Linearized least-squares method for interpretation of potential-field data from sources of simple geometry

Ahmed Salem, Dhananjay Ravat, Martin F. Mushayandebvu, Keisuke Ushijima

Research output: Contribution to journalArticlepeer-review

72 Scopus citations


We present a new method for interpreting isolated potential-field (gravity and magnetic) anomaly data. A linear equation, involving a symmetric anomalous field and its horizontal gradient, is derived to provide both the depth and nature of the buried sources. In many currently available methods, either higher order derivatives or postprocessing is necessary to extract both pieces of information; therefore, data must be of very high quality. In contrast, for gravity work with our method, only a first-order horizontal derivative is needed and the traditional data quality is sufficient. Our proposed method is similar to the Euler technique; it uses a shape factor instead of a structural index to characterize the buried sources. The method is tested using theoretical anomaly data with and without random noise. In all cases, the method adequately estimates the location and the approximate shape of the source. The practical utility of the method is demonstrated using gravity and magnetic field examples from the United States and Zimbabwe.

Original languageEnglish
Pages (from-to)783-788
Number of pages6
Issue number3
StatePublished - 2004

Bibliographical note

Funding Information:
We greatly appreciate the constructive and thoughtful comments of John Peirce, Alan Reid, Jeffrey Phillips, and Richard Blakely. We are indebted to the staff of the Exploration Geophysical Laboratory of Kyushu University for their contribu- tion and support during this work. We are also indebted to the Geological Survey of Zimbabwe for providing the aeromagnetic data. The work of A. S. was made possible by funding from the Japan Society of Promotion of Science (JSPS). The contribution of D.R. was made possible by funding from NASA.


  • Geophysical signal processing
  • Gravity
  • Least mean squares methods
  • Linearisation techniques
  • Magnetism
  • Noise
  • Object detection
  • Potential energy functions

ASJC Scopus subject areas

  • Geochemistry and Petrology


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