Düşey Elektrik Sondajı Verilerinin Genelleştirilmiş Regresyon Sinir Ağları ile Ters Çözümü

Yıl 2024, Cilt: 12 Sayı: 1, 121 - 133, 26.01.2024

Doğukan Durdağ Ertan Pekşen

https://doi.org/10.29130/dubited.1100533

Öz

Düşey elektrik sondajı (DES) verilerinin ters çözümü doğrusal olmayan bir problem olması nedeniyle zor bir işlemdir. Bu çalışmada, genelleştirilmiş regresyon sinir ağlarının özdirenç problemine uygulanabilirliği araştırılmıştır. Genelleştirilmiş regresyon sinir ağları (GRNN) ile düşey elektrik sondajı verilerinin ters çözümü test edilmiştir. Sinir ağı düz çözüm ile oluşturulan yapay verilerle eğitilmiştir. Ağ hem eğitim seti içinde bulunmayan diğer yapay veriler ile hem de arazi verisi ile test edilmiştir. Ayrıca GRNN parametresi olan yayılım parametresi de farklı değerler kullanılarak karşılaştırılmıştır. En düşük hata oranını veren yayılım parametresi kullanılmıştır. Yapay veriler 3 tabakalı modellerdir. Arazi verisi ise 4 tabakalı olarak değerlendirilmiştir. GRNN çıktısı yapay verilerde 3 tabakalı model için 3 özdirenç ve 2 tabaka kalınlığı olmak üzere 5 parametredir. Arazi verisinde ise 4 tabakalı bir model için 4 özdirenç ve 3 tabaka kalınlığı olmak üzere 7 parametredir. GRNN ile ters çözüm sonuçları çok düşük hata oranları ile başarılı olmuştur. DES verilerinin kalınlık ve özdirenç kestirimleri için GRNN yöntemi performansı yüksek bir oranda başarılı olmuştur.

Anahtar Kelimeler

Yapay sinir ağları, Özdirenç, GRNN, DES

Inversion of Vertical Electrical Sounding Data by Using General Regression Neural Network

Öz

The inversion of vertical electrical sounding (VES) data is a difficult task, since it is a non-linear problem. In this study, the general regression neural network approach was investigated to solve vertical electrical sounding inverse problems. The general regression neural network (GRNN) was tested to inversion of vertical electrical sounding data. The neural network was trained with synthetic data sets created by forward modeling. The network was tested both with another set of synthetic data and with field data. In addition, the spread parameter which is the GRNN parameter was compared using different values. The spread parameter giving the lowest error rate was used. Synthetic data are 3 layers models. Field data was inverted as 4 layers. The GRNN output is 5 parameters which are 3 resistivity and 2 layer thickness for the 3-layer model in synthetic data. In field data, there are 7 parameters which are 4 resistivity and 3 layer thickness for a 4-layer model. Inversion results of GRNN were successful with very low error rates. The GRNN method was successful with a high performance rate for estimation of the thickness and resistivity of VES data.

Anahtar Kelimeler

Artificial neural networks, Resistivity, GRNN, VES

Kaynakça

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