Construction of a velocity model of shear wave for complexly structured geological medium using neural network (by example of data of the South Saspian basin)

Simple item page
dc.citation.epage80
dc.citation.issue1 (28)
dc.citation.journalTitleГеодинаміка
dc.citation.spage71
dc.contributor.affiliationІнститут геології та геофізики АНА
dc.contributor.affiliationInstitute of Geology and Geophysics of ANAS
dc.contributor.authorАгаєв, Х. Б.
dc.contributor.authorКулієв, Р. Г.
dc.contributor.authorЯкубова, Ш. З.
dc.contributor.authorAghayev, Kh. B.
dc.contributor.authorKuliyev, R. H.
dc.contributor.authorYaqubova, Sh. Z.
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2023-06-20T07:49:32Z
dc.date.available2023-06-20T07:49:32Z
dc.date.created2020-02-25
dc.date.issued2020-02-25
dc.description.abstractМета. Розроблення методу прогнозування дво(три)вимірної швидкісної моделі середовища поперечної хвилі. Досліджено складноструктурне геологічне середовище на основі геофізичних і геологічних даних із застосуванням штучної нейронної мережі. Метод передбачає побудову та використання моделей середовища за даними геофізичних досліджень свердловин, сейсморозвідки та інших наземних геофізичних методів. На відміну від існуючих методів, у пропонованому використовують також додаткові дані про середовище: про термодинамічний стан середовища, стратиграфічну приуроченість відкладень, літологію порід, розподіл кластерів даних, фізичні властивості середовища тощо. Згідно з методом, спочатку будують одновимірні моделі за різними властивостями середовища на основі даних комплексу геофізичних досліджень свердловин. Потім за сукупністю моделей нейронну мережу вивчають для прогнозування швидкості поперечної хвилі, відтак будують дво(три)вимірні моделі середовища за результатами наземних геофізичних досліджень. З використанням сукупності цих моделей прогнозують дво(три)вимірну швидкісну модель поперечної хвилі. Результати. Із застосуванням методу спрогнозовано швидкісну модель поперечної хвилі для складноструктурного геологічного середовища Південно-Каспійського басейну. Наукова новизна. Збільшенням кількості типів використаних даних забезпечується підвищення точності прогнозування моделі середовища. Практична цінність. Підвищення ефективності сейсморозвідки під час визначення нафтогазонасиченості, пружного геодинамічного стану та інших фізичних властивостей геологічного середовища.
dc.description.abstractObject. Development of a method for predicting a two-three dimensional velocity model of a medium by using a shear wave. Complexly structured geological medium is studied on the basis of geophysical and geological data using an artificial neural network. Method. It provides the construction and use of medium models according to geophysical well logging data and other terrestrial geophysical methods. In contrast to existing methods, the proposed method also uses additional data on the medium. They include the thermodynamic state of the medium, stratigraphic confinement of deposits, rock lithology, distribution of data clusters, physical properties of the medium etc. According to the method, one-dimensional models are first constructed on various properties of the medium based on data of complex of well logging. Then, the neural network is studied to predict the shear wave velocity on a set of models. Subsequently, two-three-dimensional models of the medium are constructed according to the results of terresterial geophysical studies. Two-three dimensional velocity model of a shear wave is predicted by using a complex of these models studied by a neural network. Results. Velocity model of shear wave is predicted for complexly structured geological medium of the South Caspian Basin using the method. Scientific novelty. It is possible to increase the accuracy and resolution of prediction the medium model by increasing the number of types of data used. Practical value. Improving the efficiency of seismic exploration in determining oil and gas saturation, elastic geodynamic state and other physical properties of the geological medium.
dc.format.extent71-80
dc.format.pages10
dc.identifier.citationAghayev Kh. B. Construction of a velocity model of shear wave for complexly structured geological medium using neural network (by example of data of the South Saspian basin) / Kh. B. Aghayev, R. H. Kuliyev, Sh. Z. Yaqubova // Geodynamics. — Lviv : Lviv Politechnic Publishing House, 2020. — No 1 (28). — P. 71–80.
dc.identifier.citationenAghayev Kh. B. Construction of a velocity model of shear wave for complexly structured geological medium using neural network (by example of data of the South Saspian basin) / Kh. B. Aghayev, R. H. Kuliyev, Sh. Z. Yaqubova // Geodynamics. — Lviv : Lviv Politechnic Publishing House, 2020. — No 1 (28). — P. 71–80.
