. On the basis of geometrical optics laws, formulas were derived to calculate the shape of the hyperbolic lineup of georadar impulses reflected from a local feature in a multilayer frozen rock mass. On the example of a permafrost zone rock mass containing a layer of unfrozen rocks, the effect of the thicknesses of rock layers and their relative dielectric permittivity on the apparent dielectric permittivity resulting from the calculation of the theoretical hyperbolic time-distance curve was shown. The conditions under which it is impossible to determine the presence of a layer of unfrozen rocks from a hyperbolic time-distance curve are also presented. The established regularities were tested on synthetic georadar radargrams calculated in the gprMax software program. The findings of the theoretical studies were confirmed by the comparison with the results of the analysis of the georadar measurements computer simulation data in the gprMax system (the relative error was less than 0.5%).</p></abstract><trans-abstract xml:lang="ru"><p>В георадиолокации одной из наиболее популярных процедур определения скорости распространения электромагнитных волн в массиве горных пород является подбор теоретических гиперболических годографов с последующим сравнением с годографом, полученным при георадиолокационном измерении. Эта процедура основана на модели однородной среды, но в настоящее время объектом изучения георадиолокации часто становятся неоднородные среды, такие как горизонтально-слоистые среды, характерные для рыхлых отложений криолитозоны. В статье представлены результаты исследования формирования гиперболических годографов георадиолокационных сигналов в горизонтально-слоистой среде без учета дисперсии и поглощения электромагнитных волн. На основе законов геометрической оптики выведены формулы, позволяющие рассчитать форму гиперболической оси синфазности георадиолокационных сигналов, отраженных от локального объекта в многослойном массиве мерзлых горных пород. На примере массива горных пород криолитозоны, содержащего слой незамерзших горных пород, показано влияние мощностей слоев горных пород и их относительной диэлектрической проницаемости на кажущуюся диэлектрическую проницаемость, получаемую в результате расчета теоретического гиперболического годографа. Также представлены условия, при которых невозможно определить наличие слоя незамерзших горных пород по гиперболическому годографу. Установленные закономерности апробированы на синтетических георадиолокационных радарограммах, рассчитанных в программе gprMax. Результаты теоретических исследований подтверждены сравнением с результатами анализа данных компьютерного моделирования георадиолокационных измерений в системе gprMax (относительная погрешность составила менее 0,5 %).</p></trans-abstract><kwd-group xml:lang="ru"><kwd>модель</kwd><kwd>массив</kwd><kwd>горные породы</kwd><kwd>диэлектрическая проницаемость</kwd><kwd>скорость</kwd><kwd>гипербола</kwd><kwd>слой</kwd><kwd>георадиолокация</kwd><kwd>криолитозона</kwd><kwd>gprMax</kwd></kwd-group><kwd-group xml:lang="en"><kwd>model</kwd><kwd>rock mass</kwd><kwd>rocks</kwd><kwd>dielectric permittivity</kwd><kwd>velocity</kwd><kwd>hyperbola</kwd><kwd>layer</kwd><kwd>georadar</kwd><kwd>permafrost zone</kwd><kwd>gprMax</kwd></kwd-group><funding-group xml:lang="ru"><funding-statement>Работа выполнена в рамках государственного задания Министерства науки и высшего образования Российской Федерации (тема № 0297-2021-0020, ЕГИСУ НИОКТР № 122011800086-1).</funding-statement></funding-group><funding-group xml:lang="en"><funding-statement>The study was performed within the framework of the state assignment of the Ministry of Science and Higher Education of the Russian Federation (Project No. 0297-2021-0020, EGISU NIOKTR (Unified State Information System for R&amp;D Accounting) No. 122011800086-1).</funding-statement></funding-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Старовойтов А. В. Интерпретация георадиолокационных данных. М.: Изд-во МГУ; 2008. 192 с.</mixed-citation><mixed-citation xml:lang="en">Starovoitov A. V. Interpretation of georadar data. Мoscow: MSU Publ. House; 2008. 