Extracting Induced and Remanent Magnetizations From Magnetic Data Modeling

Liu, Shuang; Fedi, Maurizio; Hu, Xiangyun; Baniamerian, Jamaledin; Wei, Bangshun; Zhang, Dalian; Zhu, Rixiang

doi:10.1029/2017jb015364

Cited by 21 publications

(22 citation statements)

References 62 publications

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“…The ambient magnetic field in the region has an inclination of 49.1°, declination of −5.6°, and a magnitude of 49,997 nT. A previous study by Liu et al (2018), which investigated the remanence component of the anomaly using Figure 11. Predictions from different positional changes of the interpreted source body.…”

Section: Geological Backgroundmentioning

confidence: 99%

“…In the second and third scenarios, we considered, respectively, two depths (100 and 150 m) and three depths (100, 150, and 200 m). All three scenarios and their corresponding predictions, as well as predictions in Liu et al (2018), are summarized in Table 7. The predicted inclinations are consistent over…”

Section: Inclination and Declination Prediction Using Cnnsmentioning

confidence: 99%

“…The same data set was used and made publicly available by Liu et al (2018). The area has three main anomalies named, respectively, Laitoushan (LTS) in the west, Dajingzhao (DJZ) in the center, and Xuezhuang (XZ) in the east part of the region, as shown in Figure 7 of Liu et al (2018). Here, we focus on the XZ magnetic anomaly, which is shown in Figure 12a.…”

Section: Application To Field Data: Yeshan Igneous Intrusionsmentioning

confidence: 99%

“…Comparison of our estimates of the magnetization direction withLiu et al (2018) for the Xuezhuang site. The red box represents our prediction, while the blue cross-mark represents the predicted magnetization direction byLiu et al (2018), with the blue-shaded region representing their reported range of uncertainty.10.1029/2020JB019675…”

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confidence: 99%

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Predicting Magnetization Directions Using Convolutional Neural Networks

Nurindrawati

Sun

2020

JGR Solid Earth

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Magnetic data have been widely used for understanding basin structures, mineral deposit systems, the formation history of various geological systems, and many others. Proper interpretation of magnetic data requires accurate knowledge of total magnetization directions of the source bodies in an area of study. Existing approaches for estimating magnetization directions involve either unstable data processing steps or computationally intensive processes such as 3-D inversions. In this study, we developed a new method of automatically predicting the magnetization direction of a magnetic source body using convolutional neural networks (CNNs). CNNs have achieved great success in many other applications such as computer vision and seismic image interpretation but have not been used to extract parameters from magnetic data. We simulated many magnetic data maps with different magnetization directions from a synthetic source body, all subject to the same background field. Two CNNs were trained separately, one for predicting the inclination and the other for predicting declination. We determined the optimal CNN architectures for predicting inclinations and declinations by systematically comparing 13 different CNN architectures. In addition, we investigated the effect of having different parameters such as magnetization magnitude, source body shape, location, and depth on the performance of our predictive models. We also tested the method using field data from Black Hill Norite, Australia, and Yeshan region, China, for which prior research results are available for comparison. Our study shows that machine learning provides an effective means of automatically predicting magnetization directions based on magnetic data maps.

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Section: Geological Backgroundmentioning

confidence: 99%

Section: Inclination and Declination Prediction Using Cnnsmentioning

confidence: 99%

Section: Application To Field Data: Yeshan Igneous Intrusionsmentioning

confidence: 99%

mentioning

confidence: 99%

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Predicting Magnetization Directions Using Convolutional Neural Networks

Nurindrawati

Sun

2020

JGR Solid Earth

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“…One of the main issues in mineral exploration is the precision of the data processing and interpretation. In recent years, many strategies have been proposed for the stage of high-precision and quantitative data-processing, inversion and interpretation in mineral exploration data [16][17][18][19][20][21][22] . However, the anomaly in traditional methods is the projection of the magnetic anomaly vector on the normal geomagnetic field.…”

Section: Jianhua Yang Shuang Liu * and Xiangyun Humentioning

confidence: 99%

Inversion of high-amplitude magnetic total field anomaly: an application to the Mengku iron-ore deposit, northwest China

Yang

Liu

2020

Sci Rep

Self Cite

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relative-error (the difference/the magnitude magnetic anomaly) reach 10% with spherical model. They suggest projection anomaly is not credible especially as high-precision of quantitative processing and interpretation are required and propose a method using the error yielded by the projection anomaly. Coleman and Li 29 studied the difference between the errors of the total-field anomaly and magnetic amplitude data and found that the errors in the three orthogonal components converted from the total-field anomaly had similar standard deviations. Based on the total magnetic anomaly is no longer approximately equal to projection anomaly in the highly magnetic environments, Sun et al. 30 used the information from the borehole data and structural orientation as constraints and get an acceptable result. In our study, we define the error between projection anomaly and observed modulus difference anomaly firstly and carefully discuss the error caused by amplitude, inclination and declination individually and together between the projection anomaly results (data processing, inversion and interpretation) with those of the modulus difference anomaly for strong magnetic anomalies. Second, we propose directly inverting for the modulus difference anomaly in strong magnetic bodies under Cartesian coordinate. Third, the operator, given by modulus difference anomaly, is nonlinear which is different with projection anomaly. Finally, the synthetic and field data are used to test the inversion method with two types of total field data.

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Fast Computation of Terrain-Induced Gravitational and Magnetic Effects on Arbitrary Undulating Surfaces

Chen

2023

Surv Geophys

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Based on a brief review of forward algorithms for the computation of topographic gravitational and magnetic effects, including spatial, spectral and hybrid-domain algorithms working in either Cartesian or spherical coordinate systems, we introduce a new algorithm, namely the CP-FFT algorithm, for fast computation of terrain-induced gravitational and magnetic effects on arbitrary undulating surfaces. The CP-FFT algorithm, working in the hybrid spatial-spectral domain, is based on a combination of CANDECOMP/PARAFAC (CP) tensor decomposition of gravitational integral kernels and 2D Fast Fourier Transform (FFT) evaluation of discrete convolutions. By replacing the binomial expansion in classical FFT-based terrain correction algorithms using CP decomposition, convergence of the outer-zone computation can be achieved with significantly reduced inner-zone radius. Additionally, a Gaussian quadrature mass line model is introduced to accelerate the computation of the inner zone effect. We validate our algorithm by computing the gravitational potential, the gravitational vector, the gravity gradient tensor, and magnetic fields caused by densely-sampled topographic and bathymetric digital elevation models of selected mountainous areas around the globe. Both constant and variable density/magnetization models, with computation surfaces on, above and below the topography are considered. Comparisons between our new method and space-domain rigorous solutions show that with modeling errors well below existing instrumentation error levels, the calculation speed is accelerated thousands of times in all numerical tests. We release a set of open-source code written in MATLAB language to meet the needs of geodesists and geophysicists in related fields to carry out more efficiently topographic modeling in Cartesian coordinates under planar approximation.

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Extracting Induced and Remanent Magnetizations From Magnetic Data Modeling

Cited by 21 publications

References 62 publications

Predicting Magnetization Directions Using Convolutional Neural Networks

Predicting Magnetization Directions Using Convolutional Neural Networks

Inversion of high-amplitude magnetic total field anomaly: an application to the Mengku iron-ore deposit, northwest China

Fast Computation of Terrain-Induced Gravitational and Magnetic Effects on Arbitrary Undulating Surfaces

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