A new stable downward continuation of airborne magnetic data based on Wavelet deconvolution

Abstract

This paper describes an efficient wavelet‐based deconvolution method for the downward continuation of airborne potential (magnetic and gravity) field data. We formulate the downward continuation process as a linear ill‐posed deconvolution problem. To obtain a reasonable downward continued field data, it is stabilized in a wavelet domain by minimizing the L1‐norm of the coefficients subject to the constraint that is the agreement of the upward response with the original observed data up to a white Gaussian noise level. The resulting convex constrained problem is then solved very fast and efficiently via the split Bregman iterations method. The generalized cross‐validation (GCV) criterion as a plotted curve versus the iteration count with new formulation is used to determine the optimum number of Bregman iterations without prior knowledge about noise properties. A synthetic magnetic field data embedded in a Gaussian noise is constructed from isolated multi‐source anomalies, whose results show that the airborne magnetic data can be continued stably downward by the proposed automatic sparse deconvolution method in a few iterations. When compared with conventional methods such as Tikhonov and Edge‐preserving with the proposed method, similar results were obtained. To test the performance on real data, we chose to use the airborne magnetic data of the central Iranian volcanic‐sedimentary belt. The enhanced downward continued data to a depth level of 200 m beneath the ground surface indicates adequate matching of high potential zones with previous working mines and copper deposits. Obtained results from both synthetic and real data confirm that the performance of the proposed technique is as good as the classical deconvolution ones.

Matlab tool REGCONT2: effective source depth estimation by means of Tikhonov’s regularized downwards continuation of potential fields

“…Fedi and Florio, 2002;Trompat et al, 2003;Cooper, 2004) and during the last years there can be followed something like a "boom" in this scientific area (e.g. Ma et al, 2013;Zeng et al, 2013;Zhang H. et al, 2013;Abedi et al, 2014;Zeng et al, 2014;Zeng et al, 2015;Zhang Y. et al, 2016, Zhou et al, 2018Florio and Fedi, 2018;Zhang et al, 2018).…”

Parâmetros de regularização de operadores diferenciais no processamento de dados aeromagnéticos

“…Em outras palavras, o parâmetro adequado corresponde ao modelo que minimiza a média dos resíduos preditivos do dado observado. A curva VCG (Figura 6) é construída plotando a função de desajuste dos dados normalizada pelo traço da matriz para um conjunto de teste de parâmetros de regularização na escala log-log (Abedi et al, 2014). Segundo Stickel (2010), este método é eficiente no processamento de dados irregularmente espaçados.…”