In-depth comparison of deep artificial neural network architectures on seismic events classification

Canário, João Paulo; Mello, Rodrigo Fernandes de; Curilem, Millaray; Huenupán, Fernando; Rios, Ricardo

doi:10.1016/j.jvolgeores.2020.106881

Cited by 34 publications

(18 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Curilem et al [146] converted spectrograms into 20 × 20 pixel RGB images fed into a simple CNN with only two convolutional layers and successfully classified various volcanic seismic source signals with an accuracy of over 95%. In addition, Canario et al [147] compared the performance of three traditional neural network models, MLP, CNN, and LSTM, for signal classification and designed a new CNN-based model named SeismicNet. SeismicNet can directly input the acquired raw signals without converting them to images and ignores the preprocessing step.…”

Section: Classification Of Volcanic Activitymentioning

confidence: 99%

Recent Advances of Deep Learning in Geological Hazard Forecasting

Wang¹,

Sun²,

Chen³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Geological hazard is an adverse geological condition that can cause loss of life and property. Accurate prediction and analysis of geological hazards is an important and challenging task. In the past decade, there has been a great expansion of geohazard detection data and advancement in data-driven simulation techniques. In particular, great efforts have been made in applying deep learning to predict geohazards. To understand the recent progress in this field, this paper provides an overview of the commonly used data sources and deep neural networks in the prediction of a variety of geological hazards. KEYWORDSGeological hazard; deep learning; neural networks; geohazard data sources; earthquake; volcanic 1 Introduction Geological hazards are caused by endogenous and exogenous geological processes or anthropogenic factors on earth [1,2], which encompass a broad range of abnormal stratigraphic activity or extreme changes in geological environments, such as landslides, avalanches, debris flows, earthquakes, etc. Geological hazards pose a serious threat to human life and property [3]. Only by accurately forecasting the occurrence of geohazards can emergency measures be devised to mitigate the damage caused by the impending disaster. Geological hazard forecasting refers to the use of logical reasoning, numerical simulation, and comprehensive analysis based on historical geological hazard activity patterns, formation conditions, occurrence mechanisms, and other factors to speculate and assess the development and changes of geological hazards and the possible degree of danger and damage in a certain period in the future. Geohazard forecasting has attracted tremendous attention nowadays, particularly considering natural resource scarcity, environmental degradation, population expansion, sustainable development, and world economic integration. Many efforts have been made in forecasting geological hazards in the past decades. The "3S technology", including Global Position System (GPS), Geographic Information System (GIS) [4], and Remote Sensing (RS), has been widely applied in the field of geological hazard forecasting [5]. The

show abstract

Section: Classification Of Volcanic Activitymentioning

confidence: 99%

Recent Advances of Deep Learning in Geological Hazard Forecasting

Wang¹,

Sun²,

Chen³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…Each input waveform is 64 s long, and shorter waveforms are padded with 0 values to reach this length. This dataset was previously used for studying the performance of a range of neural network architectures by Canário et al, 2020a and achieved validation accuracies of over 90% using the SeismicNet, though the models were not tested on unseen data in their study.…”

Section: Llaima Datasetmentioning

confidence: 99%

“…We combine the active learning selection of training data with an existing volcano-seismic event classifier. We use an existing CNN-based volcano-seismic event classifier, known as SeismicNet, proposed by Canário et al (2020a), illustrated in Figure 2A. The SeismicNet classifier is based on a similar architecture designed for classifying waveforms from audio, known as SoundNet (Aytar et al, 2016).…”

