Inversion of Gravity Anomalies by Cuckoo Search Algorithm

Abstract

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.

Keywords

• Metaheuristics
• geophysics
• gravity
• inversion
• cuckoo search algorithm

Gravite Anomalilerinin Guguk Kuşu Arama Algoritması ile Ters Çözümü

Abstract

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.

Keywords

• Metasezgisel
• jeofizik
• gravite
• ters çözüm
• guguk kuşu arama algoritması

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