Rapid classification of local seismic events using machine learning

Jia, Luozhao; Chen, Hongfeng; Xing, Kang

doi:10.1007/s10950-022-10109-5

Cited by 7 publications

(3 citation statements)

References 34 publications

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“…We compare the proposed method to several state-of-theart networks, including CapsNet [1], CNN [10], VGG [11], ResNet [11], GoogleNet [11], CNN [12], and CNN [13] with the same training and testing set. Table 1 shows the results of the GA-Net comparison with the benchmark networks.…”

Section: Testing Resultsmentioning

confidence: 99%

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A Ghost-Attention Network for Discriminating Tectonic and Non-Tectonic Events on a Small and Imbalanced Dataset

Liu,

Ren,

Chen

et al. 2024

IEEE Access

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Discrimination between tectonic and non-tectonic events is crucial to assess seismic hazards and manage associated risks. However, the discrimination process is challenging due to the imbalanced distribution of tectonic and non-tectonic events. In this paper, we propose a ghost-attention network (GA-Net) consisting of multiple ghost modules and convolutional block attention modules (CBAMs) to solve this problem. Ghost module allows the network to extract feature maps using cost effective operations, which are suitable for small and unbalanced training sets. CBAM emphasizes meaningful features along channel and spatial axes, effectively learning which information to emphasize or suppress. We train the proposed GA-Net using seismic data from Shaanxi Province in China. The evaluation shows that on the test set, the proposed GA-Net achieves sensitivity, specificity, and accuracy of 96.40%, 93.24%, and 95.75%, respectively. In addition, a comparative analysis with several state-of-the-art networks establishes its superiority. Our GA-Net exhibits robust generalized performance on several training sets with different imbalanced levels and an independent dataset.

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Section: Testing Resultsmentioning

confidence: 99%

“…Sensitivity Specificity ACC Paras CapsNet [1] 90.31% 80.43% 88.28% 40.32K CNN [10] 93.08% 90.04% 92.45% 1.36M VGG [11] 93.08% 66.19% 87.55% 11.37K ResNet [11] 98.61% 90.34% 96.85% 7.13M GoogleNet [11] 99.08% 91.10% 97.44% 5.12M CNN [12] 94.74% 89.68% 93.70% 5.20M CNN [13] 91…”

Section: Methodsmentioning

confidence: 99%

A Ghost-Attention Network for Discriminating Tectonic and Non-Tectonic Events on a Small and Imbalanced Dataset

Liu,

Ren,

Chen

et al. 2024

IEEE Access

Self Cite

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“…Applications in distributed acoustic sensing, which require efficient processing of large amounts of data, have also been reported (Hernandez et al 2022). Researchers have used simple Convolutional Neural Network (CNN) models (Zhang et al 2020a;Majstorović et al 2021) and the latest architecture/modules, such as Recurrent Neural Network (RNN) (Mousavi et al 2019c), attention (Hou et al 2023), transformer , multi-feature fusion networks (Kim et al 2021a), graph-partitioning based CNN (Yano et al 2021), Capsule Neural Network , Residual Neural Network (ResNet) (Li et al 2022b) and inceptions (Jia et al 2022), contributing to improved performance. Moreover, some studies have aimed to enhance the interpretability of DL, often treated as black boxes (Kong et al 2022;Majstorović et al 2022).…”

Section: Event Detection/classification and Arrival Time Pickingmentioning

confidence: 99%

Recent advances in earthquake seismology using machine learning

Kubo,

Naoi,

Kano

2024

Earth Planets Space

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Given the recent developments in machine-learning technology, its application has rapidly progressed in various fields of earthquake seismology, achieving great success. Here, we review the recent advances, focusing on catalog development, seismicity analysis, ground-motion prediction, and crustal deformation analysis. First, we explore studies on the development of earthquake catalogs, including their elemental processes such as event detection/classification, arrival time picking, similar waveform searching, focal mechanism analysis, and paleoseismic record analysis. We then introduce studies related to earthquake risk evaluation and seismicity analysis. Additionally, we review studies on ground-motion prediction, which are categorized into four groups depending on whether the output is ground-motion intensity or ground-motion time series and the input is features (individual measurable properties) or time series. We discuss the effect of imbalanced ground-motion data on machine-learning models and the approaches taken to address the problem. Finally, we summarize the analysis of geodetic data related to crustal deformation, focusing on clustering analysis and detection of geodetic signals caused by seismic/aseismic phenomena. Graphical Abstract

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