Modelling of magnetic data for scaling geology

Abstract

Interpretation of magnetic data can be carried out either in the space or frequency domain. The interpretation in the frequency domain is computationally convenient because convolution becomes multiplication. The frequency domain approach assumes that the magnetic sources distribution has a random and uncorrelated distribution. This approach is modified to include random and fractal distribution of sources on the basis of borehole data. The physical properties of the rocks exhibit scaling behaviour which can be defined as P(k) = Ak−β, where P(k) is the power spectrum as a function of wave number (k), and A and β are the constant and scaling exponent, respectively. A white noise distribution corresponds to β = 0. The high resolution methods of power spectral estimation e.g. maximum entropy method and multi‐taper method produce smooth spectra. Therefore, estimation of scaling exponents is more reliable. The values of β are found to be related to the lithology and heterogeneities in the crust. The modelling of magnetic data for scaling distribution of sources leads to an improved method of interpreting the magnetic data known as the scaling spectral method. The method has found applicability in estimating the basement depth, Curie depth and filtering of magnetic data.

Mapping of magnetic basement in Central India from aeromagnetic data for scaling geology

“…). Bansal and Dimri () summarised the values of scaling exponent for the source distribution as 2.4 to 4.6 for 3D sources, which will be lower for 2D and 1D distribution of sources. A recent study by Salem et al .…”

“…Some authors are of the opinion of assigning a fixed value of scaling exponent ß3 for a region (Fedi et al 1997). Bansal and Dimri (2014) summarised the values of scaling exponent for the source distribution as 2.4 to 4.6 for 3D sources, which will be lower for 2D and 1D distribution of sources. A recent study by Salem et al (2014) has shown the β value variation between 0 and 1.7 in the Central Red Sea region.…”

“…In case of random and correlated (scaling) distribution of sources, the power spectrum of magnetisation ( φ m ) is expressed by equation , and by combining equations and , the power spectrum of the magnetic field is expressed as (Maus and Dimri , ; Fedi et al . ; Bansal and Dimri ) …”

Crustal structure in the Campanian region (Southern Apennines, Italy) from potential field modelling

“…The statistical block-ensemble source is suitable for homogeneous blocks of different sizes and magnetization (i.e., gross homogeneous bodies) 85 , 106 . The white noise is suitable for uncorrelated, highly variable magnetization/density distributions 103 , 113 , whereas the scaling source is suitable for describing irregular magnetization/density distributions which have however some degrees of correlation 114 .…”

Curie Depth and Surface Heat Flow Estimation from Anomalous Magnetic Blocks in the Lower and Part of Middle Benue Trough and Anambra Basin

“…Thermal anomaly is a manifestation of renewable and environmentally benign green geothermal energy resources. Geothermal studies are carried out based on magnetic anomaly [1][2][3][4][5][6][7][8][9][10] . Curie point depth/Curie depth is the point depth at which the magnetic sources at a particu-lar geographic location within the Earth's crust lose their magnetism contents completely at a specific temperature known as the Curie point temperature [11] .…”