Processing and Interpretation of UAV Magnetic Data: A Workflow Based on Improved Variational Mode Decomposition and Levenberg-Marquardt Algorithm

Unmanned aerial vehicles (UAVs) have become a research hotspot in the field of magnetic exploration because of their unique advantages, e.g., low cost, high safety, and easy to operate. However, the lack of effective data processing and interpretation method limits their further deployment. In view of this situation, a complete workflow of UAV magnetic data processing and interpretation is proposed in this paper, which can be divided into two steps: (1) the improved variational mode decomposition (VMD) is applied to the original data to improve its signal-to-noise ratio as much as possible, and the decomposition modes number K is determined adaptively according to the mode characteristics; (2) the parameters of target position and magnetic moment are obtained by Euler deconvolution first, and then used as the prior information of the Levenberg–Marquardt (LM) algorithm to further improve its accuracy. Experiments are carried out to verify the effectiveness of the proposed method. Results show that the proposed method can significantly improve the quality of the original data; by combining the Euler deconvolution and LM algorithm, the horizontal positioning error can be reduced from 15.31 cm to 4.05 cm, and the depth estimation error can be reduced from 16.2 cm to 5.4 cm. Moreover, the proposed method can be used not only for the detection and location of near-surface targets, but also for the follow-up work, such as the clearance of targets (e.g., the unexploded ordnance).

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Interpretation of high resolution airborne magnetic data (HRAMD) of Ilesha and its environs, Southwest Nigeria, using Euler deconvolution method

Materials and Geoenvironment ◽

10.1515/rmzmag-2017-0013 ◽

2017 ◽

Vol 64 (4) ◽

pp. 227-241

Author(s):

Oluwaseun Tolutope Olurin

Keyword(s):

High Resolution ◽

High Amplitude ◽

Sharp Contrast ◽

Magnetic Data ◽

Magnetic Intensity ◽

Euler Deconvolution ◽

Southwestern Nigeria ◽

Near Surface ◽

Low Amplitude ◽

Airborne Magnetic

AbstractInterpretation of high resolution aeromagnetic data of Ilesha and its environs within the basement complex of the geological setting of Southwestern Nigeria was carried out in the study. The study area is delimited by geographic latitudes 7°30′–8°00′N and longitudes 4°30′–5°00′E. This investigation was carried out using Euler deconvolution on filtered digitised total magnetic data (Sheet Number 243) to delineate geological structures within the area under consideration. The digitised airborne magnetic data acquired in 2009 were obtained from the archives of the Nigeria Geological Survey Agency (NGSA). The airborne magnetic data were filtered, processed and enhanced; the resultant data were subjected to qualitative and quantitative magnetic interpretation, geometry and depth weighting analyses across the study area using Euler deconvolution filter control file in Oasis Montag software. Total magnetic intensity distribution in the field ranged from –77.7 to 139.7 nT. Total magnetic field intensities reveal high-magnitude magnetic intensity values (high-amplitude anomaly) and magnetic low intensities (low-amplitude magnetic anomaly) in the area under consideration. The study area is characterised with high intensity correlated with lithological variation in the basement. The sharp contrast is enhanced due to the sharp contrast in magnetic intensity between the magnetic susceptibilities of the crystalline and sedimentary rocks. The reduced-to-equator (RTE) map is characterised by high frequencies, short wavelengths, small size, weak intensity, sharp low amplitude and nearly irregular shaped anomalies, which may due to near-surface sources, such as shallow geologic units and cultural features. Euler deconvolution solution indicates a generally undulating basement, with a depth ranging from −500 to 1000 m. The Euler deconvolution results show that the basement relief is generally gentle and flat, lying within the basement terrain.

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Simultaneous estimation of hydro-dipersive parameters using a new modified levenberg-marquardt algorithm

MATEC Web of Conferences ◽

10.1051/matecconf/201928606001 ◽

2019 ◽

Vol 286 ◽

pp. 06001

Author(s):

H. Qanza ◽

A. Maslouhi ◽

M. Hachimi ◽

A. Hmimou

Keyword(s):

Soil Water ◽

Least Squares Method ◽

Numerical Differentiation ◽

Water Dynamics ◽

Simultaneous Estimation ◽

Sensitivity Coefficients ◽

Marquardt Algorithm ◽

In Situ Experiments ◽

Levenberg Marquardt ◽

Lm Algorithm

Determination of soil hydrodynamic parameters at field scale is of great importance for modeling soil water dynamics and for agricultural water management. The direct estimation of those parameters is time-consuming and afflicted with serious uncertainties. Inverse modeling is known to get efficient technique for solving non-linear problems in hydrology. Levenberg–Marquardt (LM) algorithm is a gradient-based method, which has been widely used for solving inverse soil water flow problems. In the LM algorithm, sensitivity coefficients are mainly evaluated by numerical differentiation methods. However, sensitivity coefficients are difficult to be precisely calculated by numerical differentiation methods, if transient states and non-linearities are involved. In this paper, a new approach is proposed for sensitivity analysis using the complex variabledifferentiation method (CVDM) to estimate simultaneously the hydraulic and dispersive properties of unsaturated soil from in-situ experiments. In this approach, the sensitivity coefficients can be determined in a more accurate way than the traditional finite difference method. The results show that the new inverse analysis method in the present work has high accuracy, validity, uniqueness and higher inversion efficiency, compared with the previous least-squares method. The simulated and measured water contents and tracer concentration were generally close. Overall, it was concluded that the CVDM is a promising method to estimate hydro-dispersive parameters in the unsaturated zone.

