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Journals / Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi / 2026 / Cilt: 24 Sayı: 70

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

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

Anisya B.tekkeli(İSTANBUL ÜNİVERSİTESİ-CERRAHPAŞA)0000-0001-9402-2689
Mehmet Ali Üge(İSTANBUL ÜNİVERSİTESİ-CERRAHPAŞA, MÜHENDİSLİK FAKÜLTESİ)0000-0002-5631-7133
Mehmet Safa Arslan(İSTANBUL ÜNİVERSİTESİ-CERRAHPAŞA, MÜHENDİSLİK FAKÜLTESİ)0000-0002-1233-963X
Pages
7–18
Publication Date
2022-01-17
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Applications on noisy synthetic and field datasets showed that the robust regression performs better than the other techniques in case of data with outliers.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Very Low Frequency (VLF) electromagnetic method is widely implemented to determine shallow conductivity distribution of subsurface.
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Key Findings & Data:Collected VLF data are usually aperiodic and consequently non-linear filtering techniques are often used for eliminating noise in VLF data.
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Conclusion & Impact:Applications on noisy synthetic and field datasets showed that the robust regression performs better than the other techniques in case of data with outliers.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:VLF · Empirik Mod Ayrışımı · dayanıklı regresyon

Abstract

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.

Özet

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.

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Cite This Article

B.tekkeli, A., Üge, M. A., Arslan, M. S. (2022). Comparison of trimmed mean, Empiric Mode Decomposition and robust regression methods for filtering VLF data with outliers. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, Cilt: 24 Sayı: 70, 7–18. https://doi.org/10.21205/deufmd.2022247002

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