Mirco-earthquake source depth detection using machine learning techniques

Yang, Dehe; Zhou, Xin; Wang, Xiuying; Huang, Jianping

doi:10.1016/j.ins.2020.07.045

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…The main ideological innovation of this paper is to transform the identification procedure of seismic events into a binary classification problem between earthquakes and microtremors. The identification of seismic events is a very complex problem, and is made especially challenging due to a low SNR caused by the monitoring situation interfering with natural and anthropogenic seismicity activity, which can partially coincide in terms of magnitude, space, and time (Yang et al, 2021). However, ML methods can transform this issue into a binary classification problem.…”

Section: Idea Of Transformationmentioning

confidence: 99%

“…Recently, artificial intelligence (AI) and methods such as machine learning (ML) and deep learning (DL) have become increasingly popular. Their successful application in several geophysical contexts has demonstrated great potential, including the identification of seismic events (Kong et al, 2016;Li et al, 2018;Ross et al, 2018;Mousavi et al, 2020;Yang et al, 2021). Compared with traditional earthquake detection algorithms, ML can extract features that are more closely related to the essence of the data.…”

Section: Introductionmentioning

confidence: 99%

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Seismic Event Identification Based on a Generative Adversarial Network and Support Vector Machine

Liu

Song

2022

Front. Earth Sci.

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Identifying appropriate seismic events is the primary precondition for conducting meaningful analysis in seismological research. The successful creation of a method to automatically identify earthquakes from large amounts of data has become increasingly vital, especially with the construction of seismic stations, the collection of extensive seismic data, and the development of earthquake early warning (EEW) systems. To accurately identify seismic events, a combined model based on a generative adversarial network (GAN) and a support vector machine (SVM) is proposed to distinguish between earthquakes and microtremors. We first use 52,537 strong ground motion records from Japan to train a GAN and extract the characteristics of P waves and then use an SVM to discriminate seismic events in the testing set, thereby transforming the complex seismic event identification into a simpler binary classification of earthquakes and microtremors. The results illustrate that the combined model can achieve accuracies of 99.74% for P waves and 99.93% for microtremors, which represents an increase in accuracy of 14.13% compared with the traditional short-term averaging/long-term averaging (STA/LTA) method. Additionally, 98% of the local seismic events in the Great East Japan earthquake were identified. Therefore, the combined model has a wide range of applications in EEW and earthquake monitoring.

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Section: Idea Of Transformationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Seismic Event Identification Based on a Generative Adversarial Network and Support Vector Machine

Liu

Song

2022

Front. Earth Sci.

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“…Time series is a sequence of data points in a time domain, representing various fields of temporal data objects, such as financial recordings, psychological signals, and weather readings. In the current digital era, time-series data analysis has been widely used across domains, including forecasting [1,2], prediction [3,4,5], environmental monitoring [6,7], and fault diagnosis [8]. Over the past decades, time series classification (TSC) has become an essential task of timeseries data analysis, and hundreds of methods have been proposed to classify time-series data accurately [9,10,11].…”

Section: Introductionmentioning

confidence: 99%

Meta-Feature Fusion for Few-Shot Time Series Classification

2023

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Deep learning has been widely adopted for end-to-end time-series classification (TSC). However, the effectiveness of deep learning heavily relies on large-scale data. Thus, deep learning is prone to overfit when only few labeled samples are available. Few-shot learning (FSL) aims to address this issue by learning to generalize to new tasks with few training samples (e.g., one or five samples per class). FSL considers learning good representations crucial to classify accurately using discriminative features. In this study, we propose a framework for few-shot TSC that encodes a time series as different types of images (i.e., Recurrence plot, Markov transition field, and Gramian angular summation/difference field) and train these images to the model using the FSL procedure. Different features of each image enable the model to learn rich information. In addition, we propose temporal-context attention (TCA) and meta-feature fusion (MFF) to maximize the representation ability of these images. TCA incorporates global context of the feature map and highlights pixels having informative relevance with other pixels. After extracting features, MFF refines each feature using different kernels generated based on cross-modality features and fuses the refined features. Finally, the test samples are classified to the nearest class prototype in the embedding space. All experiments are conducted on various N-way K-shot problems. Our framework outperforms state-of-the-art models on 28 standard datasets in the UCR (University of California, Riverside) archive, which is a widely used benchmark dataset in time series classification, from 0.34% up to 29.4%.

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