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Levenberg-marquardt algorithm to identify the fault analysis for industrial applications

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.2.9040 ◽

2017 ◽

Vol 7 (1.2) ◽

pp. 141 ◽

Cited By ~ 1

Author(s):

P. Bhuvaneswari ◽

Ramesh G.P

Keyword(s):

Sensor Network ◽

Industrial Applications ◽

Fault Analysis ◽

Wireless Sensor ◽

Acoustic Sensor ◽

Significant Function ◽

Marquardt Algorithm ◽

Levenberg Marquardt ◽

Lm Algorithm ◽

Flux Sensor

The data are collected and forwarding it to the goal is a significant function of a sensor network. For some applications, it is additionally imperative to admit the fault signal to the collected data. To monitor the industrial environment through a wireless sensor network (WSNs), present a neural network based Levenberg-Marquardt (LM) Algorithm for detecting the fault using the gradient value and mean square error of the signal. The data are collected and presented by the magnetic flux sensor and MEMS acoustic sensor. The simulation model is developed in MATLAB/Simulink.

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Tertiary extension in the central British Columbia Intermontane Belt: magnetic and paleomagnetic evidence from the Endako region

Canadian Journal of Earth Sciences ◽

10.1139/e00-070 ◽

2001 ◽

Vol 38 (4) ◽

pp. 657-678 ◽

Cited By ~ 6

Author(s):

Carmel Lowe ◽

Randolph J Enkin ◽

Lambertus C Struik

Keyword(s):

British Columbia ◽

Strike Slip ◽

Paleomagnetic Data ◽

Magnetic Data ◽

Euler Deconvolution ◽

The West ◽

Near Surface ◽

Dextral Strike Slip Fault ◽

Slip Displacement ◽

Dextral Strike Slip

New magnetic and paleomagnetic data for central British Columbia support and quantify the hypothesis that the area underwent significant Tertiary-age transtensional deformation. Paleomagnetically determined tilts in Eocene rocks indicate that four fault-bounded pits, which constitute the Endako molybdenum mine, were displaced on a series of normal (probably listric) faults that have separations of less than a kilometre. The interpretation also suggests there can be little vertical offset on the Denak West Fault, which separates the Denak East and Denak West pits. Regional paleomagnetic data indicate a predominance of easterly directed tilts to the east of the Casey Fault, but to the west a large variation in the orientation and magnitude of tilts is observed. Results at one site proximal to the Casey Fault indicate a component of dip-slip displacement on this dominantly dextral strike-slip fault. Mapped northeast- and northwest-trending faults commonly correspond to linear zones of steep magnetic gradient and near-surface magnetic sources. Several additional northwest- and northeast-trending lineaments are imaged in the magnetic data where no faults are mapped (particularly over massive and lithologically homogeneous phases of the Endako batholith). Euler deconvolution solutions confirm most such lineaments are also associated with shallow magnetic sources. In profile, they have either a fault or dyke character and are interpreted to be unmapped faults, some locally intruded by mafic dykes, which cut the region into a series of fault-bounded blocks.

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Adaptive Levenberg–Marquardt Algorithm: A New Optimization Strategy for Levenberg–Marquardt Neural Networks

Mathematics ◽

10.3390/math9172176 ◽

2021 ◽

Vol 9 (17) ◽

pp. 2176

Author(s):

Zhiqi Yan ◽

Shisheng Zhong ◽

Lin Lin ◽

Zhiquan Cui

Keyword(s):

Neural Network ◽

Neural Networks ◽

Frequency Noise ◽

Optimization Strategy ◽

Engineering Data ◽

Marquardt Algorithm ◽

Highly Nonlinear ◽

Rectified Linear Unit ◽

Levenberg Marquardt ◽

Lm Algorithm

Engineering data are often highly nonlinear and contain high-frequency noise, so the Levenberg–Marquardt (LM) algorithm may not converge when a neural network optimized by the algorithm is trained with engineering data. In this work, we analyzed the reasons for the LM neural network’s poor convergence commonly associated with the LM algorithm. Specifically, the effects of different activation functions such as Sigmoid, Tanh, Rectified Linear Unit (RELU) and Parametric Rectified Linear Unit (PRLU) were evaluated on the general performance of LM neural networks, and special values of LM neural network parameters were found that could make the LM algorithm converge poorly. We proposed an adaptive LM (AdaLM) algorithm to solve the problem of the LM algorithm. The algorithm coordinates the descent direction and the descent step by the iteration number, which can prevent falling into the local minimum value and avoid the influence of the parameter state of LM neural networks. We compared the AdaLM algorithm with the traditional LM algorithm and its variants in terms of accuracy and speed in the context of testing common datasets and aero-engine data, and the results verified the effectiveness of the AdaLM algorithm.