dc.identifier.doidoi.org/10.23939/jgd2020.01.071
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/59293
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofГеодинаміка, 1 (28), 2020
dc.relation.ispartofGeodynamics, 1 (28), 2020
dc.relation.referencesAghayev, Kh. B. (2012). Prediction of the shear waves
dc.relation.referencesvelocities model accoding to the data of Geophysical
dc.relation.referencesresearches of wells and seismic-survey using neural
dc.relation.referencesnetworks. Geoinformatika, 4(44), 46–52. (in Russian).
dc.relation.referencesAghayev, Kh. B. (2013). The use of cluster analysis for
dc.relation.referencesdisassembling a geological section accoding to well
dc.relation.referenceslogging data. Karotajnik. Tver. 5, 3–11. (in Russian).
dc.relation.referencesBouska, J., & Johnston, R. (2005). The first 3D/4-C
dc.relation.referencesocean bottom seismic surveys in the Caspian
dc.relation.referencesSea: Acquisition design and processing
dc.relation.referencesstrategy. The Leading Edge, 24(9), 910–921.
dc.relation.referenceshttp://dx.doi.org/10.1190/1.2056392.
dc.relation.referencesBurger, H. R., Sheehan, A. F., and Jones, C. H. (2006).
dc.relation.referencesIntroduction to Applied Geophysics: Exploring the
dc.relation.referencesshallow subsurface W.W. Norton & Co., New York, 554 pp.
dc.relation.referencesCastagna, J., Backus, M. (1993). Offset-dependent
dc.relation.referencesreflectivity: theory and practice of AVO analysis.
dc.relation.referencesInvestigations in Geophysics Series, Soc. Expl.
dc.relation.referencesGeophysics, January, vol. 8, 348 p.
dc.relation.referencesCastagna, J. P., Batzle, M. L. and Eastwood, R. L.
dc.relation.references(1985). Relationships between compressionalwave and shear-wave velocity in clastic silicate
dc.relation.referencesrocks. Geophysics, Vol. 50(4), 571–581.
dc.relation.referenceshttp://dx.doi.org/10.1190/1.1441933.
dc.relation.referencesChashkov, A. V. and Valery, V. M. (2011). Use of the
dc.relation.referencesCluster Analysis and Artificial Neural Network
dc.relation.referencesTechnology for Log Data Interpretation. Journal of
dc.relation.referencesSiberian Federal University. Engineering &
dc.relation.referencesTechnologies, 4(4), 453–462. http://elib.sfukras.ru/handle/2311/2485.
dc.relation.referencesEberhart, P. D., Han, D. H., Zoback, M. D. (1989).
dc.relation.referencesEmpirical relationships among seismic velocity,
dc.relation.referenceseffective pressure, porosity, and clay content in
dc.relation.referencessandstone, Geophysics, 54, 1, pp. 82–89.
dc.relation.referenceshttps://doi.org/10.1190/1.1442580.
dc.relation.referencesEllis, D. V., & Singer, J. M. (2007). Well logging for
dc.relation.referencesearth scientists (Vol. 692). Dordrecht: Springer.
dc.relation.referenceshttps://doi.org/10.1007/978-1-4020-4602-5.
dc.relation.referencesEskandari, H., Rezaee, R. and Mohammadnia, M.
dc.relation.references(2004). Application of Multiple Regression and
dc.relation.referencesArtificial Neural Network Techniques to Predict
dc.relation.referencesShear Wave Velocity from Well Log Data for a
dc.relation.referencesCarbonate Reservoir, South-West Iran. Cseg Recorder, 42–48.
dc.relation.referencesGarotta, R. J. (2000). Transverse waves: from registration
dc.relation.referencesto interpretation. Short Course of Lectures for
dc.relation.referencesHigher Education Institutions, Society of
dc.relation.referencesGeophysicists-Scouts (SEG), 226 p.
dc.relation.referencesGholami, R., Moradzadeh, A., Rasouli, V., and Hanachi, J. (2014). Shear Wave Velocity Prediction
dc.relation.referencesUsing Seismic Attributes and Well Log Data, Acta
dc.relation.referencesGeophys. Vol. 62, 818–848. doi: 10.2478/s11600-013-0200-7.
dc.relation.referencesGuliev, H. H., and Aghaev, Kh. B. (2010). The seismic
dc.relation.referencessections modeling accounting the stressed state of the
dc.relation.referencesmedium. Geodynamics, No. 1 (9), p. 81–86. (in
dc.relation.referencesRussian).
