Helicopter EM (ZTEM–VTEM) survey results over the Nuqrah copper–lead–zinc–gold SEDEX massive sulphide deposit in the Western Arabian Shield, Kingdom of Saudi Arabia

Legault, Jean M.; Izarra, Carlos; Prikhodko, Alexander; Zhao, Shuangyi; Saadawi, Emad M.

doi:10.1071/eg14028

Cited by 14 publications

(7 citation statements)

References 36 publications

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“…Figure 2 compares, for the sounding locations considered in Figure 1, the 3D responses calculated for the entire 3D model against the corresponding 1D measurements that would be obtained by considering exclusively the 1D portion of the original distribution just below the acquisition position. The acquisition specifications are those of a typical VTEM system [11,12] (e.g., each sounding consists of 54 measurements). Not surprisingly, the 1D responses (Figure 2b) are characterized by abrupt lateral changes associated with the lateral variations of the conductivity model (Figure 2a), whereas, in accordance with the physics, the 3D dB/dt data are much smoother (Figure 2c).…”

Section: Test 1: 3d Conductivity Distribution With Homogeneous Layersmentioning

confidence: 99%

“…Its first application probably dates back to 1948 [5], but since then, many different helicopter and fixed-wing acquisition systems have become commercially available. Amongst the most internationally known examples are: EQUATOR [6][7][8], Xcite [9,10], VTEM [11,12], SkyTEM [13], AeroTEM [14], MULTIPULSE [15], MEGATEM/GEOTEM [16,17], TEMPEST [18,19], and Spectrem [20].…”

Section: Introductionmentioning

confidence: 99%

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1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error

2021

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Airborne electromagnetic surveys may consist of hundreds of thousands of soundings. In most cases, this makes 3D inversions unfeasible even when the subsurface is characterized by a high level of heterogeneity. Instead, approaches based on 1D forwards are routinely used because of their computational efficiency. However, it is relatively easy to fit 3D responses with 1D forward modelling and retrieve apparently well-resolved conductivity models. However, those detailed features may simply be caused by fitting the modelling error connected to the approximate forward. In addition, it is, in practice, difficult to identify this kind of artifacts as the modeling error is correlated. The present study demonstrates how to assess the modelling error introduced by the 1D approximation and how to include this additional piece of information into a probabilistic inversion. Not surprisingly, it turns out that this simple modification provides not only much better reconstructions of the targets but, maybe, more importantly, guarantees a correct estimation of the corresponding reliability.

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Section: Test 1: 3d Conductivity Distribution With Homogeneous Layersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error

2021

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“…A case demonstrates the DNN predictive targeting for VMS mineralisation is conducted using TDEM data. In late 2011early 2012, as part of a larger survey campaign in the western Arabian Shield, a helicopter VTEM (Versatile Time-domain ElectroMagnetic; Witherly et al, 2004) survey was flown over the 1.4 Mt Nuqrah sedimentary exhalative (SEDEX) copperlead-zinc-gold massive sulphide deposits in order to determine their geophysical signatures (Legault et al, 2014).…”

Section: Sedex Case Studymentioning

confidence: 99%

Mineralisation predictive targeting using TensorFlow (Google) deep neural networks

Kwan

Johnson²,

Legault

et al. 2019

ASEG Extended Abstracts

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“…Helicopter transient electromagnetic (HTEM) system, with the advantage of high survey efficiency and lower cost, has been a more and more popular method in mineral exploration [1], environment investigation and unexploded material detection [2][3][4]. The transmitting coil and receiving coil are loaded by a helicopter, and the transmitting coil is used to create a primary time varying magnetic field, which causes eddy currents to flow in conductors.…”

Section: Introductionmentioning

confidence: 99%

Improved Bucking Coil Design in Helicopter Transient Electromagnetic System

Xiao¹,

Shi²,

Wu³

et al. 2017

PIER M

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Abstract-The presence of the primary field in the helicopter transient electromagnetic system makes the dynamic range of the response signal so large that it is difficult to observe the secondary field. Therefore, a bucking coil is usually introduced to eliminate the primary field. However, in a traditional design, the size of the bucking coil increases with the size of the system, which makes the bucking coil hard to install, and opposite magnetic moment is large in huge systems. In this paper, a new bucking coil design for a helicopter transient electromagnetic system is proposed. Compared with the traditional design, the bucking coil diameter, total weight and total magnetic moment in two designs are calculated. The results show that the bucking coil we designed is more than 8 times smaller and 5 times lighter than that in the traditional design, which is easier for installation. The bucking moment impact is reduced to 0.03% of the total magnetic moment when the diameter of the transmitting coil increases to 35 m, which improves the efficiency of the system. Then we analyze the requirement of manufactory precision and installation accuracy for the bucking coil in our design to get the best bucking result.

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Helicopter EM (ZTEM–VTEM) survey results over the Nuqrah copper–lead–zinc–gold SEDEX massive sulphide deposit in the Western Arabian Shield, Kingdom of Saudi Arabia

Cited by 14 publications

References 36 publications

1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error

1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error

Mineralisation predictive targeting using TensorFlow (Google) deep neural networks

Improved Bucking Coil Design in Helicopter Transient Electromagnetic System

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