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Modification of Levenberg-Marquardt Algorithm for Solve Two Dimension Partial Differential Equation

Journal of University of Babylon for Pure and Applied Sciences ◽

10.29196/jubpas.v26i7.1417 ◽

2018 ◽

Vol 26 (7) ◽

pp. 107-117

Author(s):

Khalid Mindeel M. Al-Abrahemee ◽

Rana T. Shwayaa

Keyword(s):

Neural Network ◽

Differential Equation ◽

Partial Differential Equation ◽

Partial Differential Equations ◽

Partial Differential ◽

Two Dimension ◽

Marquardt Algorithm ◽

Levenberg Marquardt ◽

In The Beginning ◽

Lm Algorithm

In this paper we presented a new way based on neural network has been developed for solutione of two dimension partial differential equations . A modified neural network use to over passing the Disadvantages of LM algorithm, in the beginning we suggest signaler value decompositions of Jacobin matrix (J) and inverse of Jacobin matrix( J-1), if a matrix rectangular or singular Secondly, we suggest new calculation of μk , that ismk=|| E (w)||2 look the nonlinear execution equations E(w) = 0 has not empty solution W* and we refer to the second norm in all cases ,whereE(w): is continuously differentiable and E(x) is Lipeschitz continuous, that is=|| E(w 2)- E(w 1)||£ L|| w 2- w 1|| ,where L is Lipeschitz constant.

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Integrated Sensor System for Rock Mass Underground Deformation Measurement and Data Analysis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.336-338.295 ◽

2013 ◽

Vol 336-338 ◽

pp. 295-302 ◽

Cited By ~ 1

Author(s):

Yang Ming Xie ◽

Qing Li ◽

Guo Qing Jiang

Keyword(s):

Experimental Data ◽

Rock Mass ◽

Data Analysis ◽

Data Processing ◽

Real Time ◽

Sensor System ◽

Integrated Sensor ◽

Monitoring Process ◽

Marquardt Algorithm ◽

Levenberg Marquardt

In order to thoroughly reflect the underground deformation of rock mass, in this article, a sensor system which study on the landslide is invented and the reliable fitting formula based on the experimental data is produced. In first part, we briefly introduce the fundamental principles and measuringways of the instrument, then describe the whole effective monitoring process, and in the data processing, finally obtain the efficacious fitting formula by analyzing basic steps of Levenberg-Marquardt algorithm and utilizing this algorithm to fit experimental data. The experiment demonstrates that the real-time underground displacement measurement is practical and can be applied to analyze early deformation of rock mass and warn the unstable situation.

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

Remote Sensing ◽

10.3390/rs12030452 ◽

2020 ◽

Vol 12 (3) ◽

pp. 452 ◽

Cited By ~ 1

Author(s):

Yaxin Mu ◽

Xiaojuan Zhang ◽

Wupeng Xie ◽

Yaoxin Zheng

Keyword(s):

Unmanned Aerial Vehicle ◽

Large Scale ◽

Magnetic Measurement ◽

Sliding Window ◽

Magnetic Data ◽

Magnetic Survey ◽

Interference Field ◽

Euler Deconvolution ◽

Near Surface ◽

Aerial Vehicle

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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Improving Levenberg-Marquardt Algorithm Inversion Result Using Singular Value Decomposition

Earth Science Research ◽

10.5539/esr.v5n2p20 ◽

2016 ◽

Vol 5 (2) ◽

pp. 20

Author(s):

Widodo Widodo ◽

Durra Handri Saputera

Keyword(s):

Singular Value Decomposition ◽

Singular Value ◽

Parameter Analysis ◽

Model Parameters ◽

Inversion Result ◽

One Dimensional ◽

Marquardt Algorithm ◽

Levenberg Marquardt ◽

Value Decomposition ◽

Lm Algorithm

Inversion is a process to determine model parameters from data. In geophysics this process is very important because subsurface image is obtained from this process. There are many inversion algorithms that have been introduced and applied in geophysics problems; one of them is Levenberg-Marquardt (LM) algorithm. In this paper we will present one of LM algorithm application in one-dimensional magnetotelluric (MT) case. The LM algorithm used in this study is improved version of LM algorithm using singular value decomposition (SVD). The result from this algorithm is then compared with the algorithm without SVD in order to understand how much it has been improved. To simplify the comparison, simple synthetic model is used in this study. From this study, the new algorithm can improve the result of the original LM algorithm. In addition, SVD is allowing more parameter analysis to be done in its process. The algorithm created from this study is then used in our modeling program, called MAT1DMT.

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