Superparamagnetic effects in EM surveys for Mineral Exploration

Mutton, Paul

doi:10.1071/aseg2012ab413

Cited by 10 publications

(6 citation statements)

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“…These algorithms have been used this author for the interpretation of TEM data in a chrome deposit in Zimbabwe (Zadorozhnaya et.al., 2012). In Australia Buselli (1982) have observed SPM effect above the Elura ore body and P. Mutton (2012) recognized SPM on airborne data. Excellent theoretical developments regarding the SPM effect have been published by T.Lee.…”

Section: Introductionmentioning

confidence: 93%

Different Type of Tem Signals Along Only One Profile: Benefit Study for Beginners

Hallbauer-Zadorozhnaya

2013

Symposium on the Application of Geophysics to Engineering and Environmental Problems 2013

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Section: Introductionmentioning

confidence: 93%

Different Type of Tem Signals Along Only One Profile: Benefit Study for Beginners

Hallbauer-Zadorozhnaya

2013

Symposium on the Application of Geophysics to Engineering and Environmental Problems 2013

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“…In order to quantify the fitting efficacy on real data, we used a line of VTEM data over an area that we also had Magnetic Viscosity Meter (MVM) data (Mutton, 2012). The two lines we will look at here are known as Kapalagulu Test Lines 1 and 2.…”

Section: Discussionmentioning

confidence: 99%

“…There are many high conductance anomalies on the map that would appear at first glance to be suitable drilling candidates in this dataset. With recent reductions in AEM noise levels, SPM effects (explained in the next section) has become an issue in lowamplitude, late-delay time data, and many anomalies have been drilled based on mistaken interpretation (Mutton, 2012). The challenge lies in determining which of these anomalies indicate basement conductor responses, and which indicate SPM responses.…”

Section: Superparamagnetismmentioning

confidence: 99%

Correcting for SPM effects in airborne EM

Kratzer

Macnae

Mutton³

2013

ASEG Extended Abstracts

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INTRODUCTION Superparamagnetism(SPM) effects in airborne electromagnetic (AEM) surveys can be a source of needless expense in exploration if not identified, due to unnecessary drilling or further exploration. AEM surveys are commonly used for conductor mapping. Figure 4 shows, for example, the EMFlow apparent conductance (surface to 600 m depth) for a VTEM survey in the Mwese area in Africa. There are many high conductance anomalies on the map that would appear at first glance to be suitable drilling candidates in this dataset. With recent reductions in AEM noise levels, SPM effects (explained in the next section) has become an issue in lowamplitude, late-delay time data, and many anomalies have been drilled based on mistaken interpretation (Mutton, 2012). The challenge lies in determining which of these anomalies indicate basement conductor responses, and which indicate SPM responses. METHOD AND RESULTSFor a distribution of SPM decays, the signal from SPM appears as proportional to 1/t, in a dB/dt receiver. We use a 1/t basis function, together with exponential decays, to decompose AEM data. Previous work has described basis function decomposition of AEM data for EM response characterisation. This approach has recently been extended to airborne IP effect detection through least-squares fitting of both EM and IP basis functions as described in Kratzer and Macnae (2012). This last paper provides a comprehensive description of basis function fitting methodology, which we will briefly summarise below. To fit for SPM effects we add a single basis function to the equation representing an inverse delay-time:where t k and t k+1 are the start and end of the sample windows with respect to the primary field turn-off. We then construct our least-squares problem:where R is our time-series data, AEM are our exponential EM basis functions, A IP our IP basis functions, A SPM is our SPM basis function from equation 4, Λ is the smoothing parameter bidiagonal matrix of -λ and λ, and a EM , a IP and a SPM are the EM, IP and SPM amplitudes. AIP effects in AEM were accurately modelled with AIP as well as AEM basis functions as described by Kratzer and Macnae (2012). In the current work, we found that several modifications were necessary to the AIP fitting process, in order to reliably detect SPM. For example, we found that if any significant weight was given to minimising errors in fitting the early channels (<0.5ms delay) in VTEM, the SPM basis function was not used, and so these early channels were not used in the processing. We have found that normalisation of the EM decay basis functions is necessary to prevent the large dynamic range of data leading to unstable fitting. This process may have the effect of allowing unstable fits of the very long and very short decays by the fitting algorithm. In order to reduce the incentive of the least-squares fitting algorithm to fit very long EM time constants to SPM signals, we also normalise the EM smoothing matrix Λ (defined above) (along with the EM basis functions). Figure 1 shows an e...

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“…Superparamagnetism (SPM) has long been known to be a small amplitude effect visible at late delay times in ground electromagnetic (EM) data (Buselli ; Pasion, Billings and Oldenburg ). More recently, Mutton (); Kratzer, Macnae and Mutton (); and Sattel and Mutton (, ) present examples of SPM affecting the late delay times of airborne electromagnetic (AEM) data. Mutton () discussed miniaturised ground equipment that was able to confirm that the origin of most late‐time responses in one area in Africa was SPM signals in the top 1 or 2 m of soil.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, Mutton (); Kratzer, Macnae and Mutton (); and Sattel and Mutton (, ) present examples of SPM affecting the late delay times of airborne electromagnetic (AEM) data. Mutton () discussed miniaturised ground equipment that was able to confirm that the origin of most late‐time responses in one area in Africa was SPM signals in the top 1 or 2 m of soil. These SPM sources were spatially coincident with AEM anomalies initially interpreted to have deep conductive sources.…”

Section: Introductionmentioning

confidence: 99%

Definitive superparamagnetic source identification through spatial, temporal, and amplitude analysis of airborne electromagnetic data

Macnae

2016

Geophysical Prospecting

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The aim of this paper is to add confidence to existing methods using decay shape analysis to detect superparamagnetic responses in airborne electromagnetic data. While expensive to acquire, vertical spatial gradient measurements of the electromagnetic signals can discriminate near‐surface superparamagnetic sources. This research investigated the use of horizontal spatial gradients and amplitude information as further indicators of superparamagnetic. High horizontal gradients were shown both theoretically and in field data to help discriminate superparamagnetic from deep mineral targets. Further, superparamagnetic responses have characteristically small amplitudes inconsistent with realistic mineral exploration targets at shallow depths.

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Superparamagnetic effects in EM surveys for Mineral Exploration

Cited by 10 publications

References 1 publication

Different Type of Tem Signals Along Only One Profile: Benefit Study for Beginners

Different Type of Tem Signals Along Only One Profile: Benefit Study for Beginners

Correcting for SPM effects in airborne EM

Definitive superparamagnetic source identification through spatial, temporal, and amplitude analysis of airborne electromagnetic data

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