An Elaborately Designed Virtual Frame to Level Aeromagnetic Data

Fan, Zhenfeng; Huang, Ling; Zhang, Xiaojuan; Fang, Guangyou

doi:10.1109/lgrs.2016.2574750

Cited by 10 publications

(5 citation statements)

References 16 publications

(14 reference statements)

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“…The raw magnetic data was collected continuously by two magnetometers ( = 160 Hz), and the 2D magnetic contour map is produced by the Kriging interpolation in Figure 9. Four features are observed from the magnetic maps of raw data: 1) The measurement inconsistency between adjacent lines results in leveling errors [38], which is related to the attitude information of the drone; 2) A subtle linear trend exists along the profile from south to north; 3) The noise signal measured by magnetometer 1 is higher than the noise measured by magnetometer 2; 4) Only two magnetic anomalies are visible, other magnetic anomalies are masked. A lowpass filter with = 3 Hz was used to eliminate the high-frequency interference signal firstly, then the trend term was removed to subtract the regional geomagnetic field, the processed results are displayed in Figure 10.…”

Section: Resultsmentioning

confidence: 99%

Automatic Detection of Near-Surface Targets for Unmanned Aerial Vehicle (UAV) Magnetic Survey

Zhang

Xie

et al. 2020

Remote Sensing

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Great progress has been made in the integration of Unmanned Aerial Vehicle (UAV) magnetic measurement systems, but the interpretation of UAV magnetic data is facing serious challenges. This paper presents a complete workflow for the detection of the subsurface objects, like Unexploded Ordnance (UXO), by the UAV-borne magnetic survey. The elimination of interference field generated by the drone and an improved Euler deconvolution are emphasized. The quality of UAV magnetic data is limited by the UAV interference field. A compensation method based on the signal correlation is proposed to remove the UAV interference field, which lays the foundation for the subsequent interpretation of UAV magnetic data. An improved Euler deconvolution is developed to estimate the location of underground targets automatically, which is the combination of YOLOv3 (You Only Look Once version 3) and Euler deconvolution. YOLOv3 is a deep convolutional neural network (DCNN)-based image and video detector and it is applied in the context of magnetic survey for the first time, replacing the traditional sliding window. The improved algorithm is more satisfactory for the large-scale UAV-borne magnetic survey because of the simpler and faster workflow, compared with the traditional sliding window (SW)-based Euler method. The field test is conducted and the experimental results show that all procedures in the designed routine is reasonable and effective. The UAV interference field is suppressed significantly with root mean square error 0.5391 nT and the improved Euler deconvolution outperforms the SW Euler deconvolution in terms of positioning accuracy and reducing false targets.

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Section: Resultsmentioning

confidence: 99%

Automatic Detection of Near-Surface Targets for Unmanned Aerial Vehicle (UAV) Magnetic Survey

Zhang

Xie

et al. 2020

Remote Sensing

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“…It is hard to separate the leveling errors from differences. Furthermore, virtual tie lines (Huang and Fraser, 1999;Fan et al, 2016;Zhang et al, 2018) are skillfully constructed to level geophysical data instead of tie lines.…”

Section: Tie-line Leveling Methodsmentioning

confidence: 99%

Airborne electromagnetic data leveling based on structured variational method

Zhang,

Chen,

et al. 2024

Preprint

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Abstract. The leveling errors are defined as the data difference among flight lines in airborne geophysical data. The differences of the signal leveling always show as a striping pattern parallel to the flight lines on the imaged maps. The fixed structured pattern inspires us to structure a guided leveling error model by an anisotropic Gabor filter. Then we embed the leveling error model in total variational framework to flexibly calculate leveling errors. The guided leveling error model constrain the noise term of total variation rather than just blind removal. Moreover, the structured variational method can be extended to remove other type of noises which have general noise priors. We have applied the method to the airborne electromagnetic, magnetic data, and apparent conductivity data collected by Ontario Geological Survey to confirm its validity and robustness by comparing the results with the published data. The structured variational method can better level airborne geophysical data based on the space properties of leveling error.

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“…Then, other geophysicists focused on using long-wavelength components to level airborne magnetic data (Luo et al, 2012;White and Beamish, 2015). Furthermore, virtual tie lines (Huang and Fraser, 1999;Zhang et al, 2018) and cross-line frame (Fan et al, 2016) are skillfully constructed to level geophysical data instead of tie lines.…”

Section: Introductionmentioning

confidence: 99%

Leveling airborne geophysical data using a unidirectional variational model

Zhang

Sun

Yan

et al. 2022

Geosci. Instrum. Method. Data Syst.

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Abstract. Airborne geophysical data leveling is an indispensable step in conventional data processing. Traditional data leveling methods mainly explore the leveling error properties in the time and frequency domain. A new technique is proposed to level airborne geophysical data in view of the image space properties of the leveling error, including directional distribution property and amplitude variety property. This work applied a unidirectional variational model to all the survey data based on the gradient difference between the leveling errors in flight line direction and the tie-line direction. Then, a spatially adaptive multi-scale model is introduced to iteratively decompose the leveling errors which effectively avoid the difficulty in parameter selection. Considering that anomaly data with large amplitude may hide the real data level, a leveling preprocessing method is given to construct a smooth field based on the gradient data. The leveling method can automatically extract the leveling errors of the entire survey area simultaneously without the participation of staff members or tie-line control. We have applied the method to the airborne electromagnetic and magnetic data and apparent-conductivity data collected by the Ontario Geological Survey to confirm its validity and robustness by comparing the results with the published data.

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An Elaborately Designed Virtual Frame to Level Aeromagnetic Data

Cited by 10 publications

References 16 publications

Automatic Detection of Near-Surface Targets for Unmanned Aerial Vehicle (UAV) Magnetic Survey

Automatic Detection of Near-Surface Targets for Unmanned Aerial Vehicle (UAV) Magnetic Survey

Airborne electromagnetic data leveling based on structured variational method

Leveling airborne geophysical data using a unidirectional variational model

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