dc.relation.referencesGuliyev, H., Aghayev, Kh., Mehraliyev, F. and
dc.relation.referencesAhmadova, E. (2019). Determination of the physical
dc.relation.referencesproperties of complexly constructed media using nearsurface crosswell method. Visnyk of Taras Shevchenko
dc.relation.referencesNational University of Kyiv: Geology. 3(86). 13–20.
dc.relation.referenceshttp://doi.org/10.17721/1728-2713.86.02
dc.relation.referencesGuliyev, H. H., Aghayev, Kh. B. and Shirinov, N. M.
dc.relation.references(2010). The Research of the Influence of the Pressure
dc.relation.referencesto the Values of Elastic Parameters of Geological
dc.relation.referencesMedium on the Basis of Seismic and Well Data.
dc.relation.referencesVisnyk of Taras Shevchenko National University of
dc.relation.referencesKyiv: Geology, 50. 10–16.
dc.relation.referencesGuliyev, H. H., Aghayev, Kh. B. and Sultanova G. A.
dc.relation.references(2019). Determination of stress in the geological
dc.relation.referencesmedium on the basis of well data using acoustoelastic
dc.relation.referencescorrelations. International Journal of Geophysics 41(6):173–182.
dc.relation.referencesHabib Akhundi, Mohammad Ghafoori, and GholamReza Lashkaripour. (2014). Prediction of Shear
dc.relation.referencesWave Velocity Using Artificial Neural Network
dc.relation.referencesTechnique, Multiple Regression and Petrophysical
dc.relation.referencesData: A Case Study in Asmari Reservoir (SW Iran)
dc.relation.referencesOpen Journal of Geology, 4, 303–313.
dc.relation.referencesKrief, M., Garat, J., Stellingwerff, J., and Ventre J.
dc.relation.references(1990). A petrophysical interpretation using the
dc.relation.referencesvelocities of P and S waves (full-waveform sonic),
dc.relation.referencesThe Log Analyst 31, 8, 355–369.
dc.relation.referencesMeltem, Akan. (2016). Processing and Interpretation of
dc.relation.referencesThree-Component Vertical Seismic Profile Data,
dc.relation.referencesRoss Sea, Antarctica. Graduate Theses & NonTheses, 64, 90 p. https://digitalcommons.mtech.edu › viewcontent.
dc.relation.referencesPoulton, M.M. (2002). Neural networks as an intelligence amplification tool: A review of applications.
dc.relation.referencesGeophysics 67(3), 979–993. doi: 10.1190/1.1484539.
dc.relation.referencesPuzyrev N. N., Trigubov A. V., Brodov L. Yu.,
dc.relation.referencesVedernikov G. V., Lebedev K. A. (1985). Seismic
dc.relation.referencesexploration by the method of transverse and
dc.relation.referencesconverted waves / M.: Nedra, 277 p. (in Russian).
dc.relation.referencesRobert, R. Stewart, James E. Gaiser, R. James Brown,
dc.relation.referencesand Don C. Lawton. (2002). Converted-wave seismic
dc.relation.referencesexploration: applications. Geophysics 68(1): 40–57.
dc.relation.referencesdoi: 10.1190/1.1543193.
dc.relation.referencesSaeed Parvizi, Riyaz Rharrat, Mohammad R. ASEF,
dc.relation.referencesBijan Janangiry and Abdolnabi Hashemi. (2015).
dc.relation.referencesPrediction of the Shear Wave Velocity from
dc.relation.referencesCompressional Wave Velocity for Gachsaran
dc.relation.referencesFormation. Acta Geophysicavol. 63(5), 1231–1243. doi: 10.1515/acgeo-2015-0048.
dc.relation.referencesSchön, J. H. (2015). Physical properties of rocks:
dc.relation.referencesFundamentals and principles of petrophysics.
dc.relation.referencesElsevier. https://trove.nla.gov.au/work/9281433
dc.relation.referencesShahoo Maleki, Ali Moradzadeh, Reza Ghavami Riabi,
dc.relation.referencesRaoof Gholami, and Farhad Sadeghzadeh. (2014).
dc.relation.referencesPrediction of shear wave velocity using empirical
dc.relation.referencescorrelations and artificial intelligence methods.
dc.relation.referencesNRIAG Journal of Astronomy and Geophysics 3, 70–81. doi:10.316/j.nrjag.2014.05.001.
