Joint interpretation of magnetic and gravity data at the Golgohar mine in Iran

Geobody estimation by Bhattacharyya method utilizing nonlinear inverse modeling of magnetic data in Baba-Ali iron deposit, NW Iran

“… Synthetic and field data could utilized to validate the effectiveness and correctness of the developed nonlinear inverse algorithms. The ability of high resolution and rapid inversions is a crucial prerequisite for the inversion's practical application without either limiting or data down-sampling the resolution or size of the inversion domain [ 16 , 17 , 24 , 25 , 67 , 69 , 72 , 78 , 108 ]. …”

“…The ability of high resolution and rapid inversions is a crucial prerequisite for the inversion's practical application without either limiting or data down-sampling the resolution or size of the inversion domain [ 16 , 17 , 24 , 25 , 67 , 69 , 72 , 78 , 108 ].…”

“…As the magnetic inversion literature review in recent years, Utsugi [ 68 ] presented a 3D inversion method by combining lasso and ridge (L1 and L2) norm regularization to a real subsurface magnetic anomaly structure in Hokkaido Island, northern Japan. Milano et al [ 69 ] performed a 2D inversion modeling of gravity and magnetic data based on the damped weighted minimum-length solution in the Golgohar iron mine in central Iran. Results confirmed an increased correlation between susceptibility and density models in horizontal and vertical directions and demonstrated the existence of isolated sources with high density and magnetization distinctions.…”

3D joint interpretation of potential field, geology, and well data to evaluate a salt dome in the Qarah‐Aghaje area, Zanjan, NW Iran

“…Menke, 2012): $$$$\begin{equation}{\bf{m}}\; = {{\bf{m}}_{\rm{r}}}{\rm{\;}} + \left( {{\bf{W}}_{\rm{m}}^{ - 1}{{\bf{A}}^{\rm{T}}}} \right){\left( {{\bf{AW}}_{\rm{m}}^{ - 1}{{\bf{A}}^{\rm{T}}} + \alpha {{\bf{W}}_{\rm{d}}}} \right)^{ - 1}}\left( {{\bf{d}} - {\bf{A}}{{\bf{m}}_{\rm{r}}}} \right),\end{equation}$$$$where W m is a model‐weighting matrix produced by the multiplication of compactness and depth‐weighting functions and $${{\bf{W}}_{\rm{d}}} = \;I$$. This algorithm has been successfully manipulated for the inversion of DC resistivity (Varfinezhad et al., 2022), gravity (Varfinezhad & Ardestani, 2021), magnetic (Milano et al., 2021; Varfinezhad et al., 2020), and EM‐LIN (Parnow et al., 2021; Varfinezhad & Parnow, 2022) data.…”

“…) where W m is a model-weighting matrix produced by the multiplication of compactness and depth-weighting functions and W d = I. This algorithm has been successfully manipulated for the inversion of DC resistivity , gravity (Varfinezhad & Ardestani, 2021), magnetic (Milano et al, 2021; Compactness constraint is proposed by Last and Kubic (1983) as:…”