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unmanned aerial vehicles

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Journal Article
Journal: The Leading Edge
Published: 01 August 2024
The Leading Edge (2024) 43 (8): 494–505.
... imagery to design a detailed acquisition plan. This paper presents a concept that utilizes unmanned aerial vehicles (UAVs) to successfully and sustainably perform on-demand high-resolution areal scouting to aid near-surface geoscience data acquisition campaigns and seismic drone deployment. We share...
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Journal Article
Published: 01 February 2017
Journal of Sedimentary Research (2017) 87 (2): 126–132.
...Nora M. Nieminski; Stephan A. Graham Abstract: The ability to view and characterize outcrops that are difficult to study from the ground is greatly improved by aerial investigation. We describe the application of flying a small, unmanned aerial vehicle (UAV) to collect photographic data...
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Journal Article
Journal: Geophysics
Published: 29 September 2021
Geophysics (2021) 86 (6): J21–J32.
...Callum Walter; Alexander Braun; Georgia Fotopoulos ABSTRACT The development of a functional unmanned aerial vehicle (UAV) mounted aeromagnetic system requires integrating a magnetometer payload onboard a UAV platform in a manner that preserves the integrity of the total magnetic field measurements...
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Journal Article
Journal: Geophysics
Published: 18 February 2025
Geophysics (2025) 90 (2): P1–P12.
...Zhenning Ma; Rongyi Qian; Yu Zhao; Yinhu Huang; Zhiyong Wu; Jun Zhang; Xu Liu ABSTRACT An unmanned aerial vehicle (UAV) seismic source, suitable for complex terrains due to its eco-friendly, cost-effective, and efficient nature, operates by hovering and releasing impact objects to generate seismic...
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Journal Article
Journal: Geosphere
Published: 04 November 2022
Geosphere (2022) 18 (6): 1958–1973.
...Kathryn M. Bateman; Randolph T. Williams; Thomas F. Shipley; Basil Tikoff; Terry Pavlis; Cristina G. Wilson; Michele L. Cooke; Ake Fagereng Abstract Field geologists are increasingly using unmanned aerial vehicles (UAVs or drones), although their use involves significant cognitive challenges...
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Journal Article
Published: 08 November 2019
Environmental & Engineering Geoscience (2019) 25 (4): 301–317.
...Jordan A. Carey; Nicholas Pinter; Alexandra J. Pickering; Carol S. Prentice; Stephen B. Delong ABSTRACT The combination of unmanned aerial vehicle (UAV) photography with structure-from-motion (SfM) digital photogrammetry provides a quickly deployable and cost-effective method for monitoring...
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Journal Article
Journal: Geophysics
Published: 10 June 2021
Geophysics (2021) 86 (4): R399–R412.
... an exploration system, facilitating the acquisition in these areas by delivering the receivers from the sky using unmanned aerial vehicles. Air dropping of the sensors in vegetated areas results in an irregular geometry for the acquisition. This irregularity can limit the application of conventional surface wave...
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Journal Article
Journal: AAPG Bulletin
Published: 01 January 2025
AAPG Bulletin (2025) 109 (1): 35–56.
... the geometric dimensions of karst dissolution features on the surface and underground, using three-dimensional unmanned aerial vehicle and ground-penetrating radar data sets. We first confirmed that ellipsoids can effectively approximate karst dissolution features. Subsequently, we demonstrated...
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Journal Article
Published: 11 August 2023
Seismological Research Letters (2023) 94 (6): 2627–2641.
... by the northeast‐striking fault belt, but the long axis of the isoseismic line, distribution of early aftershocks and coseismic rupture plane all strike northwest, posing challenges to the seismogenic mechanism. To investigate this, we conducted a 400 km 2 unmanned aerial vehicle (UAV) aeromagnetic survey near...
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Journal Article
Published: 01 August 2024
Jour. Geol. Soc. India (2024) 100 (8): 1208–1209.
... with a Magnetic Sensor for the first time in India for Mineral Exploration using an Unmanned Aerial System. Unmanned Aerial Vehicle/Drone-based Magnetic Surveys (DMS) are an innovative method used in geophysical exploration to map subsurface magnetic properties. They offer several advantages over traditional...
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Journal Article
Published: 01 May 2023
Earthquake Spectra (2023) 39 (2): 962–984.
... using unmanned aerial vehicle (UAV) remote sensing systems offer great flexibility and high efficiency with the capability to obtain high-resolution images, which can reflect actual damage to affected areas intuitively. Consequently, UAV remote sensing has become a convenient and important means...
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Journal Article
Journal: Interpretation
Published: 26 October 2018
Interpretation (2018) 6 (4): T1117–T1139.
... and petrophysical heterogeneity on reservoir performance, conventional field methods are combined with unmanned aerial vehicle-based photogrammetry to create representative outcrop-based reservoir models. Outcrop reservoir models and fluid-flow simulations compare three reservoir scenarios of the Burro Canyon...