dc.relation.referencesVeeken, P. C. H., and Silva, Da M. (2004). Seismic
dc.relation.referencesinversion methods and some of their constraints: First
dc.relation.referencesBreak, 22, 47–70. doi: 10.3997/1365-2397.2004011.
dc.relation.referencesVolarovich, M. P., Bayuk, E. I., Levykin A. I., and
dc.relation.referencesTomashevskaya, I. S. (1974). Physico-mechanical
dc.relation.referencesproperties of rocks and minerals at high pressures.
dc.relation.referencesPublishing House “Science”, 1–123 p. (in Russian).
dc.relation.referencesVoskresensky, Yu. N. (2001). The study of changes in
dc.relation.referencesthe amplitudes of seismic reflections for the search
dc.relation.referencesand exploration of hydrocarbon deposits. Russian
dc.relation.referencesState University of Oil and Gas named after THEM.
dc.relation.referencesGubkin. Moscow: Ministry of Education of the
dc.relation.referencesRussian Federation. 69 p. (in Russian).
dc.relation.referencesYilmaz, Oz. (2001). Seismic data analysis: processing,
dc.relation.referencesinversion and interpretation of seismic data. Society
dc.relation.referencesof Exploration Geopysicists. Investigations in
dc.relation.referencesgeophysics, 2, Tulsa, SEG, 2027
dc.relation.referencesenAghayev, Kh. B. (2012). Prediction of the shear waves
dc.relation.referencesenvelocities model accoding to the data of Geophysical
dc.relation.referencesenresearches of wells and seismic-survey using neural
dc.relation.referencesennetworks. Geoinformatika, 4(44), 46–52. (in Russian).
dc.relation.referencesenAghayev, Kh. B. (2013). The use of cluster analysis for
dc.relation.referencesendisassembling a geological section accoding to well
dc.relation.referencesenlogging data. Karotajnik. Tver. 5, 3–11. (in Russian).
dc.relation.referencesenBouska, J., & Johnston, R. (2005). The first 3D/4-C
dc.relation.referencesenocean bottom seismic surveys in the Caspian
dc.relation.referencesenSea: Acquisition design and processing
dc.relation.referencesenstrategy. The Leading Edge, 24(9), 910–921.
dc.relation.referencesenhttp://dx.doi.org/10.1190/1.2056392.
dc.relation.referencesenBurger, H. R., Sheehan, A. F., and Jones, C. H. (2006).
dc.relation.referencesenIntroduction to Applied Geophysics: Exploring the
dc.relation.referencesenshallow subsurface W.W. Norton & Co., New York, 554 pp.
dc.relation.referencesenCastagna, J., Backus, M. (1993). Offset-dependent
dc.relation.referencesenreflectivity: theory and practice of AVO analysis.
dc.relation.referencesenInvestigations in Geophysics Series, Soc. Expl.
dc.relation.referencesenGeophysics, January, vol. 8, 348 p.
dc.relation.referencesenCastagna, J. P., Batzle, M. L. and Eastwood, R. L.
dc.relation.referencesen(1985). Relationships between compressionalwave and shear-wave velocity in clastic silicate
dc.relation.referencesenrocks. Geophysics, Vol. 50(4), 571–581.
dc.relation.referencesenhttp://dx.doi.org/10.1190/1.1441933.
dc.relation.referencesenChashkov, A. V. and Valery, V. M. (2011). Use of the
dc.relation.referencesenCluster Analysis and Artificial Neural Network
dc.relation.referencesenTechnology for Log Data Interpretation. Journal of
dc.relation.referencesenSiberian Federal University. Engineering &
dc.relation.referencesenTechnologies, 4(4), 453–462. http://elib.sfukras.ru/handle/2311/2485.
dc.relation.referencesenEberhart, P. D., Han, D. H., Zoback, M. D. (1989).
dc.relation.referencesenEmpirical relationships among seismic velocity,
dc.relation.referenceseneffective pressure, porosity, and clay content in
dc.relation.referencesensandstone, Geophysics, 54, 1, pp. 82–89.
dc.relation.referencesenhttps://doi.org/10.1190/1.1442580.
dc.relation.referencesenEllis, D. V., & Singer, J. M. (2007). Well logging for
dc.relation.referencesenearth scientists (Vol. 692). Dordrecht: Springer.
dc.relation.referencesenhttps://doi.org/10.1007/978-1-4020-4602-5.
dc.relation.referencesenEskandari, H., Rezaee, R. and Mohammadnia, M.
