Magnetic object recognition with magnetic gradient tensor system heading-line surveys based on kernel extreme learning machine and sparrow search algorithm

Li, Qingzhu; Li, Zhining; Shi, Zhao; Fan, Hongbo

doi:10.1016/j.measurement.2022.111967

Cited by 8 publications

(1 citation statement)

References 27 publications

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“…Li et al [48] employed two invariants, normalized source strength (NSS) and tensor contraction (TC), to enhance the tilt angle, and they utilized the self-adaptive fuzzy c-means (SAFCM) clustering algorithm to locate multiple magnetic sources. Li et al [49] processed the magnetic gradient tensor data using the Kernel Extreme Learning Machine (KELM) and Sparrow search algorithms. Deng et al [50] inverted the magnetic gradient tensor using a convolutional neural network (CNN).…”

Section: Introductionmentioning

confidence: 99%

A Novel Method of Magnetic Sources Edge Detection Based on Gradient Tensor

Lv,

Huang,

Yang

et al. 2024

Minerals

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The edge detection method based on the magnetic gradient tensor data plays an important role in magnetic exploration because it is free from geomagnetic interference and contains more abundant information. This paper proposes a new anomaly edge detection method using the magnetic gradient tensor components. The model is established to compare with other methods, such as directional total horizontal derivative (THDz), analytical signal (AS), tilt angle, theta map, and so on, under conditions of vertical magnetization, oblique magnetization, and noise interference. Through the study of the anomaly distribution of the rectangular model, it is observed that the edge detection method proposed in this paper is nearly impervious to noise interference, exhibits strong anti-interference capabilities, delivers a high-quality boundary identification effect, and provides greater accuracy in anomaly edges with minimal error. When multiple anomalous bodies are present, the edge detection results are less susceptible to interference from each other, resulting in higher resolution. The efficiency of the algorithm is demonstrated by real magnetic data from some study areas in Jiangxi Province, China. The experimental results show that the proposed method is more precise and accurate than the total horizontal derivative, analytical signal, tilt angle, and theta map methods.

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Section: Introductionmentioning

confidence: 99%