Parameter estimation by Differential Search Algorithm from horizontal loop electromagnetic (HLEM) data

Alkan, Hilal; Balkaya, Çağlayan

doi:10.1016/j.jappgeo.2017.12.016

Cited by 20 publications

(8 citation statements)

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“…These parameters are vital for the success of the optimization and their selection depends on the nature of the problem under consideration. Therefore, parameter tuning studies should be performed before the parameter estimations performed by global optimization algorithms (Fernandez-Martinez et al, 2010;Pekşen et al, 2014;Ekinci et al, 2016Ekinci et al, , 2017Balkaya et al, 2017;Alkan and Balkaya, 2018) even though they are time-consuming (Eiben and Smith, 2011).…”

Section: Tuning and Parameter Estimations Through Synthetic Datamentioning

confidence: 99%

“…the nonuniqueness and ill-posedness phenomena, noise content, and also the insufficient number of observed data, inversion techniques are generally successfully used in geophysical parameter estimation studies with the help of some constraints and prior information. While improving the cost/error/objective function, which is the indicator of the fitness between the observed and the calculated geophysical data, the global minimum is searched in a model space using either a global optimization or a local optimization technique (Gallardo and Meju, 2004;Tarantola, 2005;Ekinci, 2008;Ekinci and Demirci, 2008;Fernández-Martínez et al, 2010;Mehanee et al, 2011;Göktürkler and Balkaya, 2012;Biswas and Sharma, 2014;Mehanee and Essa, 2015;Alkan and Balkaya, 2018). Gradient-based local optimization techniques are known to be faster in terms of convergence rates and computational cost, but their success strongly depends on the initial guess, which should be in the close neighborhood of the global minimum (Menke, 1989;Chunduru et al, 1997;Başokur et al, 2007;Ekinci and Demirci, 2008;Maiti et al, 2011;Ogunbo, 2018).…”

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confidence: 99%

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Parameter Estimations from Gravity and Magnetic Anomalies Due to Deep-Seated Faults: Differential Evolution versus Particle Swarm Optimization

2019

Turkish J Earth Sci

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Introduction Due to the mathematical nature of the gravity and magnetic methods in geophysics, numerous data processing techniques are easily performed to analyze their anomalies obtained from different types of investigations changing in a wide range of varieties. Based on the objectives of the investigations, the most commonly used techniques are generally separated into two different groups. The first group techniques, involving linear transformations, directional derivative-based techniques, image enhancement procedures, spectral methods, filtering operators, etc., generally aim at exploring and locating the geological source structures causing the potential field anomalies (

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Section: Tuning and Parameter Estimations Through Synthetic Datamentioning

confidence: 99%

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confidence: 99%

Parameter Estimations from Gravity and Magnetic Anomalies Due to Deep-Seated Faults: Differential Evolution versus Particle Swarm Optimization

2019

Turkish J Earth Sci

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“…b. Remarkably, nature-inspired derivative-free metaheuristic algorithms have been effectively applied with proper modifications, including particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), differential search (DS), simulated-annealing (SA), ant-colony optimization (ACO), gravitational search algorithm (GSA), bat algorithm (BA), manta-ray foraging (MRF) optimization (Balkaya et [44][45][46][47][48][49][50][51][52] [15] [53,54] . However, in general, the correctness and accuracy of the results obtained by the aforementioned interpretation techniques depend on the precision by which the anomaly of the source structure/s is extracted from the entire measured information.…”

Section: Introductionmentioning

confidence: 99%

A Novel Metaheuristic Strategy for the Elucidation of Gravity Anomalies with Application to Geothermal Energy Investigations and Volcanic Activity Studies: Hunger Games Search (HGS)

Su,

Ai,

Qin

et al. 2023

Preprint

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Recent metaheuristic approaches are extensively and intensively being introduced to the interpretation of gravity anomalies due to their superior advantages. We emphasize the application of Hunger Games Search (HGS), a novel metaheuristic inspired by hunger-driven instincts and behavioral choices of animals, to elucidate gravity data for geothermal energy exploration and volcanic activity study. After analyzing the modal features of the predetermined objective function and tuning the algorithm control parameters involved, HGS has been trial-tested on simulated data sets of different scenarios and finally experienced in two field cases from India and Japan. Notably, a second moving average (SMA) strategy has been successfully integrated into the objective function to eradicate the regional component from observed responses. Post-inversion uncertainty tests have been also implemented to understand the reliability of solutions achieved. The solutions obtained by HGS have been compared in terms of convergence rate, accuracy, stability, and robustness with the solutions of the commonly utilized Particle Swarm Optimization (PSO) algorithm. Based on the results accessed, the theoretical and field cases presented could be recuperated more precisely, more stably, more robustly and more coherently with the available geophysical, geological, and borehole verification, as HGS is able to explore the model space more comprehensively without compromising its capability to approach the global minimum efficiently. This novel metaheuristic thus can be considered as a promising tool in geothermal energy investigations and the study of volcanic activities and further recommended to reconnaissance studies aimed at effectively recognizing subsurface structures from various geophysical anomalies.

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“…Literatürde sıklıkla kullanılan metasezgisel yöntemler genetik algoritma-GA [4], yapay ısıl işlem-YIİ [5], parçacık sürü optimizasyonu-PSO [6], farksal evrim-FE [7][8][9] olarak örneklendirilebilir. Jeofizikte parametre kestirim çalışmalarında yaygın olarak kullanılan algoritmalara ise GA [10][11][12], PSO [13][14][15][16][17][18][19], FE [2,3,[19][20][21][22][23][24], YIİ algoritması [25][26][27][28][29] farksal arama algoritması [30,31] örnek olarak verilebilir. Parametre kestirim çalışmalarında uygulanan güncel algoritmalara örnekler ise gri kurt optimizasyonu [32] geri-izleme arama optimizasyonu [33], guguk kuşu arama algoritması [34] olarak verilebilir.…”

Section: Introductionunclassified

Inversion of Gravity Anomalies by Cuckoo Search Algorithm

KARAOĞLAN>

Göktürkler

2022

Deu Muhendislik Fakultesi Fen Ve Muhendislik

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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.

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Parameter estimation by Differential Search Algorithm from horizontal loop electromagnetic (HLEM) data

Cited by 20 publications

References 54 publications

Parameter Estimations from Gravity and Magnetic Anomalies Due to Deep-Seated Faults: Differential Evolution versus Particle Swarm Optimization

Parameter Estimations from Gravity and Magnetic Anomalies Due to Deep-Seated Faults: Differential Evolution versus Particle Swarm Optimization

A Novel Metaheuristic Strategy for the Elucidation of Gravity Anomalies with Application to Geothermal Energy Investigations and Volcanic Activity Studies: Hunger Games Search (HGS)

Inversion of Gravity Anomalies by Cuckoo Search Algorithm

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