Araştırma Makalesi
BibTex RIS Kaynak Göster

Inversion of Gravity Anomalies by Cuckoo Search Algorithm

Yıl 2022, Cilt: 24 Sayı: 72, 799 - 813, 19.09.2022

Seçil Turan Karaoğlan Gökhan Göktürkler

https://doi.org/10.21205/deufmd.2022247210

Öz

Metaheuristic methods have been used frequently in applications of geophysical inversion studies. These search algorithms, which have comprehensive searching characteristics for parameter space without needing a good initial model unlike derivative-based inversion methods, give advantage for the parameter estimations in geophysics. In the presented study, the cuckoo search algorithm is used for the inversion of the gravity anomalies. The cuckoo search algorithm was decided for the parameter estimation studies because of the low number of the user-defined parameter of the algorithm and yielding better results compared to the many nature-inspired metaheuristic methods. Model parameters of the gravity anomalies are amplitude coefficient, depth of causative source, exact origin of causative source and shape factors. Control parameters of the algorithm (population number and probability of recognition of the egg) are evaluated in detail by parameter tuning study with noise-free synthetic data. Then, the results of the control parameters are tested with noisy synthetic data. On the other hand, a field data from the chromite deposit in Cuba and from the base metal sulphide deposit in Canada are evaluated and model parameters of the field data sets are estimated. The model parameters are statistically tested for the determination of the accuracy of model parameters of the synthetic and field data sets by Metropolis-Hasting algorithm. Based on its nature, the algorithm, which does not need a good initial model and partial derivative calculation according to the model parameters, provides improved usage in parameter estimation studies with two user-defined parameters. As a result of the uncertainty analysis, it was determined that the algorithm is applicable for inversion of gravity data.

