Curie-temperature depth estimation using a self-similar magnetization model

Maus, Stefan; Gordon, D.; Fairhead, Derek

doi:10.1111/j.1365-246x.1997.tb00945.x

Cited by 148 publications

(136 citation statements)

References 35 publications

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“…The observed spectrum shows a fall-off in power at the longest wavelengths (> 20 km) compared to the synthetic values. This difference is often seen and is interpreted to be caused by the limited depth extent of magnetic sources (Maus et al, 1997;Bouligand et al, 2009). Using an FFT approach to compute the fractal field ensures the periodicity requirement at the extended grid edges is met.…”

Section: Methodsmentioning

confidence: 99%

“…For gravity data, published values of β range from −4.5 to −5 based on regional and continent-wide data compilations (Maus and Dimri, 1996;Maus et al, 1998;Pilkington and Todoeschuck, 2004). Magnetic data show a wide range of β values between −1 and −4.8, based on sample areas with scales from <10 km up to >1000 km (Gregotski et al, 1991;Pilkington and Todoeschuck, 1993;Maus and Dimri, 1995;1996;Maus et al, 1997;Bouligand et al, 2009). Although not a necessary requirement for the fractal (or scaling) potential field description (Fedi et al, 1997;Quarta et al, 2000), the spatial distribution of rock properties (density and magnetic susceptibility) that cause these fields are also fractal with behaviours summarized in Bouligand et al (2009).…”

Section: Fractal Description Of Potential Fieldsmentioning

confidence: 99%

“…The fractal model of magnetic and gravity field data has already been exploited in a variety of uses including kriging of aeromagnetic data using a fractal covariance model (Pilkington et al, 1994), inversion for fractally magnetised source distributions (Maus and Dimri, 1995), Curie depth determination (Maus et al, 1997;Bouligand et al, 2009;Bansal et al, 2011), deriving accurate covariance models for satellite gravity data (Bansal and Dimri, 2005) and synthetic model-making to determine filtering parameters (Pilkington and Cowan, 2006). In the following, we outline a further use of fractals in the preparation of gravity and magnetic data grids prior to FFT-based processing and enhancement algorithms.…”

Section: Fractal Description Of Potential Fieldsmentioning

confidence: 99%

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Grid preparation for magnetic and gravity data using fractal fields

Pilkington

Keating

2012

Nonlin. Processes Geophys.

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Abstract. Most interpretive methods for potential field (magnetic and gravity) measurements require data in a gridded format. Many are also based on using fast Fourier transforms to improve their computational efficiency. As such, grids need to be full (no undefined values), rectangular and periodic. Since potential field surveys do not usually provide data sets in this form, grids must first be prepared to satisfy these three requirements before any interpretive method can be used. Here, we use a method for grid preparation based on a fractal model for predicting field values where necessary. Using fractal field values ensures that the statistical and spectral character of the measured data is preserved, and that unwanted discontinuities at survey boundaries are minimized. The fractal method compares well with standard extrapolation methods using gridding and maximum entropy filtering. The procedure is demonstrated on a portion of a recently flown aeromagnetic survey over a volcanic terrane in southern British Columbia, Canada.

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Section: Methodsmentioning

confidence: 99%

Section: Fractal Description Of Potential Fieldsmentioning

confidence: 99%

Section: Fractal Description Of Potential Fieldsmentioning

confidence: 99%

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Grid preparation for magnetic and gravity data using fractal fields

Pilkington

Keating

2012

Nonlin. Processes Geophys.