dc.relation.referencesen(2004). Application of Multiple Regression and
dc.relation.referencesenArtificial Neural Network Techniques to Predict
dc.relation.referencesenShear Wave Velocity from Well Log Data for a
dc.relation.referencesenCarbonate Reservoir, South-West Iran. Cseg Recorder, 42–48.
dc.relation.referencesenGarotta, R. J. (2000). Transverse waves: from registration
dc.relation.referencesento interpretation. Short Course of Lectures for
dc.relation.referencesenHigher Education Institutions, Society of
dc.relation.referencesenGeophysicists-Scouts (SEG), 226 p.
dc.relation.referencesenGholami, R., Moradzadeh, A., Rasouli, V., and Hanachi, J. (2014). Shear Wave Velocity Prediction
dc.relation.referencesenUsing Seismic Attributes and Well Log Data, Acta
dc.relation.referencesenGeophys. Vol. 62, 818–848. doi: 10.2478/s11600-013-0200-7.
dc.relation.referencesenGuliev, H. H., and Aghaev, Kh. B. (2010). The seismic
dc.relation.referencesensections modeling accounting the stressed state of the
dc.relation.referencesenmedium. Geodynamics, No. 1 (9), p. 81–86. (in
dc.relation.referencesenRussian).
dc.relation.referencesenGuliyev, H., Aghayev, Kh., Mehraliyev, F. and
dc.relation.referencesenAhmadova, E. (2019). Determination of the physical
dc.relation.referencesenproperties of complexly constructed media using nearsurface crosswell method. Visnyk of Taras Shevchenko
dc.relation.referencesenNational University of Kyiv: Geology. 3(86). 13–20.
dc.relation.referencesenhttp://doi.org/10.17721/1728-2713.86.02
dc.relation.referencesenGuliyev, H. H., Aghayev, Kh. B. and Shirinov, N. M.
dc.relation.referencesen(2010). The Research of the Influence of the Pressure
dc.relation.referencesento the Values of Elastic Parameters of Geological
dc.relation.referencesenMedium on the Basis of Seismic and Well Data.
dc.relation.referencesenVisnyk of Taras Shevchenko National University of
dc.relation.referencesenKyiv: Geology, 50. 10–16.
dc.relation.referencesenGuliyev, H. H., Aghayev, Kh. B. and Sultanova G. A.
dc.relation.referencesen(2019). Determination of stress in the geological
dc.relation.referencesenmedium on the basis of well data using acoustoelastic
dc.relation.referencesencorrelations. International Journal of Geophysics 41(6):173–182.
dc.relation.referencesenHabib Akhundi, Mohammad Ghafoori, and GholamReza Lashkaripour. (2014). Prediction of Shear
dc.relation.referencesenWave Velocity Using Artificial Neural Network
dc.relation.referencesenTechnique, Multiple Regression and Petrophysical
dc.relation.referencesenData: A Case Study in Asmari Reservoir (SW Iran)
dc.relation.referencesenOpen Journal of Geology, 4, 303–313.
dc.relation.referencesenKrief, M., Garat, J., Stellingwerff, J., and Ventre J.
dc.relation.referencesen(1990). A petrophysical interpretation using the
dc.relation.referencesenvelocities of P and S waves (full-waveform sonic),
dc.relation.referencesenThe Log Analyst 31, 8, 355–369.
dc.relation.referencesenMeltem, Akan. (2016). Processing and Interpretation of
dc.relation.referencesenThree-Component Vertical Seismic Profile Data,
dc.relation.referencesenRoss Sea, Antarctica. Graduate Theses & NonTheses, 64, 90 p. https://digitalcommons.mtech.edu › viewcontent.
dc.relation.referencesenPoulton, M.M. (2002). Neural networks as an intelligence amplification tool: A review of applications.
dc.relation.referencesenGeophysics 67(3), 979–993. doi: 10.1190/1.1484539.
dc.relation.referencesenPuzyrev N. N., Trigubov A. V., Brodov L. Yu.,
dc.relation.referencesenVedernikov G. V., Lebedev K. A. (1985). Seismic
dc.relation.referencesenexploration by the method of transverse and
dc.relation.referencesenconverted waves, M., Nedra, 277 p. (in Russian).
dc.relation.referencesenRobert, R. Stewart, James E. Gaiser, R. James Brown,
dc.relation.referencesenand Don C. Lawton. (2002). Converted-wave seismic
dc.relation.referencesenexploration: applications. Geophysics 68(1): 40–57.