Anahtar Kelimeler

Metaheuristics geophysics gravity inversion cuckoo search algorithm

Kaynakça

  • [1] Göktürkler, G., Balkaya, Ç., Ekinci Y.L., Turan, S. 2016. Uygulamalı jeofizikte metasezgiseller. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 22(6): 563-580. DOI: 10.5505/pajes.2015.81904.
  • [2] Ekinci, Y.L., Balkaya, Ç., Göktürkler, G., Turan S. 2016. Model parameter estimations from residual gravity anomalies due to simple-shaped sources using differential evolution algorithm. Journal of Applied Geophysics. Cilt. 129 s. 133-147. DOI: 10.1016/j.jappgeo.2016.03.040.
  • [3] Balkaya, Ç., Ekinci Y.L., Göktürkler, G., Turan, Seçil. 2017. 3D non-linear inversion of magnetic anomalies caused by prismatic bodies using differential evolution algorithm, Journal of Applied Geophysics, Cilt. 136, s. 372–386. DOI: 10.1016/j.jappgeo.2016.10.040.
  • [4] Holland, J.H. 1975. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, Ann Arbor, MI: University of Michigan Press. 211s.
  • [5] Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P. 1983. Optimization by simulated annealing, Science, Cilt. 220, s. 671-680. DOI: 10.1126/science.220.4598.671.
  • [6] Kennedy, J., Eberhart R. 1995. Particle swarm optimization. In: IEEE International Conference on Neural Networks, Piscataway, NJ, USA. s. 1942–1948.
  • [7] Storn, R., Price, K. 1995. Differential Evolution-A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces, Technical Report, International Computer Science Institute Berkeley, USA. TR 95-012.
  • [8] Storn, R., Price, K. 1997. Differential Evolution – A simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization Cilt. 11(4) s. 341-359. DOI: 10.1023/A:1008202821328.
  • [9] Storn, R. 1996. Differential Evolution Design of an IIR-Filter. In: IEEE International conference on Evolutionary Computation (ICEC’96), Nagoya, Japan. s. 268-273.
  • [10] Abdelazeem, M., Gobashy, M., 2006. Self-potential inversion using genetic algorithm. JKAU Earth Sci. 17 (1), 83–101. DOI: 10.4197/Ear.17-1.5.
  • [11] Montesinos, F.G., Blanco-Montenegro, I., Arnoso, J. 2016. Three-dimensional inverse modelling of magnetic anomaly sources based on a genetic algorithm. Phys. Earth Planet. Inter. 253, 74–87. DOI: 10.1016/j.pepi.2016.02.004.
  • [12] Kaftan, I. 2017. Interpretation of magnetic anomalies using a genetic algorithm. Acta Geophys. 65 (4), 627–634. DOI: 10.1007/s11600-017-0060-7.
  • [13] Essa, K.S., Mehanee, S.A., & Elhussein, M. 2021. Gravity data interpretation by a two-sided fault-like geologic structure using the global particle swarm technique. Physics of the Earth and Planetary Interiors, Cilt. 311, s. 106631. DOI: 10.1016/j.pepi.2020.106631
  • [14] Pallero, J.L.G., Fernandez-Martinez, J.L., Fernandez-Muniz, Z., Bonvalot, S., Gabalda, G., Nalpas, T. 2021. GRAVPSO2D:A matlab package for 2D gravity inversion in sedimentary basins using the Particle Swarm Optimization algorithm, Computers and Geosciences, Cilt. 146, s. 104653. DOI: 10.1016/j.cageo.2020.104653
  • [15] Srivastava, S., Agarwal, B. N. P. 2010. Inversion of the amplitude of the two-dimensional analytic signal of magnetic anomaly by the particle swarm optimization technique, Geophysical Journal International, Cilt. 182, s. 652–662.
  • [16] Pekşen, E., Yas, T., Kıyak, A. 2014. 1-D DC resistivity modeling and interpretation in anisotropic media using particle swarm optimization, Pure Appl. Geophys. Cilt. 171, s. 2371–2389. DOI: 10.1007/s00024-014-0802-2.
  • [17] Ekinci, Y.L., Balkaya, Ç., Göktürkler, G. 2020. Global Optimization of Near-Surface Potential Field Anomalies Through Metaheuristics. In: Biswas, A., Sharma, S. (Eds.), Advances in Modeling and Interpretation in near Surface Geophysics. Springer Geophysics. Springer, Cham, s. 155–188. DOI: 10.1007/978-3-030-28909-6_7.
  • [18] Tarhan, Bal, O., B.Tekkeli, A., Karcıoğlu, G. 2021. Application of particle swarm optimization to 3D Euler deconvolution and 3D modeling of gravity data-a case study from Biga and can towns, NW Turkey, Arabian Journal of Geoscencies, Cilt. 14(8). DOI: 10.1007/s12517-021-07029-y.
  • [19] Ekinci, Y.L., Balkaya, Ç., Göktürkler, G. 2019. Parameter estimations from gravity and magnetic anomalies due to deep-seated faults: differential evolution versus particle swarm optimization, Turkish Journal of Earth Sciences, Cilt. 28, s. 860–881. DOI: 10.3906/yer-1905-3.
  • [20] Ekinci, Y.L., Balkaya, Ç., Göktürkler, G., Özyalın, Ş. 2021. Gravity data inversion for the basement relief delineation through global optimization: A case study from the Aegean Graben System, western Anatolia, Turkey, Geophysical Journal International, Cilt. 224(2), s. 923–944. DOI: 10.1093/gji/ggaa492
  • [21] Roy, A., Dubey, P. C., Prasad, M. 2021. Gravity inversion for heterogeneous sedimentary basin with b-spline polynomial approximation using differential evolution algorithm, Geophysics, Cilt. 86(3), s. F35–F47. DOI: 10.1190/geo2019-0779.1
  • [22] Li, X., Yin, M. 2012. Application of differential evolution algorithm on self-potential data. PLoS One 7 (12), 1–11. DOI: 10.1371/journal.pone.0051199.
