Estimating source location using normalized magnetic source strength calculated from magnetic gradient tensor data

Majid Beiki*, David A. Clark, James R. Austin, Clive A. Foss

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

76 Citations (Scopus)


For a number of widely used models, normalized source strength (NSS) can be derived from eigenvalues of the magnetic gradient tensor. The NSS is proportional to a constant q normalized by the nth power of the distance between observation and integration points where q is a shape factor depending upon geometry of the model and n is the structural index. The NSS is independent of magnetization direction, and its amplitude is only affected by the magnitude of magnetization. The NSS is also a homogenous function and satisfies Euler's homogeneity equation. Therefore, Euler deconvolution of the NSS can be used to estimate source location. In our algorithm, we use data points enclosed by a square window centered at maxima of the NSS for simultaneously estimating the source location and structural index. The window size is increased until it exceeds a predefined limit. Then the most reliable solution is chosen based on some statistical analysis (minimum uncertainty). One of the advantages of the presented method is that it allows automatic identification of the structural index as the constant background field is eliminated. Another advantage is reduction of interference effects from neighboring sources by differentiation of the NSS. We have compared our method with the analytic signal amplitude and when the magnetic source contains remanent magnetization with a different direction to the inducing field, the NSS provides more reliable information about source geometry. Application of the method has been demonstrated on an aeromagnetic data set from the Tuckers Igneous Complex, QueenslAustralia. The NSS has improved interpretation of magnetic anomalies for this igneous complex, for which available geologic information shows relatively strong remanent magnetization.

Original languageEnglish
Pages (from-to)J23-J37
Number of pages15
Issue number6
Publication statusPublished - Nov 2012
Externally publishedYes


  • Algorithm
  • Deconvolution
  • Interpretation
  • Magnetics
  • Magnetization


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