dc.relation.referencesendoi: 10.1190/1.1543193.
dc.relation.referencesenSaeed Parvizi, Riyaz Rharrat, Mohammad R. ASEF,
dc.relation.referencesenBijan Janangiry and Abdolnabi Hashemi. (2015).
dc.relation.referencesenPrediction of the Shear Wave Velocity from
dc.relation.referencesenCompressional Wave Velocity for Gachsaran
dc.relation.referencesenFormation. Acta Geophysicavol. 63(5), 1231–1243. doi: 10.1515/acgeo-2015-0048.
dc.relation.referencesenSchön, J. H. (2015). Physical properties of rocks:
dc.relation.referencesenFundamentals and principles of petrophysics.
dc.relation.referencesenElsevier. https://trove.nla.gov.au/work/9281433
dc.relation.referencesenShahoo Maleki, Ali Moradzadeh, Reza Ghavami Riabi,
dc.relation.referencesenRaoof Gholami, and Farhad Sadeghzadeh. (2014).
dc.relation.referencesenPrediction of shear wave velocity using empirical
dc.relation.referencesencorrelations and artificial intelligence methods.
dc.relation.referencesenNRIAG Journal of Astronomy and Geophysics 3, 70–81. doi:10.316/j.nrjag.2014.05.001.
dc.relation.referencesenVeeken, P. C. H., and Silva, Da M. (2004). Seismic
dc.relation.referenceseninversion methods and some of their constraints: First
dc.relation.referencesenBreak, 22, 47–70. doi: 10.3997/1365-2397.2004011.
dc.relation.referencesenVolarovich, M. P., Bayuk, E. I., Levykin A. I., and
dc.relation.referencesenTomashevskaya, I. S. (1974). Physico-mechanical
dc.relation.referencesenproperties of rocks and minerals at high pressures.
dc.relation.referencesenPublishing House "Science", 1–123 p. (in Russian).
dc.relation.referencesenVoskresensky, Yu. N. (2001). The study of changes in
dc.relation.referencesenthe amplitudes of seismic reflections for the search
dc.relation.referencesenand exploration of hydrocarbon deposits. Russian
dc.relation.referencesenState University of Oil and Gas named after THEM.
dc.relation.referencesenGubkin. Moscow: Ministry of Education of the
dc.relation.referencesenRussian Federation. 69 p. (in Russian).
dc.relation.referencesenYilmaz, Oz. (2001). Seismic data analysis: processing,
dc.relation.referenceseninversion and interpretation of seismic data. Society
dc.relation.referencesenof Exploration Geopysicists. Investigations in
dc.relation.referencesengeophysics, 2, Tulsa, SEG, 2027
dc.relation.urihttp://dx.doi.org/10.1190/1.2056392
dc.relation.urihttp://dx.doi.org/10.1190/1.1441933
dc.relation.urihttp://elib.sfukras.ru/handle/2311/2485
dc.relation.urihttps://doi.org/10.1190/1.1442580
dc.relation.urihttps://doi.org/10.1007/978-1-4020-4602-5
dc.relation.urihttp://doi.org/10.17721/1728-2713.86.02
dc.relation.urihttps://digitalcommons.mtech.edu
dc.relation.urihttps://trove.nla.gov.au/work/9281433
dc.rights.holder© Інститут геології і геохімії горючих копалин Національної академії наук України, 2020
dc.rights.holder© Інститут геофізики ім. С. І. Субботіна Національної академії наук України, 2020
dc.rights.holder© Національний університет “Львівська політехніка”, 2020
dc.rights.holder© Aghayev Kh. B., Kuliyev R. H., Yaqubova Sh. Z.
dc.subjectсейсмічна розвідка
dc.subjectхвиля тиску і зсуву
dc.subjectсейсмічна швидкість
dc.subjectсередня модель
dc.subjectпередбачення
dc.subjectнейронна мережа
dc.subjectseismic exploration
dc.subjectpressure and shear wave
dc.subjectseismic velocity
dc.subjectmedium model
dc.subjectprediction
dc.subjectneural network
dc.subject.udc550.8.056
dc.titleConstruction of a velocity model of shear wave for complexly structured geological medium using neural network (by example of data of the South Saspian basin)
dc.title.alternativeПобудова швидкісної моделі поперечної хвилі для складноструктурного геологічного середовища з використанням нейронної мережі (на прикладі даних Південно-Каспійського басейну)
dc.typeArticle

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