Abstract

Magnetic surveying is a widely used and cost-efficient remote sensing method for the detection of subsurface structures at all scales. Traditionally, magnetic surveying has been conducted as ground or airborne surveys, which are cheap and provide large-scale consistent data coverage, respectively. However, ground surveys are often incomplete and slow, whereas airborne surveys suffer from being inflexible, expensive and characterized by a reduced signal-to-noise ratio, due to increased sensor-to-source distance. With the rise of reliable and affordable survey-grade Unmanned Aerial Vehicles (UAVs), and the developments of light-weight magnetometers, the shortcomings of traditional magnetic surveying systems may be bypassed by a carefully designed UAV-borne magnetometer system. Here, we present a study on the development and testing of a light-weight scalar field UAV-integrated magnetometer bird system (the CMAGTRES-S100). The idea behind the CMAGTRES-S100 is the need for a high-speed and flexible system that is easily transported in the field without a car, deployable in most terrain and weather conditions, and provides high-quality scalar data in an operationally efficient manner and at ranges comparable to sub-regional scale helicopter-borne magnetic surveys. We discuss various steps in the development, including (i) choice of sensor based on sensor specifications and sensor stability tests, (ii) design considerations of the bird, (iii) operational efficiency and flexibility and (iv) output data quality. The current CMAGTRES-S100 system weighs ∼5.9 kg (including the UAV) and has an optimal surveying speed of 50 km/h. The system was tested along a complex coastal setting in Brittany, France, targeting mafic dykes and fault contacts with magnetite infill and magnetite nuggets (skarns). A 2.0 × 0.3 km area was mapped with a 10 m line-spacing by four sub-surveys (due to regulatory restrictions). The sub-surveys were completed in 3.5 h, including >2 h for remobilisation and the safety clearance of the area. A noise-level of ±0.02 nT was obtained and several of the key geological structures were mapped by the system.

Highlights

  • Getting reliable geophysical models of the subsurface is an absolute necessity for the successful mapping of raw materials resources, subsurface contamination, archaeology or Unexploded Ordnance (UXO)

  • We describe the development, testing and output data quality of a light-weight, high-speed, scalar field magnetometer bird intended for km-scale surveying by small-tomedium sized Unmanned Aerial Vehicles (UAVs)

  • A Gaussian distribution is found for both the GSMP-35u and the GSM19 gradients (Figure 5b), again disregarding the abnormal behavior at 15.43 for the GSM19, which adds a small right-sided tail to the histogram distribution

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Summary

Introduction

Getting reliable geophysical models of the subsurface is an absolute necessity for the successful mapping of raw materials resources, subsurface contamination, archaeology or Unexploded Ordnance (UXO). Since being accepted as a cost-efficient subsurface mapping approach more than 50 years ago [7,8,9,10], magnetic measurements have been conducted mainly as ground (walking) magnetic surveys, that is, a person traversing an area of interest while carrying a magnetometer, or as airborne surveys in which a small airplane or helicopter carries a magnetometer system in various configurations. Test 1 (Figure S2, Supplementary Data) indicate that the GSM19, MFAM, and GSMP-35u magnetometers overall map a similar magnetic field during Test 1. A noisy behavior is observed of the GSM19 reference magnetometer around 15:43, and both sensors of the MFAM have multiple misreads (spikes) where the instruments measure incorrectly. A Gaussian distribution is found for both the GSMP-35u and the GSM19 gradients (Figure 5b), again disregarding the abnormal behavior at 15.43 for the GSM19, which adds a small right-sided tail to the histogram distribution. The corresponding histogram plots of the MFAM-MAG1 and MFAM-MAG2 sensors are skewed and—importantly—different

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