  • [23] Balkaya, Ç. 2013. An implementation of differential evolution algorithm for inversion of geoelectrical data, Journal of Applied Geophysics, Cilt. 98, s. 160–175. DOI: 10.1016/j.jappgeo.2013.08.019.
  • [24] Ekinci, Y.L., Özyalın, Ş., Sındırgı, P., Balkaya, Ç., Göktürkler, G. 2017. Amplitude inversion of 2D analytic signal of magnetic anomalies through differential evolution algorithm, Journal of Geophysics and Engineering, 14(6): 1492-1508. DOI: 10.1088/1742-2140/aa7ffc.
  • [25] Göktürkler, G., Balkaya, Ç., 2012. Inversion of self-potential anomalies caused by simple geometry bodies using global optimization algorithms. Journal of Geophysics and Engineering, Cilt. 9 (5), s. 498–507. DOI: 10.1088/1742-2132/9/5/498.
  • [26] Sharma, S.P., Biswas, A. 2013. Interpretation of self-potential anomaly over a 2D inclined structure using very fast simulated-annealing global optimization - an insight about ambiguity, Geophysics, Cilt. 78 (3), WB3–WB15. DOI: 10.1190/geo2012-0233.1.
  • [27] Biswas, A., Sharma, S.P. 2014. Optimization of self-potential interpretation of 2-D inclined sheet-type structures based on very fast simulated annealing and analysis of ambiguity, Journal of Applied Geophysics, Cilt. 105, s. 235–247. DOI: 10.1016/j.jappgeo.2014.03.023.
  • [28] Chauhan, M.S., Fedi, M., Sen, M.S. 2018. Gravity inversion by the Multi-Homogeneity depth estimation method for investigating salt domes and complex sources, Geophysical Prospecting, Cilt. 66, s. 175–191. DOI: 10.1111/1365-2478.12603.
  • [29] Trivedi, S., Kumar, P., Parija, M.P., Biswas, A. 2020. Global optimization of model parameters from the 2-D analytic signal of gravity and magnetic anomalies over geobodies with idealized structure. In: Biswas, A., Sharma, S. (Eds.), Advances in Modeling and Interpretation in near Surface Geophysics. Springer Geophysics. Springer, Cham, s. 189–221. DOI: 10.1007/978-3-030-28909-6_8.
  • [30] Alkan, H. and Balkaya, Ç. 2018. Parameter estimation by Differential Search Algorithm from horizontal loop electromagnetic (HLEM) data. Journal of Applied Geophysics, Cilt. 149, s. 77-94. DOI: 10.1016/j.jappgeo.2017.12.016.
  • [31] Balkaya, C., Kaftan, İ. 2021. Inverse modelling via differential search algorithm for interpreting magnetic anomalies caused by 2D dyke-shaped bodies, Journal of Earth System Sciences, Cilt. 130, s. 135. DOI: 10.1007/s12040-021-01614-1.
  • [32] Agarwal, A., Chandra, A., Shalivahan, S., Singh, R.K. 2018. Grey wolf optimizer: a new strategy to invert geophysical data sets, Geophysical Prospecting, Cilt. 66, s. 1215–1226. DOI: 10.1111/1365-2478.12640.
  • [33] Ekinci, Y.L., Balkaya, Ç., Göktürkler, G. 2021. Backtracking Search Optimization: A Novel Global Optimization Algorithm for the Inversion of Gravity Anomalies, Pure and Applied Geophysics, Cilt. 178, s. 4507–4527. DOI: 10.1007/s00024-021-02855-3.
  • [34] Turan-Karaoğlan, S., Göktürkler, G. 2021. Cuckoo Search Algorithm for model parameter estimation from self-potential data, Journal of Applied Geophysics, Cilt. 194, s. 104461. DOI: 10.1016/j.jappgeo.2021.104461.
  • [35] Yang, X.-S., Deb, S. 2009. Cuckoo search via Lévy flights. In: IEEE World Congress on Nature and Biologically Inspired Computing (NaBIC); Coimbatore, India, s. 210-214.
  • [36] Yang, X.-S., Deb, S. 2014. Cuckoo search: recent advances and applications, Neural Computing and Applications, Cilt. 24, s. 169–174. DOI: 10.1007/s00521-013-1367-1.
  • [37] Gandomi, A.H., Yang, X.-S., Alavi, A.H. 2013. Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems, Engineering with Computers, Cilt. 29 (1), s. 17–35. DOI: 10.1007/s00366-011-0241-y.
  • [38] Bodaghi, A., Ansari, H.R., Gholami, M. 2015. Optimized support vector regression for drilling rate of penetration estimation, Open Geosciences, Cilt. 7(1), s. 870-879. DOI: 10.1515/geo-2015-0054.
  • [39] Ouaarab, A., Ahiod, B., Yang, X.S., 2014. Discrete cuckoo search algorithm for the travelling salesman problem, Neural Computing and Applications, Cilt. 24, s. 1659–1669. DOI: 10.1007/s00521-013-1402-2
  • [40] Yang, X.-S. 2014. Nature-Inspired Optimization Algorithms, 1st ed. Massachusetts, USA: Elsevier.
  • [41] Abdelrahman, E.M., Bayoumi, A.I., Abdelhady, Y.E., Gobashy, M.M., El-Araby, H.M. 1989. Gravity interpretation using correlation factors between successive least-squares residual anomalies, Geophysics, Cilt. 54(12), s. 1614-1621. DOI: 10.1190/1.1442629
  • [42] Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E. 1953. Equations of state calculations by fast computing machines, The Journal of Chemical Physics, Cilt. 21, s. 1087-1091. DOI: 10.1063/1.1699114.
  • [43] Hasting, W. 1970. Monte Carlo sampling methods using Markov chains and their applications, Biometrika, Cilt. 57(1), s. 97-109. DOI: 10.2307/2334940
  • [44] Galassi, M., Davies, J., Theiler, J., Gough, B., Jungman, G., Alken, P., Booth M., Rossi, F. 2009. GNU Scientific Library Reference Manual 3rd edition (Bristol: Network Theory Ltd) s. 497.
  • [45] Davis, W.E., Jackson, W.H., Richter, D.H. 1957. Gravity prospecting for chromite deposits in Camaguey province, Cuba, Geophysics, Cilt. 22(4), s. 848–869. DOI: 10.1190/1.1438427.
  • [46] Grant, F.S., West, G.F. 1965. Interpretation Theory in Applied Geophysics, McGraw-Hill Book Co. 583s.

