SGCast: A New Forecasting Framework for Multilocation Geomagnetic Data With Missing Traces Based on Matrix Factorization

Huan, Liu; Bin, Junchi; Liu, Yihao; Dong, Haobin; Liu, Zheng; Mrad, Nezih; Blasch, Erik

doi:10.1109/tim.2021.3088459

Cited by 5 publications

(1 citation statement)

References 37 publications

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“…Various anomalies will occur prior to earthquakes, which can also be called seismic precursor [1][2][3][4]. Accurate capture of the seismic precursor will have great significance for the study of short-term earthquake prediction [5].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: An unsupervised anomaly detection approach for pre-seismic ionospheric total electron content

Wen¹,

Peng²,

Huang³

et al. 2023

Meas. Sci. Technol.

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This paper focuses on the anomaly detection for ionospheric total electron content (TEC) before earthquakes. In this paper, a novel unsupervised approach is proposed. First, interval-based method is employed to granulate the TEC series. Justiﬁable granularity principle is utilized to construct interval information granules (IGs) for representing TEC series. Second, high-order diﬀerence method is introduced to construct rectangle IGs and cube IGs for obtaining the new representation of TEC. Third, corresponding similarity measurement method is designed to calculate the anomaly score of each IG, which is the evaluation criterion for detecting the anomalies. Finally, experimental results using real TEC datasets validate the eﬀectiveness of the proposed approach. Compared with the existing major approaches, because the proposed approach can capture more morphological details and variation trend of TEC series, it can achieve a higher detection accuracy.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: An unsupervised anomaly detection approach for pre-seismic ionospheric total electron content

Wen¹,

Peng²,

Huang³

et al. 2023

Meas. Sci. Technol.

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A modular magneto-inductive sensor for low vector magnetic field measurements

Huan

Wang

Zhao

et al. 2021

Review of Scientific Instruments

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The low magnetic field measurement has been utilized since ancient times in order to find economic resources, to detect magnetic anomalies, etc. In this case, the vector magnetic survey can simultaneously obtain the modulus and direction information of the magnetic field, which can contribute to obtaining more precise information and characteristics of magnetic field resources. This paper is concerned with the potential to exploit the signals of vector magnetic field measurement with a magneto-inductive (MI) sensor. To evaluate the capability of the MI sensor, a test platform is set up and its performance, including the noise floor, the resolution, and the sensitivity, is comprehensively characterized. Furthermore, a comparative geomagnetic observation and magnetic anomaly detection among the proposed MI sensor, a high-precision Overhauser sensor, and a commonly used and accepted commercial MI sensor are conducted. The experimental results identify the capability of the proposed MI sensor in weak magnetic detection.

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Bayesian Exponential Regularized Tensor Based Approach for Sparse Geomagnetic Data Completion

Li,

Bin,

Liu

2023

2023 International Conference on Sensing, Measurement &Amp;amp; Data Analytics in the Era of Artificial Intelligence (ICSMD)

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SGCast: A New Forecasting Framework for Multilocation Geomagnetic Data With Missing Traces Based on Matrix Factorization

Cited by 5 publications

References 37 publications

RETRACTED: An unsupervised anomaly detection approach for pre-seismic ionospheric total electron content

RETRACTED: An unsupervised anomaly detection approach for pre-seismic ionospheric total electron content

A modular magneto-inductive sensor for low vector magnetic field measurements

Bayesian Exponential Regularized Tensor Based Approach for Sparse Geomagnetic Data Completion

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