192 p. (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Владов М. Л., Судакова М. С. Георадиолокация. От физических основ до перспективных направлений. М.: Изд-во ГЕОС; 2017. 240 с.</mixed-citation><mixed-citation xml:lang="en">Vladov M. L., Sudakova M. S. Georadar. From physical fundamentals to upcoming trends. Мoscow: GEOS Publ. House; 2017. 240 p. (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Lombardi F., Podd F., Solla M. From its core to the niche: insights from GPR applications. Remote Sens. 2022;14(13):3033. https://doi.org/10.3390/rs14133033</mixed-citation><mixed-citation xml:lang="en">Lombardi F., Podd F., Solla M. From its core to the niche: insights from GPR applications. Remote Sens. 2022;14(13):3033. https://doi.org/10.3390/rs14133033</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Фролов А. Д. Электрические и упругие свойства мерзлых пород и льдов. Пущино: ОНТИ ПНЦ РАН; 1998. 515 с.</mixed-citation><mixed-citation xml:lang="en">Frolov A. D. Electrical and elastic properties of frozen rocks and ice. Pushchino: ONTI PNTs RAS Publ.; 1998. 515 p. (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Persico R. Introduction to ground penetrating radar: inverse scattering and data processing. New Jersey: Wiley-IEEE Press; 2014. 392 с. https://doi.org/10.1002/9781118835647.ch2</mixed-citation><mixed-citation xml:lang="en">Persico R. Introduction to ground penetrating radar: inverse scattering and data processing. New Jersey: Wiley-IEEE Press; 2014. 392 с. https://doi.org/10.1002/9781118835647.ch2</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Jol H. M. Ground penetrating radar: theory and application. Elsevier; 2008. 544 p. https://doi.org/10.1016/B978-0-444-53348-7.X0001-4</mixed-citation><mixed-citation xml:lang="en">Jol H. M. Ground penetrating radar: theory and application. Elsevier; 2008. 544 p. https://doi.org/10.1016/B978-0-444-53348-7.X0001-4</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Дьяков А. Ю., Калашник А. И. Методические основы георадарных исследований горнотехнических объектов. Апатиты: Изд-во ФИЦ КНЦ РАН; 2021. 110 с. https://doi.org/10.37614/978.5.91137.443.3</mixed-citation><mixed-citation xml:lang="en">Dyakov A. Yu., Kalashnik A. I. Methodological fundamentals of GPR studies of mining features. Apatity: FITs KSC RAS Publ.; 2021. 110 p. (In Russ.) https://doi.org/10.37614/978.5.91137.443.3</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Solla M., Perez-Gracia V., Fontul S. A review of GPR application on transport infrastructures: troubleshooting and best practices. Remote Sens. 2021;13(4):672. https://doi.org/10.3390/rs13040672</mixed-citation><mixed-citation xml:lang="en">Solla M., Perez-Gracia V., Fontul S. A review of GPR application on transport infrastructures: troubleshooting and best practices. Remote Sens. 2021;13(4):672. https://doi.org/10.3390/rs13040672</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Wunderlich T., Wilken D., Majchczack B. S. et al. Hyperbola detection with retinanet and comparison of hyperbola fitting methods in GPR data from an archaeological site. Remote Sensing. 2022;14:3665. https://doi.org/10.3390/rs14153665</mixed-citation><mixed-citation xml:lang="en">Wunderlich T., Wilken D., Majchczack B. S. et al. Hyperbola detection with retinanet and comparison of hyperbola fitting methods in GPR data from an archaeological site. Remote Sensing. 2022;14:3665. https://doi.org/10.3390/rs14153665</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Mertens L., Persico R., Matera L., Lambot S. Automated detection of reflection hyperbolas in complex gpr images with no a priori knowledge on the medium. In: IEEE Transactions on Geoscience and Remote Sensing. 2016;1:580–596. https://doi.org/10.1109/TGRS.2015.2462727</mixed-citation><mixed-citation xml:lang="en">Mertens L., Persico R., Matera L., Lambot S. Automated detection of reflection hyperbolas in complex gpr images with no a priori knowledge on the medium. In: IEEE Transactions on Geoscience and Remote Sensing. 2016;1:580–596. https://doi.org/10.1109/TGRS.2015.2462727</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Lei W., Hou F., Xi J. et al. Automatic hyperbola detection and fitting in GPR B-scan image. Automation in Construction. 