Gravite Anomalilerinin Guguk Kuşu Arama Algoritması ile Ters Çözümü

Yıl 2022, Cilt: 24 Sayı: 72, 799 - 813, 19.09.2022

Seçil Turan Karaoğlan Gökhan Göktürkler

https://doi.org/10.21205/deufmd.2022247210

Öz

Metasezgisel algoritmalar jeofizik ters çözüm çalışmalarında sıklıkla kullanılır duruma gelmiştir. Türev tabanlı en iyileme yöntemlerinin aksine, iyi bir başlangıç modeline ihtiyaç duymayan arama algoritmaları parametre uzayını kapsamlı tarama özelliğine sahip olduklarından jeofizikte model parametre kestirimleri için avantaj sağlamaktadır. Sunulan çalışmada, gravite anomalilerinin ters çözümünde guguk kuşu arama algoritması kullanılmıştır. Algoritmanın kullanıcı tanımlı parametre sayısının az olması ve yapılan literatür taramasında doğadan esinlenilerek oluşturulan birçok metasezgisel yönteme göre daha iyi sonuç vermesi, parametre kestirim çalışmasında guguk kuşu algoritmasının kullanılmasını teşvik etmektedir. Gravite belirtisine ait genlik katsayısı, kaynak derinliği, belirti izdüşümü ve şekil faktörleri kestirimi yapılan model parametreleridir. Algoritmaya ait kontrol parametreleri (popülasyon sayısı ve yumurtanın yuvadan atılma olasılığı) ise gürültüsüz kuramsal veri kümesi kullanılarak parametre belirleme çalışmaları (parameter tuning) ile detaylı bir şekilde irdelenmiştir. Sonrasında kontrol parametre çiftinin doğruluğu gürültü içeren veri kümesi üzerinde test edilmiştir. Ardından, Küba’da bir kromit yatağı üzerinde ölçülen arazi verisi ve Kanada’da yer alan bir sülfit cevheri üzerinde ölçülen arazi verisi değerlendirilerek, anomalilere ait model parametreleri kestirilmiştir. Kuramsal ve arazi veri kümelerine ait model parametrelerinin güvenilirliğinin belirlenmesi için, Metropolis-Hasting algoritması kullanılarak, kestirim parametreleri istatistiksel olarak da test edilmiştir. Doğası gereği iyi bir başlangıç modeline ve model parametrelerine göre kısmi türev hesabına ihtiyaç duymayan algoritma, kullanıcı tanımlı iki parametre içermesi sayesinde parametre kestirim çalışmalarında kolaylık sağlamıştır. Yapılan belirsizlik analizleri sonucunda da algoritmanın gravite verilerinin ters çözümünde uygulanabilir bir algoritma olduğu belirlenmiştir.

