Anisya B.Tekkeli, Mehmet Ali Üge, Mehmet Safa Arslan

Aykırı değerler içeren VLF verilerinin süzgeçlenmesinde budanmış ortalama, Empirik Mod Ayrışımı ve dayanıklı regresyon yöntemlerinin karşılaştırılması

Çok Düşük Frekans (VLF) elektromanyetik yöntem , yeraltının sığ iletkenlik dağılımının belirlenmesi için sıklıkla uygulanır. Toplanan VLF verileri genelde aperiyodiktir ve bu nedenle doğrusal olmayan süzgeçleme yöntemleri VLF verilerinin gürültüden arındırılması için sıklıkla uygulanmaktadır. Bu çalışmada, aykırı değerler içeren verilerin değerlendirilmesinde daha başarılı yöntemlerin belirlenmesi için farklı doğrusal olmayan süzgeçler kuramsal ve gürültülü arazi verilerine uygulanmıştır. Kuramsal verilerin aksine, arazi verilerinde gerçek yeraltı modeli bilinemediği için, aynı ölçüm hattı üzerinde farklı zamanda toplanmış VLF-R ve Elektrik Rezistivie Tomografisi verileri değerlendirilmiştir ve sonuçları yeraltının daha doğru bir temsili olarak kabul edilmiştir. Kuramsal ve arazi verileri üzerinde gerçekleştirilen uygulamalarda, aykırı değerler içeren verilerde dayanıklı regresyonun diğer yöntemlere göre daha başarılı olduğunu göstermiştir.

Anahtar Kelimeler:

VLF, Empirik Mod Ayrışımı, dayanıklı regresyon, budanmış ortalama, ters çözüm

Comparison of trimmed mean, Empiric Mode Decomposition and robust regression methods for filtering VLF data with outliers

Very Low Frequency (VLF) electromagnetic method is widely implemented to determine shallow conductivity distribution of subsurface. Collected VLF data are usually aperiodic and consequently non-linear filtering techniques are often used for eliminating noise in VLF data. In this study, several non-linear filtering methods are implemented on synthetic and field data to determine methods performing better in case of outliers in data. In contrary of synthetic studies, the true subsurface model is unknown for the field data. Hence, VLF-R and Electrical Resistivity Tomography data, which are also collected over the same measuring profile, are also interpreted and their results are assumed as better representations of the subsurface. Applications on noisy synthetic and field datasets showed that the robust regression performs better than the other techniques in case of data with outliers.

Keywords:

VLF, Empiric Mode Decomposition, robust regression, trimmed mean, inversion,

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