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“…The limited depth extent of the crustal magnetization would be visible in magnetic maps, covering less than 100 × 100 km (Maus et al 1997). Okubo et al (1985Okubo et al ( , 2003 suggested the optimal dimensions of the investigated square window to be about ten times the actual target depth (Hisarli et al 2011).…”

Section: Data Processing and Analysismentioning

confidence: 99%

Estimation of Curie point depths and heat flow from Ardebil province, Iran, using aeromagnetic data

et al. 2016

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This study attempts to estimate the Curie point depth (CPD) using the centroid and forward modelling of the spectral peak methods in the Sabalan geothermal field in Ardabil, NW of Iran. The reduced-to-pole aeromagnetic data were divided into 18 overlapping blocks of the sizes of 100 × 100 km. In the centroid method, the average depth to the top of the deepest crustal block, Z t , was first computed by linear fitting to the second longest wavelength segment of the power spectrum of aeromagnetic data. Then, depth to the centroid of the deepest crustal block, Z 0 , was computed by linear fitting to the longest wavelength segment of the power spectrum of the aeromagnetic data. The depth to the magnetic bottom was obtained from Z b = 2Z 0 − Z t . In the forward modelling of the spectral peak method, the modelled spectra fitted to the observed spectrum iteratively and Z t and Z b were finally estimated. According to the obtained results, the CPD varies from 10 to 18.6 km. The Curie temperature of magnetite was used to determine the thermal gradient and the heat flow in the area. The study area is found to have a great energy potential in the west, northwest and the southwest of the Sabalan with shallow CPD, high geothermal gradient and heat flow.

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“…As a result of the large variability of magnetic field survey data and measured magnetic susceptibilities, several studies have applied fractal geometry techniques to magnetic field and susceptibility data models (Gettings et al, 1991;Gregotski et al, 1991;Todoeschuck, 1993, 1995;Pilkington et al, 1994;Maus and Dimri, 1994Maus et al, 1997;Zhou and Thybo, 1998;Maus, 1999;Quarta et al, 2000). Most authors (e.g.…”

Section: Basis For a Fractal Modelmentioning

confidence: 99%

Multifractal magnetic susceptibility distribution models of hydrothermally altered rocks in the Needle Creek Igneous Center of the Absaroka Mountains, Wyoming

Gettings

2005

Nonlin. Processes Geophys.

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Abstract. Magnetic susceptibility was measured for 700 samples of drill core from thirteen drill holes in the porphyry copper-molybdenum deposit of the Stinkingwater mining district in the Absaroka Mountains, Wyoming. The magnetic susceptibility measurements, chemical analyses, and alteration class provided a database for study of magnetic susceptibility in these altered rocks. The distribution of the magnetic susceptibilities for all samples is multi-modal, with overlapping peaked distributions for samples in the propylitic and phyllic alteration class, a tail of higher susceptibilities for potassic alteration, and an approximately uniform distribution over a narrow range at the highest susceptibilities for unaltered rocks. Samples from all alteration and mineralization classes show susceptibilities across a wide range of values. Samples with secondary (supergene) alteration due to oxidation or enrichment show lower susceptibilities than primary (hypogene) alteration rock.Observed magnetic susceptibility variations and the monolithological character of the host rock suggest that the variations are due to varying degrees of alteration of blocks of rock between fractures that conducted hydrothermal fluids. Alteration of rock from the fractures inward progressively reduces the bulk magnetic susceptibility of the rock. The model introduced in this paper consists of a simulation of the fracture pattern and a simulation of the alteration of the rock between fractures. A multifractal model generated from multiplicative cascades with unequal ratios produces distributions statistically similar to the observed distributions. The reduction in susceptibility in the altered rocks was modelled as a diffusion process operating on the fracture distribution support. The average magnetic susceptibility was then computed for each block. For the purpose of comparing the model results with observation, the simulated magnetic susceptibilities were then averaged over the same interval as the measured data. Comparisons of the model and data from drillholes show good but not perfect agreement.

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Curie-temperature depth estimation using a self-similar magnetization model

Cited by 148 publications

References 35 publications

Grid preparation for magnetic and gravity data using fractal fields

Grid preparation for magnetic and gravity data using fractal fields

Estimation of Curie point depths and heat flow from Ardebil province, Iran, using aeromagnetic data

Multifractal magnetic susceptibility distribution models of hydrothermally altered rocks in the Needle Creek Igneous Center of the Absaroka Mountains, Wyoming

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