Inversion of Gravity Anomalies by a Hybrid Metaheuristic Algorithm

Abstract

In this work, we introduce application of a hybrid algorithm (DE/PSO) to estimate the model parameters from residual gravity anomalies due to some simple geometrical bodies. This algorithm combines differential evolution (DE) and particle swarm optimization (PSO). To investigate the performance of the hybrid algorithm, test studies were carried out using synthetic and field data sets. The synthetic data sets include noise-free and noisy synthetic anomalies. Two published gravity anomalies from Cuba and Canada were used as the field data. In the hybrid algorithm, DE and PSO yield [premature] solutions separately and share their best solutions during an iterative process. An openly accessible metaheuristics package (NMOF) in R programming environment was used to implement the hybrid algorithm. Through simulations using synthetic anomalies, DE/PSO algorithm was successful to provide improved results. In comparison to the solutions from the single algorithms (DE and PSO), the DE/PSO algorithm shows more effectiveness in terms of accuracy and convergence. The true model parameters of noise-free and noisy synthetic gravity anomalies were recovered well by the hybrid algorithm. The results of inversion for the field examples are characterized by low residual values between the observed gravity anomalies and the calculated ones.

Keywords

• Differential evolution
• particle swarm optimization
• hybrid metaheuristic
• gravity anomaly
• geophysics

Gravite Anomalilerinin Hibrit Metasezgisel Algoritma ile Ters Çözümü

Abstract

Bu çalışmada, basit geometrik şekilli cisimlerden kaynaklanan rezidüel gravite anomalilerin model parametrelerinin kestirimi için bir hibrit algoritmanın (DE/PSO) uygulaması sunulmaktadır. Bu algoritma, Farksal Evrim (DE) ve Parçacık Sürü Optimizasyonunu (PSO) birleştirmektedir. Hibrit algoritmanın performansını araştırmak için kuramsal ve arazi veri setleri kullanılarak test çalışmaları gerçekleştirilmiştir. Kuramsal veri setleri, gürültüsüz ve gürültülü sentetik anomalileri içermektedir. Arazi verileri ise literatürde yer alan Küba ve Kanada gravite anomalileridir. Hibrit algoritmada, DE ve PSO algoritmaları ayrı ayrı [ilksel] çözümler üreterek tekrarlı bir süreç boyunca en iyi çözümlerini paylaşmaktadır. Hibrit algoritmayı gerçekleştirmek için R programlama ortamında açık erişimli bir metasezgisel paket (NMOF) kullanılmıştır. DE/PSO algoritması, kuramsal anomalilerin kullanıldığı simülasyonlarda, iyileştirilmiş sonuçlar sağlamada başarılı olmuştur. Her bir algoritmadan (DE ve PSO) gelen çözümlerle karşılaştırıldığında, DE/PSO algoritmasının, doğruluk ve yakınsama açısından daha etkili olduğu görülmüştür. Gürültüsüz ve gürültülü kuramsal gravite anomalilerinin doğru model parametreleri, hibrit algoritma tarafından daha iyi bir şekilde kestirilmiştir. Arazi örnekleri için ters çözüm sonuçları, gözlenen ve hesaplanan gravite anomalileri arasında düşük hata değerlerine sahiptir.

Keywords

• Farksal evrim
• parçacık sürü optimizasyonu
• hibrit metasezgisel
• gravite anomalisi
• jeofizik

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