Anahtar Kelimeler

Metasezgisel jeofizik gravite ters çözüm guguk kuşu arama algoritması

Kaynakça

  • [1] Göktürkler, G., Balkaya, Ç., Ekinci Y.L., Turan, S. 2016. Uygulamalı jeofizikte metasezgiseller. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 22(6): 563-580. DOI: 10.5505/pajes.2015.81904.
  • [2] Ekinci, Y.L., Balkaya, Ç., Göktürkler, G., Turan S. 2016. Model parameter estimations from residual gravity anomalies due to simple-shaped sources using differential evolution algorithm. Journal of Applied Geophysics. Cilt. 129 s. 133-147. DOI: 10.1016/j.jappgeo.2016.03.040.
  • [3] Balkaya, Ç., Ekinci Y.L., Göktürkler, G., Turan, Seçil. 2017. 3D non-linear inversion of magnetic anomalies caused by prismatic bodies using differential evolution algorithm, Journal of Applied Geophysics, Cilt. 136, s. 372–386. DOI: 10.1016/j.jappgeo.2016.10.040.
  • [4] Holland, J.H. 1975. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, Ann Arbor, MI: University of Michigan Press. 211s.
  • [5] Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P. 1983. Optimization by simulated annealing, Science, Cilt. 220, s. 671-680. DOI: 10.1126/science.220.4598.671.
  • [6] Kennedy, J., Eberhart R. 1995. Particle swarm optimization. In: IEEE International Conference on Neural Networks, Piscataway, NJ, USA. s. 1942–1948.
  • [7] Storn, R., Price, K. 1995. Differential Evolution-A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces, Technical Report, International Computer Science Institute Berkeley, USA. TR 95-012.
  • [8] Storn, R., Price, K. 1997. Differential Evolution – A simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization Cilt. 11(4) s. 341-359. DOI: 10.1023/A:1008202821328.
  • [9] Storn, R. 1996. Differential Evolution Design of an IIR-Filter. In: IEEE International conference on Evolutionary Computation (ICEC’96), Nagoya, Japan. s. 268-273.
  • [10] Abdelazeem, M., Gobashy, M., 2006. Self-potential inversion using genetic algorithm. JKAU Earth Sci. 17 (1), 83–101. DOI: 10.4197/Ear.17-1.5.
  • [11] Montesinos, F.G., Blanco-Montenegro, I., Arnoso, J. 2016. Three-dimensional inverse modelling of magnetic anomaly sources based on a genetic algorithm. Phys. Earth Planet. Inter. 253, 74–87. DOI: 10.1016/j.pepi.2016.02.004.
  • [12] Kaftan, I. 2017. Interpretation of magnetic anomalies using a genetic algorithm. Acta Geophys. 65 (4), 627–634. DOI: 10.1007/s11600-017-0060-7.
  • [13] Essa, K.S., Mehanee, S.A., & Elhussein, M. 2021. Gravity data interpretation by a two-sided fault-like geologic structure using the global particle swarm technique. Physics of the Earth and Planetary Interiors, Cilt. 311, s. 106631. DOI: 10.1016/j.pepi.2020.106631
  • [14] Pallero, J.L.G., Fernandez-Martinez, J.L., Fernandez-Muniz, Z., Bonvalot, S., Gabalda, G., Nalpas, T. 2021. GRAVPSO2D:A matlab package for 2D gravity inversion in sedimentary basins using the Particle Swarm Optimization algorithm, Computers and Geosciences, Cilt. 146, s. 104653. DOI: 10.1016/j.cageo.2020.104653
  • [15] Srivastava, S., Agarwal, B. N. P. 2010. Inversion of the amplitude of the two-dimensional analytic signal of magnetic anomaly by the particle swarm optimization technique, Geophysical Journal International, Cilt. 182, s. 652–662.
  • [16] Pekşen, E., Yas, T., Kıyak, A. 2014. 1-D DC resistivity modeling and interpretation in anisotropic media using particle swarm optimization, Pure Appl. Geophys. Cilt. 171, s. 2371–2389. DOI: 10.1007/s00024-014-0802-2.
