Search

La capacità di descrivere in maniera accurata e quantitativa il modello geologico del sottosuolo in termini di conducibilità elettrica a partire dall'analisi di dati elettromagnetici airborne nel dominio del tempo rappresenta la principale sfida in numerose applicazioni: da quelle idrogeofisiche a quelle ambientali, dall'esplorazione mineraria a quella petrolifera. L'accuratezza dei modelli inversi 1D è influenzata da una serie di parametri che descrivono il sistema nel calcolo del modello diretto (Christiansen et al., 2011). Variazioni nella modellizzazione dei suddetti parametri possono portare alla definizione di modelli inversi più o meno differenti tra loro. Un'accurata misura di tali parametri si rende quindi necessaria per ottenere risultati soddisfacenti. Nel presente lavoro l'attenzione è posta sul metodo di campionamento del transiente elettromagnetico e viene mostrato come l'utilizzo di diversi metodi di campionamento possa influenzare i risultati dell'inversione, in termini di resistività e spessore degli elettrostrati.

In this research, I focused on processing and modeling of TDEM and aeromagnetic data acquired during the same survey, with the aim of exploring the possibilities for a joint interpretation of these different datasets. My research involved both methodological advances and applications to real dataset. In particular, I will show two case studies, both located in Canada. The first one, is related to Broken Evil prospect in Ontario. This region hosts some of the richest mineral deposits of Canada (especially volcanic massive sulfide deposits). The second one is related to Drybones bay in Northwest Territories, where in 1994 a completely underwater kimberlite structure was discovered. The interpretative results of the TDEM and magnetic data reveal that the sensitivity of the two methods may differ with respect to the investigated geological structures. This would imply a certain difficulty in setting up a true joint inversion algorithm to interpret these two data types simultaneously that would be valid in all the studied cases. However, results show how with different resolution and depth of investigation, in the studied areas the cooperation of the TDEM and aeromagnetic data can lead to a better understand of the structures at depth.

This paper focuses on the sampling of the electromagnetic transient as acquired by airborne time-domain electromagnetic (TDEM) systems. Typically, the sampling of the electromagnetic transient is done using a fixed number of gates whose width grows logarithmically (log-gating). The log-gating has two main benefits: improving the signal to noise (S/N) ratio at late times, when the electromagnetic signal has amplitudes equal or lower than the natural background noise, and ensuring a good resolution at the early times. However, as a result of fixed time gates, the conventional log-gating does not consider any geological variations in the surveyed area, nor the possibly varying characteristics of the measured signal. We show, using synthetic models, how a different, flexible sampling scheme can increase the resolution of resistivity models. We propose a new sampling method, which adapts the gating on the base of the slope variations in the electromagnetic (EM) transient. The use of such an alternative sampling scheme aims to get more accurate inverse models by extracting the geoelectrical information from the measured data in an optimal way.

We present the joint interpretation of airborne electromagnetic and aeromagnetic data, acquired to study kimberlite pipes. We analyse the data surveyed in 2005 over Drybones Bay, Archean Slave Province of the Northwest Territories, northern Canada. This area hosts a recently discovered kimberlite province with >150 kimberlite pipes. Magnetic and electromagnetic data were each one modelled by 1D inversion. For magnetic data we inverted vertical soundings built through upward continuations of the measured data at various altitudes. The validity of the method was prior verified by tests on synthetic data. Electromagnetic data were processed and inverted using the modified AarhusINV code, with Cole-Cole modelling, in order to take into account induced polarization effects, consisting in negative voltages and otherwise skewed transients. The integrated study of the two kinds of data has led to a better understanding of the structures at depth, even though the comparison between the magnetic and the electromagnetic models shows the different sensitivity of the two methods with respect to the geological structure at Drybones Bay.