Radyal Taban Fonksiyonlu Yapay Sinir Ağları (RTFA) ve Levenberg-Marquardt(LM) Ters Çözüm Yöntemleriyle Küre Şekilli Yapıların Doğal Uçlaşma Anomalilerinin Değerlendirilmesi

Year 2023, Volume: 25 Issue: 73, 159 - 166, 26.01.2023

Petek Sındırgı , İlknur Kaftan

https://doi.org/10.21205/deufmd.2023257313

Abstract

Doğal uçlaşama (DU) uygulamalarında kaynak yapı özelliklerini belirlemek yöntemin temel amacıdır.
Çeşitli yöntemler bu özelliklerin saptanmasında kullanlmaktadır. Bu çalışmada Radyal Taban Fonksiyonlu Yapay Sinir Ağları (RTFA) ve geleneksel Levenberg-Marquardt (LM) ters çözüm yöntemleri DU verilerine uygulanmıştır. Çalışma iki aşamadan oluşmaktadır. İlk aşamada, gürültüsüz ve gürültülü küre şekilli kuramsal modelin DU anomalisinin her iki yöntemle ters çözümleri yapılarak model parametreleri saptanmıştır. İkinci aşamada ise yöntemler Seferihisar(İzmir) alanından toplanmış olan DU verilerine uygulanmıştır. Elde edilen sonuçlar karşılaştırıldığında, RTFA’nın LM ters çözüm sonuçlarına göre nispeten daha küçük hata değeriyle model parametrelerini saptadığı görülmüştür. Sonuç olarak, bu çalışma, RTFA yöntemi kullanılarak DU küre modeli ters çözümünün güvenilir bir şekilde yapılabileceğini ortaya koymuştur.

Keywords

Doğal Uçlaşma(DU), Radyal Taban Fonksiyonlu Yapay Sinir Ağlar (RTFA), Levenberg-Marquardt(LM) ters çözümü

Evaluation of Self-Potential Anomalies caused by Sphere Shaped Structures with Radial Basis Function Neural Networks (RBFNN) and Levenberg-Marquardt(LM) Inversion Methods

Abstract

The main purpose of the method is to determine the source structure properties in self-potential (SP) applications. Various methods are used to determine these properties. In this study, Radial Basis Function Neural Network (RBFANN) and traditional Levenberg-Marquardt (LM) inversion methods were applied to SP data. The study consists of two stages. In the first stage, the model parameters were determined by performing inverse solutions of the SP anomaly of the noise-free and noisy spherical polarized synthetic model with both methods. In the second stage, the methods were applied to the SP data collected from the Seferihisar (İzmir) field. When the results were compared, it was seen that RBFANN determined the model parameters with a relatively smaller error value than the LM inversion results. In conclusion, this study revealed that the SP sphere model inversion can be reliably performed using the RBFANN method.

Keywords

Self Potential, Radial Basis Function Neural Network (RBFANN), Levenberg-Marquardt(LM) inversion

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