  • [17] Ekinci, Y.L., Balkaya, Ç., Göktürkler, G. 2020. Global Optimization of Near-Surface Potential Field Anomalies Through Metaheuristics. In: Biswas, A., Sharma, S. (Eds.), Advances in Modeling and Interpretation in near Surface Geophysics. Springer Geophysics. Springer, Cham, s. 155–188. DOI: 10.1007/978-3-030-28909-6_7.
  • [18] Tarhan, Bal, O., B.Tekkeli, A., Karcıoğlu, G. 2021. Application of particle swarm optimization to 3D Euler deconvolution and 3D modeling of gravity data-a case study from Biga and can towns, NW Turkey, Arabian Journal of Geoscencies, Cilt. 14(8). DOI: 10.1007/s12517-021-07029-y.
  • [19] Ekinci, Y.L., Balkaya, Ç., Göktürkler, G. 2019. Parameter estimations from gravity and magnetic anomalies due to deep-seated faults: differential evolution versus particle swarm optimization, Turkish Journal of Earth Sciences, Cilt. 28, s. 860–881. DOI: 10.3906/yer-1905-3.
  • [20] Ekinci, Y.L., Balkaya, Ç., Göktürkler, G., Özyalın, Ş. 2021. Gravity data inversion for the basement relief delineation through global optimization: A case study from the Aegean Graben System, western Anatolia, Turkey, Geophysical Journal International, Cilt. 224(2), s. 923–944. DOI: 10.1093/gji/ggaa492
  • [21] Roy, A., Dubey, P. C., Prasad, M. 2021. Gravity inversion for heterogeneous sedimentary basin with b-spline polynomial approximation using differential evolution algorithm, Geophysics, Cilt. 86(3), s. F35–F47. DOI: 10.1190/geo2019-0779.1
  • [22] Li, X., Yin, M. 2012. Application of differential evolution algorithm on self-potential data. PLoS One 7 (12), 1–11. DOI: 10.1371/journal.pone.0051199.
  • [23] Balkaya, Ç. 2013. An implementation of differential evolution algorithm for inversion of geoelectrical data, Journal of Applied Geophysics, Cilt. 98, s. 160–175. DOI: 10.1016/j.jappgeo.2013.08.019.
  • [24] Ekinci, Y.L., Özyalın, Ş., Sındırgı, P., Balkaya, Ç., Göktürkler, G. 2017. Amplitude inversion of 2D analytic signal of magnetic anomalies through differential evolution algorithm, Journal of Geophysics and Engineering, 14(6): 1492-1508. DOI: 10.1088/1742-2140/aa7ffc.
  • [25] Göktürkler, G., Balkaya, Ç., 2012. Inversion of self-potential anomalies caused by simple geometry bodies using global optimization algorithms. Journal of Geophysics and Engineering, Cilt. 9 (5), s. 498–507. DOI: 10.1088/1742-2132/9/5/498.
  • [26] Sharma, S.P., Biswas, A. 2013. Interpretation of self-potential anomaly over a 2D inclined structure using very fast simulated-annealing global optimization - an insight about ambiguity, Geophysics, Cilt. 78 (3), WB3–WB15. DOI: 10.1190/geo2012-0233.1.
  • [27] Biswas, A., Sharma, S.P. 2014. Optimization of self-potential interpretation of 2-D inclined sheet-type structures based on very fast simulated annealing and analysis of ambiguity, Journal of Applied Geophysics, Cilt. 105, s. 235–247. DOI: 10.1016/j.jappgeo.2014.03.023.
  • [28] Chauhan, M.S., Fedi, M., Sen, M.S. 2018. Gravity inversion by the Multi-Homogeneity depth estimation method for investigating salt domes and complex sources, Geophysical Prospecting, Cilt. 66, s. 175–191. DOI: 10.1111/1365-2478.12603.
  • [29] Trivedi, S., Kumar, P., Parija, M.P., Biswas, A. 2020. Global optimization of model parameters from the 2-D analytic signal of gravity and magnetic anomalies over geobodies with idealized structure. In: Biswas, A., Sharma, S. (Eds.), Advances in Modeling and Interpretation in near Surface Geophysics. Springer Geophysics. Springer, Cham, s. 189–221. DOI: 10.1007/978-3-030-28909-6_8.
