A Review on Recent High-Resolution Edge Filters for Interpreting Potential Field Data

Pham L. T., Oliveira S. P., ÖKSÜM E.

Surveys in Geophysics, 2026 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1007/s10712-026-09960-9
  • Dergi Adı: Surveys in Geophysics
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Artic & Antarctic Regions, Compendex, Geobase, INSPEC, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Earth, Atmospheric, & Aquatic Science Collection (ProQuest), Technology Collection (ProQuest)
  • Anahtar Kelimeler: Edge filters, High resolution, Potential fields
  • Süleyman Demirel Üniversitesi Adresli: Evet

Özet

Detecting the source edges from potential field data is crucial in geological interpretations since it helps to identify contacts, faults, and other tectonic features. In recent years, many high-resolution filters have been introduced to generate sharp signals over the edges. In this study, we review 16 recent high-resolution filters, and introduce two frameworks for most of these filters. The filters are compared and discussed in terms of their accuracy and sharpness in detecting the edges using synthetic data. It is found that the filters based on the horizontal gradient are helpful in reducing the false information, while the filters using the second vertical derivative in the numerator can provide the edges with the highest resolution. The findings also show that the performance of many of the newer filters based on horizontal gradient or modified horizontal gradient versions is similar or lower compared to the previous horizontal gradient-based filters. Finally, we estimate and discuss the edges obtained from applying the filters to aeromagnetic data in the Montresor area (Canada), comparing the inferred edges with known geological structures. The obtained results demonstrate that the horizontal gradient-based filters are a useful tool for interpreting potential field data.