2019;106:102839. https://doi.org/10.1016/j.autcon.2019.102839</mixed-citation><mixed-citation xml:lang="en">Lei W., Hou F., Xi J. et al. Automatic hyperbola detection and fitting in GPR B-scan image. Automation in Construction. 2019;106:102839. https://doi.org/10.1016/j.autcon.2019.102839</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Dou Q., Wei L., Magee R., Cohn A. G. Real-time hyperbola recognition and fitting in GPR data. In: IEEE Transactions on Geoscience and Remote Sensing. 2017;55(1):51–62. https://doi.org/10.1109/TGRS.2016.2592679</mixed-citation><mixed-citation xml:lang="en">Dou Q., Wei L., Magee R., Cohn A. G. Real-time hyperbola recognition and fitting in GPR data. In: IEEE Transactions on Geoscience and Remote Sensing. 2017;55(1):51–62. https://doi.org/10.1109/TGRS.2016.2592679</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Daniels J. J. Fundamentals of ground penetrating radar. In: Symposium on the Application of Geophysics to Engineering and Environmental Problems. 1989;1:62–142. https://doi.org/10.4133/1.2921864</mixed-citation><mixed-citation xml:lang="en">Daniels J. J. Fundamentals of ground penetrating radar. In: Symposium on the Application of Geophysics to Engineering and Environmental Problems. 1989;1:62–142. https://doi.org/10.4133/1.2921864</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Омельяненко А. В., Федорова Л. Л. Георадиолокационные исследования многолетнемерзлых пород. Якутск: Изд-во ЯНЦ СО РАН; 2006. 136 с.</mixed-citation><mixed-citation xml:lang="en">Omelyanenko A. V., Fedorova L. L. Georadar studies of permafrost. Yakutsk: YaSC SB RAS Publ.; 2006. 136 p. (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Якупов В. С. Геофизика криолитозоны. Якутск: Изд-во Якутского госуниверситета; 2008. 342 с.</mixed-citation><mixed-citation xml:lang="en">Yakupov V. S. Geophysics of permafrost zone. Yakutsk: Yakutsk State University Publ.; 2008. 342 p. (In Russ.)</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Warren C., Giannopoulos A., Giannakis I. gprMax: Open source software to simulate electromagnetic wave propagation for Ground Penetrating Radar. Computer Physics Communications. 2016;209:163–170. https://doi.org/10.1016/j.cpc.2016.08.020</mixed-citation><mixed-citation xml:lang="en">Warren C., Giannopoulos A., Giannakis I. gprMax: Open source software to simulate electromagnetic wave propagation for Ground Penetrating Radar. Computer Physics Communications. 2016;209:163–170. https://doi.org/10.1016/j.cpc.2016.08.020</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">Wunderlich T., Wilken D., Majchczack B. S., et al. Hyperbola detection with RetinaNet and comparison of hyperbola fitting methods in GPR data from an archaeological site. Remote Sensing. 2022;14:3665. https://doi.org/10.3390/rs14153665</mixed-citation><mixed-citation xml:lang="en">Wunderlich T., Wilken D., Majchczack B. S., et al. Hyperbola detection with RetinaNet and comparison of hyperbola fitting methods in GPR data from an archaeological site. Remote Sensing. 2022;14:3665. https://doi.org/10.3390/rs14153665</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Dewantara D., Parnadi W. W. Automatic hyperbola detection and apex extraction using convolutional neural network on GPR data. Journal of Physics: Conference Series. 2022;1:012027. https://doi.org/10.1088/1742-6596/2243/1/012027</mixed-citation><mixed-citation xml:lang="en">Dewantara D., Parnadi W. W. Automatic hyperbola detection and apex extraction using convolutional neural network on GPR data. Journal of Physics: Conference Series. 2022;1:012027. https://doi.org/10.1088/1742-6596/2243/1/012027</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">Wang H., Ouyang S., Liao K.-F., Jin L.-N. GPR B-SCAN image hyperbola detection method based on deep learning. Acta Electronica Sinica. 2021;49(5):953-963. https://doi.org/10.12263/DZXB.20200635</mixed-citation><mixed-citation xml:lang="en">Wang H., Ouyang S., Liao K.-F., Jin L.-N. GPR B-SCAN image hyperbola detection method based on deep learning. Acta Electronica Sinica. 2021;49(5):953-963. https://doi.org/10.12263/DZXB.20200635</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