  • [30] Alkan, H. and Balkaya, Ç. 2018. Parameter estimation by Differential Search Algorithm from horizontal loop electromagnetic (HLEM) data. Journal of Applied Geophysics, Cilt. 149, s. 77-94. DOI: 10.1016/j.jappgeo.2017.12.016.
  • [31] Balkaya, C., Kaftan, İ. 2021. Inverse modelling via differential search algorithm for interpreting magnetic anomalies caused by 2D dyke-shaped bodies, Journal of Earth System Sciences, Cilt. 130, s. 135. DOI: 10.1007/s12040-021-01614-1.
  • [32] Agarwal, A., Chandra, A., Shalivahan, S., Singh, R.K. 2018. Grey wolf optimizer: a new strategy to invert geophysical data sets, Geophysical Prospecting, Cilt. 66, s. 1215–1226. DOI: 10.1111/1365-2478.12640.
  • [33] Ekinci, Y.L., Balkaya, Ç., Göktürkler, G. 2021. Backtracking Search Optimization: A Novel Global Optimization Algorithm for the Inversion of Gravity Anomalies, Pure and Applied Geophysics, Cilt. 178, s. 4507–4527. DOI: 10.1007/s00024-021-02855-3.
  • [34] Turan-Karaoğlan, S., Göktürkler, G. 2021. Cuckoo Search Algorithm for model parameter estimation from self-potential data, Journal of Applied Geophysics, Cilt. 194, s. 104461. DOI: 10.1016/j.jappgeo.2021.104461.
  • [35] Yang, X.-S., Deb, S. 2009. Cuckoo search via Lévy flights. In: IEEE World Congress on Nature and Biologically Inspired Computing (NaBIC); Coimbatore, India, s. 210-214.
  • [36] Yang, X.-S., Deb, S. 2014. Cuckoo search: recent advances and applications, Neural Computing and Applications, Cilt. 24, s. 169–174. DOI: 10.1007/s00521-013-1367-1.
  • [37] Gandomi, A.H., Yang, X.-S., Alavi, A.H. 2013. Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems, Engineering with Computers, Cilt. 29 (1), s. 17–35. DOI: 10.1007/s00366-011-0241-y.
  • [38] Bodaghi, A., Ansari, H.R., Gholami, M. 2015. Optimized support vector regression for drilling rate of penetration estimation, Open Geosciences, Cilt. 7(1), s. 870-879. DOI: 10.1515/geo-2015-0054.
  • [39] Ouaarab, A., Ahiod, B., Yang, X.S., 2014. Discrete cuckoo search algorithm for the travelling salesman problem, Neural Computing and Applications, Cilt. 24, s. 1659–1669. DOI: 10.1007/s00521-013-1402-2
  • [40] Yang, X.-S. 2014. Nature-Inspired Optimization Algorithms, 1st ed. Massachusetts, USA: Elsevier.
  • [41] Abdelrahman, E.M., Bayoumi, A.I., Abdelhady, Y.E., Gobashy, M.M., El-Araby, H.M. 1989. Gravity interpretation using correlation factors between successive least-squares residual anomalies, Geophysics, Cilt. 54(12), s. 1614-1621. DOI: 10.1190/1.1442629
  • [42] Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E. 1953. Equations of state calculations by fast computing machines, The Journal of Chemical Physics, Cilt. 21, s. 1087-1091. DOI: 10.1063/1.1699114.
  • [43] Hasting, W. 1970. Monte Carlo sampling methods using Markov chains and their applications, Biometrika, Cilt. 57(1), s. 97-109. DOI: 10.2307/2334940
  • [44] Galassi, M., Davies, J., Theiler, J., Gough, B., Jungman, G., Alken, P., Booth M., Rossi, F. 2009. GNU Scientific Library Reference Manual 3rd edition (Bristol: Network Theory Ltd) s. 497.
  • [45] Davis, W.E., Jackson, W.H., Richter, D.H. 1957. Gravity prospecting for chromite deposits in Camaguey province, Cuba, Geophysics, Cilt. 22(4), s. 848–869. DOI: 10.1190/1.1438427.
  • [46] Grant, F.S., West, G.F. 1965. Interpretation Theory in Applied Geophysics, McGraw-Hill Book Co. 583s.
Toplam 46 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Seçil Turan Karaoğlan 0000-0002-3871-4792

