DeepQuake — An application of CNN for seismo-acoustic event classification in The Netherlands

Trani, Luca; Pagani, Giuliano Andrea; Zanetti, João Paulo Pereira; Chapeland, Camille; Evers, Läslo

doi:10.1016/j.cageo.2021.104980

Cited by 21 publications

(15 citation statements)

References 33 publications

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“…in this problem (Linville et al, 2019;Trani et al, 2021). At local scales, data from the Mount Saint Helens nodal array and from a dense network in Wyoming have been used to explore the use of classical P/S amplitude ratios for explosion discrimination (O'Rourke et al, 2016;R.…”

Section: Forensic Seismologymentioning

confidence: 99%

“…A critical part of the workflow is the discrimination between explosions and earthquakes (Richards & Kim, 2009). With growing time‐histories of data and associated event catalogs, recent work using data from regional networks in Utah and the Netherlands has demonstrated that machine learning can play an increasingly important role in this problem (Linville et al., 2019; Trani et al., 2021). At local scales, data from the Mount Saint Helens nodal array and from a dense network in Wyoming have been used to explore the use of classical P / S amplitude ratios for explosion discrimination (O’Rourke et al., 2016; R. Wang et al., 2020).…”

Section: Science Enabled By Big Data Seismologymentioning

confidence: 99%

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Big Data Seismology

Arrowsmith

Trugman

MacCarthy

et al. 2022

Reviews of Geophysics

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The discipline of seismology is based on observations of ground motion that are inherently undersampled in space and time. Our basic understanding of earthquake processes and our ability to resolve 4D Earth structure are fundamentally limited by data volume. Today, Big Data Seismology is an emergent revolution involving the use of large, data‐dense inquiries that is providing new opportunities to make fundamental advances in these areas. This article reviews recent scientific advances enabled by Big Data Seismology through the context of three major drivers: the development of new data‐dense sensor systems, improvements in computing, and the development of new types of techniques and algorithms. Each driver is explored in the context of both global and exploration seismology, alongside collaborative opportunities that combine the features of long‐duration data collections (common to global seismology) with dense networks of sensors (common to exploration seismology). The review explores some of the unique challenges and opportunities that Big Data Seismology presents, drawing on parallels from other fields facing similar issues. Finally, recent scientific findings enabled by dense seismic data sets are discussed, and we assess the opportunities for significant advances made possible with Big Data Seismology. This review is designed to be a primer for seismologists who are interested in getting up‐to‐speed with how the Big Data revolution is advancing the field of seismology.

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Section: Forensic Seismologymentioning

confidence: 99%

Section: Science Enabled By Big Data Seismologymentioning

confidence: 99%

Big Data Seismology

Arrowsmith

Trugman

MacCarthy

et al. 2022

Reviews of Geophysics

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“…Convolutional neural networks (CNNs) have commonly been used for spatial pattern analysis to learn spatial features, but CNNs are showing remarkable success in many other research and industrial applications, such as vegetation remote sensing [52], seismo-acoustic event classification [53], computer vision [54], RUL estimation [55], among others. Like all typical neural network-type models, CNNs are neuron-based.…”

Section: B Deep Convolutional Neural Network As Data-driven Modelmentioning

confidence: 99%

A Hybrid Model-Based Approach on Prognostics for Railway HVAC

2022

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Prognostics and health management (PHM) of systems usually depends on appropriate prior knowledge and sufficient condition monitoring (CM) data on critical components' degradation process to appropriately estimate the remaining useful life (RUL). A failure of complex or critical systems such as heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage may adversely affect people or the environment. Critical systems must meet restrictive regulations and standards, and this usually results in an early replacement of components. Therefore, the CM datasets lack data on advanced stages of degradation, and this has a significant impact on developing robust diagnostics and prognostics processes. This paper proposes a methodology for implementing a hybrid model-based approach (HyMA) to overcome the limited representativeness of the training dataset for developing a prognostic model. The proposed methodology is evaluated building an HyMA which fuses information from a physics-based model with a deep learning algorithm to implement a prognostics process for a complex and critical system. The physics-based model of the HVAC system is used to generate runto-failure data. This model is built and validated using information and data on the real asset; the failures are modelled according to expert knowledge and an experimental test to evaluate the behaviour of the HVAC system while working, with the air filter at different levels of degradation. In addition to using the sensors located in the real system, we model virtual sensors to observe parameters related to system components' health. The run-to-failure datasets generated are normalized and directly used as inputs to a deep convolutional neural network (CNN) for RUL estimation. The effectiveness of the proposed methodology and approach is evaluated on datasets containing the air filter's run-to-failure data. The experimental results show remarkable accuracy in the RUL estimation, thereby suggesting the proposed HyMA and methodology offer a promising approach for PHM.INDEX TERMS Prognostics and health management, Hybrid Modelling, Deep learning, HVAC system, railway.

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“…Deep learning (DL) has recently become a popular technique in seismology. Indeed, many DL techniques based on Convolutional Neural Network [ 5 ], Deep Recurrent Neural Network [ 6 ], Capsule Neural Network [ 7 ] and Autoencoder [ 8 ] have been proposed to detect and classify earthquakes, as well as other seismic signals triggered by volcanoes and landslides. Though the aforementioned techniques can extract important features from the input data for detection and classification, it is not clear how this extraction is performed or how effective the extracted features are.…”

Section: Introductionmentioning

confidence: 99%

Graph-Based Feature Weight Optimisation and Classification of Continuous Seismic Sensor Array Recordings

Stanković

Stankovic

et al. 2022

Sensors

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Slope instabilities caused by heavy rainfall, man-made activity or earthquakes can be characterised by seismic events. To minimise mortality and infrastructure damage, a good understanding of seismic signal properties characterising slope failures is therefore crucial to classify seismic events recorded from continuous recordings effectively. However, there are limited contributions towards understanding the importance of feature selection for the classification of seismic signals from continuous noisy recordings from multiple channels/sensors. This paper first proposes a novel multi-channel event-detection scheme based on Neyman–Pearson lemma and Multi-channel Coherency Migration (MCM) on the stacked signal across multi-channels. Furthermore, this paper adapts graph-based feature weight optimisation as feature selection, exploiting the signal’s physical characteristics, to improve signal classification. Specifically, we alternatively optimise the feature weight and classification label with graph smoothness and semidefinite programming (SDP). Experimental results show that with expert interpretation, compared with the conventional short-time average/long-time average (STA/LTA) detection approach, our detection method identified 614 more seismic events in five days. Furthermore, feature selection, especially via graph-based feature weight optimisation, provides more focused feature sets with less than half of the original number of features, at the same time enhancing the classification performance; for example, with feature selection, the Graph Laplacian Regularisation classifier (GLR) raised the rockfall and slide quake sensitivities to 92% and 88% from 89% and 85%, respectively.

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DeepQuake — An application of CNN for seismo-acoustic event classification in The Netherlands

Cited by 21 publications

References 33 publications

Big Data Seismology

Big Data Seismology

A Hybrid Model-Based Approach on Prognostics for Railway HVAC

Graph-Based Feature Weight Optimisation and Classification of Continuous Seismic Sensor Array Recordings

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