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(a) Customised Octacopter Drone-Unmanned Aerial Vehicle (UAV) from Marut. (b) Geometric-Magarrow Cesium Vapour Magnetometer. (c) UAV-based magnetic data acquisition in Sidhan Mn-Fe block (30 m AGL, 50 m spacing). (d) Flight planning in Sidhan Mn-Fe block using Mission Planner Software
Published: 01 August 2024
Fig. 2. (a) Customised Octacopter Drone-Unmanned Aerial Vehicle (UAV) from Marut. (b) Geometric-Magarrow Cesium Vapour Magnetometer. (c) UAV-based magnetic data acquisition in Sidhan Mn-Fe block (30 m AGL, 50 m spacing). (d) Flight planning in Sidhan Mn-Fe block using Mission Planner
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Unmanned aerial vehicle–based photogrammetric model and field photographs of the study area in the Bighorn Basin, Wyoming, USA (see Fig. 1). (A) Locations of field-documented sandstones in photogrammetric panels. The red dashed line indicates how the composite section of Figure 3F was constructed. Trenched sections (white bars) include Deer Creek Amphitheater (DCA), Purple Butte (PB), Upper Deer Creek (UDC), and Creek Star Hill (CSH). (B) Field photograph showing the floodplain-rich nature of the studied stratigraphy. (C) Field photograph comparing sinuous and braided channel deposits viewed from a distance. Locations of panels B and C are approximated in panel A.
Published: 02 May 2024
Figure 2. Unmanned aerial vehicle–based photogrammetric model and field photographs of the study area in the Bighorn Basin, Wyoming, USA (see Fig. 1 ). (A) Locations of field-documented sandstones in photogrammetric panels. The red dashed line indicates how the composite section of Figure 3F
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Aerial view of the landslide taken by an unmanned aerial vehicle (view is from the toe of the slide, towards the source area).
Published: 04 May 2017
Fig. 3. Aerial view of the landslide taken by an unmanned aerial vehicle (view is from the toe of the slide, towards the source area).
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Geological structures, earthquakes, unmanned aerial vehicle (UAV) aeromagnetic survey area, and shale gas extraction platforms near the Ms 6.0 earthquake epicenter in Luxian. The relocated earthquake data (including foreshocks and aftershocks that occurred between 1 January 2020 and 22 October 2021) were provided by L. H. Fang and X. W. Li (Li et al., 2022). The inset map in the upper left corner shows the topography of the larger region, with historical earthquakes (≥M 3 since 1970, from the China Earthquake Networks Center [CENC]) indicated by dots. The color version of this figure is available only in the electronic edition.
Published: 11 August 2023
Figure 1. Geological structures, earthquakes, unmanned aerial vehicle (UAV) aeromagnetic survey area, and shale gas extraction platforms near the M s  6.0 earthquake epicenter in Luxian. The relocated earthquake data (including foreshocks and aftershocks that occurred between 1 January
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(a) Unmanned aerial vehicle photo of a retrogressive landslide scar along the Göta River, in the study area, marked using the yellow line (see also Figure 1). (b) Wireless receivers and 3C-MEMs-based landstreamer nearly collocated (approximately 1 m parallel) and spaced 1 m from each other used to acquire the data set presented in this study. (c) The 5 kg sledgehammer vertical impact used as the seismic source and shot locations placed along the receiver line (collocated with the receivers). This study mainly focuses on retrieving shear-wave reflections from the wireless data that were connected to vertical-component geophones.
Published: 03 April 2023
Figure 2. (a) Unmanned aerial vehicle photo of a retrogressive landslide scar along the Göta River, in the study area, marked using the yellow line (see also Figure  1 ). (b) Wireless receivers and 3C-MEMs-based landstreamer nearly collocated (approximately 1 m parallel) and spaced 1 m from each
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Histogram illustrating exponential rise of unmanned aerial vehicle (UAV) use in geological sciences. Data are the result of a disciplinary ISI Web of Science search in geoscience journals utilizing the words “UAV” or “drones” from 1999 to 2020.
Published: 04 November 2022
Figure 1. Histogram illustrating exponential rise of unmanned aerial vehicle (UAV) use in geological sciences. Data are the result of a disciplinary ISI Web of Science search in geoscience journals utilizing the words “UAV” or “drones” from 1999 to 2020.
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(Top) Unmanned aerial vehicle (UAV) flight path (yellow) shown on oblique satellite imagery as an expert geologist traverses perpendicular to the strike of two exposed faults (Skeleton Canyon fault “SCF” and an unnamed fault in white) near their inferred intersection. Satellite imagery, map data: Google, Landsat/Copernicus. (Bottom) Image of the two target faults as observed using the UAV’s onboard camera/video feed. Thickness of the unnamed fault is ~10 m; 33°36’43.04”N, 116°01’36.10” W.
Published: 04 November 2022
Figure 4. (Top) Unmanned aerial vehicle (UAV) flight path (yellow) shown on oblique satellite imagery as an expert geologist traverses perpendicular to the strike of two exposed faults (Skeleton Canyon fault “SCF” and an unnamed fault in white) near their inferred intersection. Satellite imagery
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(Top) Unmanned aerial vehicle (UAV) flight path (yellow) shown on oblique satellite imagery illustrating an expert geologist using the UAV to gain elevation to better observe the relationship between two faults (white; Painted Canyon fault “PCF”). Satellite imagery, map data: Google, Landsat/Copernicus). (Bottom) Image of two intersecting faults as observed using the UAV’s onboard camera/video feed. In the upper panel, the height of the cliff face in the foreground is ~220 m. In the lower panel, the maximum thickness of Mecca Formation in the image is ~40 m; 33°37’03.03” N, 115°59’57.00” W.
Published: 04 November 2022
Figure 5. (Top) Unmanned aerial vehicle (UAV) flight path (yellow) shown on oblique satellite imagery illustrating an expert geologist using the UAV to gain elevation to better observe the relationship between two faults (white; Painted Canyon fault “PCF”). Satellite imagery, map data: Google