Signal and Noise Detection in Magnetotelluric Data by the Artificial Neural Network Method

Year 2013, Volume: 34 Issue: 1, 141 - 168, 01.02.2013

Ebru Şengül Uluocak , Emin U. Ulugergerli Hilal Göktaş

Abstract

In this study artificial neural network method was used to classify noisy components in the MT method data. For this purpose a multi-layered, feed-foorward and back propagation model was employed. Noisy time windows were determined to an accuracy of 89 % depending on the training data set. In addition, when all types of noise in the data are defined (synthetic data), all noisy time windows can be sellected and eliminated by artificial neural network method.Test results from synthetic and field data indicate that artificial neural network classification is succesfull in identifying and eliminating the noisy data windows compared to both visual inspection and conventional assessment methods

Keywords

Magnetotelluric, Artificial Neural Network, Time Series, Signal-Noise Detection

Yapay Sinir Ağı Yöntemi ile Manyetotellürik Veride Sinyal ve Gürültü Ayırımı

Abstract

Bu çalışma kapsamında manyetotellürik yöntem verisindeki gürültü bileşenlerini sınıflamak için yapay sinir ağı yöntemi kullanılmıştır. Bu amaçla çok katmanlı, ileri beslemeli ve geri yayılımlı bir model oluşturulmuştur. Seçilen eğitim setine bağlı olarak gürültülü zaman pencereleri % 89 doğrulukla belirlenmiştir. Ayrıca verideki gürültü türlerinin hepsi tanımlandığında (yapay veri), tüm gürültülü pencereler yapay sinir ağı yöntemi ile seçilip elenebilmektedir.Yapay veri ve arazi verisi ile yapılan uygulamalar sonucunda, hem görsel denetlemeye hem de geleneksel değerlendirme yöntemlerine göre, gürültülü veri pencerelerini sınıflayıp elemede yapay sinir ağı yönteminin daha başarılı oldugu gösterilmiştir

Keywords

Manyetotellürik, Yapay Sinir Ağı, Zaman Serisi, Sinyal-Gürültü Tanıma

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