Gökhan Göktürkler 0000-0002-2842-0766

Yayımlanma Tarihi 19 Eylül 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 24 Sayı: 72

Kaynak Göster

APA Turan Karaoğlan, S., & Göktürkler, G. (2022). Gravite Anomalilerinin Guguk Kuşu Arama Algoritması ile Ters Çözümü. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, 24(72), 799-813. https://doi.org/10.21205/deufmd.2022247210
AMA Turan Karaoğlan S, Göktürkler G. Gravite Anomalilerinin Guguk Kuşu Arama Algoritması ile Ters Çözümü. DEUFMD. Eylül 2022;24(72):799-813. doi:10.21205/deufmd.2022247210
Chicago Turan Karaoğlan, Seçil, ve Gökhan Göktürkler. “Gravite Anomalilerinin Guguk Kuşu Arama Algoritması Ile Ters Çözümü”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi 24, sy. 72 (Eylül 2022): 799-813. https://doi.org/10.21205/deufmd.2022247210.
EndNote Turan Karaoğlan S, Göktürkler G (01 Eylül 2022) Gravite Anomalilerinin Guguk Kuşu Arama Algoritması ile Ters Çözümü. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 24 72 799–813.
IEEE S. Turan Karaoğlan ve G. Göktürkler, “Gravite Anomalilerinin Guguk Kuşu Arama Algoritması ile Ters Çözümü”, DEUFMD, c. 24, sy. 72, ss. 799–813, 2022, doi: 10.21205/deufmd.2022247210.
ISNAD Turan Karaoğlan, Seçil - Göktürkler, Gökhan. “Gravite Anomalilerinin Guguk Kuşu Arama Algoritması Ile Ters Çözümü”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 24/72 (Eylül 2022), 799-813. https://doi.org/10.21205/deufmd.2022247210.
JAMA Turan Karaoğlan S, Göktürkler G. Gravite Anomalilerinin Guguk Kuşu Arama Algoritması ile Ters Çözümü. DEUFMD. 2022;24:799–813.
MLA Turan Karaoğlan, Seçil ve Gökhan Göktürkler. “Gravite Anomalilerinin Guguk Kuşu Arama Algoritması Ile Ters Çözümü”. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen Ve Mühendislik Dergisi, c. 24, sy. 72, 2022, ss. 799-13, doi:10.21205/deufmd.2022247210.
Vancouver Turan Karaoğlan S, Göktürkler G. Gravite Anomalilerinin Guguk Kuşu Arama Algoritması ile Ters Çözümü. DEUFMD. 2022;24(72):799-813.

Dokuz Eylül Üniversitesi, Mühendislik Fakültesi Dekanlığı Tınaztepe Yerleşkesi, Adatepe Mah. Doğuş Cad. No: 207-I / 35390 Buca-İZMİR.