Section: Machine Learning Classificationmentioning

confidence: 99%

See 1 more Smart Citation

A Deep Active Learning Approach to the Automatic Classification of Volcano-Seismic Events

et al. 2022

View full text Add to dashboard Cite

Volcano-seismic event classification represents a fundamental component of volcanic monitoring. Recent advances in techniques for the automatic classification of volcano-seismic events using supervised deep learning models achieve high accuracy. However, these deep learning models require a large, labelled training dataset to successfully train a generalisable model. We develop an approach to volcano-seismic event classification making use of active learning, where a machine learning model actively selects the training data which it learns from. We apply a diversity-based active learning approach, which works by selecting new training points which are most dissimilar from points already in the model according to a distance-based calculation applied to the model features. We combine the active learning with an existing volcano-seismic event classifier and apply the model to data from two volcanoes: Nevado del Ruiz, Colombia and Llaima, Chile. We find that models with data selected using an active learning approach achieve better testing accuracy and AUC (Area Under the Receiver Operating Characteristic Curve) than models with data selected using random sampling. Additionally, active learning decreases the labelling burden for the Nevado del Ruiz dataset but offers no increase in performance for the Llaima dataset. To explain these results, we visualise the features from the two datasets and suggest that active learning can reduce the quantity of labelled data required for less separable data, such as the Nevado del Ruiz dataset. This study represents the first evaluation of an active learning approach in volcano-seismology.

show abstract

“…Various machine learning (ML) methods have been applied to resolve this issue (e.g., Dowla et al 1990; Wang and Teng 1995;Del Pezzo et al 2003;Scarpetta et al 2005;Kong et al 2016), of which convolutional neural networks (CNNs; e.g., LeCun et al 2015) have frequently been used for seismic signal discriminations and phase picking (e.g., Perol et al 2018;Ross et al 2018a, b;Sugiyama et al 2021). CNNs have been applied to slow earthquakes in subduction zones (Nakano et al 2019;Takahashi et al 2021) and volcanic signals (Canário et al 2020). Recent studies have combined CNNs with other methods to improve the accuracy and efficiency of these tasks (Mousavi et al 2019(Mousavi et al , 2020Soto and Schurr 2021).…”

Section: Introductionmentioning

confidence: 99%

Discriminating seismic events using 1D and 2D CNNs: applications to volcanic and tectonic datasets

Sugiyama

2022

Earth Planets Space

View full text Add to dashboard Cite

Detecting seismic events, discriminating between different event types, and picking P- and S-wave arrival times are fundamental but laborious tasks in seismology. In response to the ever-increasing volume of seismic observational data, machine learning (ML) methods have been applied to try to resolve these issues. Although it is straightforward to input standard (time-domain) seismic waveforms into ML models, many studies have used time–frequency-domain representations because the frequency components may be effective for discriminating events. However, detailed comparisons of the performances of these two methods are lacking. In this study, we compared the performances of 1D and 2D convolutional neural networks (CNNs) in discriminating events in datasets from two different tectonic settings: tectonic tremor and ordinary earthquakes observed at the Nankai trough, and eruption signals and other volcanic earthquakes at Sakurajima volcano. We found that the 1D and 2D CNNs performed similarly in these applications. Half of the misclassified events were misassigned the same labels in both CNNs, implying that the CNNs learned similar features inherent to the input signals and thus misclassified them similarly. Because the first convolutional layer of a 1D CNN applies a set of finite impulse response (FIR) filters to the input seismograms, these filters are thought to extract signals effective for discriminating events in the first step. Therefore, because our application was the discrimination of signals dominated by low- and high-frequency components, we tested which frequency components were effective for signal discriminations based on the filter responses alone. We found that the FIR filters comprised high-pass and low-pass filters with cut-off frequencies around 7–9 Hz, frequencies at which the magnitude relations of the input signal classes change. This difference in the power of high- and low-frequency components proved essential for correct signal classifications in our dataset. Graphical Abstract

show abstract

In-depth comparison of deep artificial neural network architectures on seismic events classification

Cited by 34 publications

References 34 publications

Recent Advances of Deep Learning in Geological Hazard Forecasting

Recent Advances of Deep Learning in Geological Hazard Forecasting

A Deep Active Learning Approach to the Automatic Classification of Volcano-Seismic Events

Discriminating seismic events using 1D and 2D CNNs: applications to volcanic and tectonic datasets

Contact Info

Product

Resources

About