Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD

Lara, Pablo Eduardo Espinoza; Fernandes, Carlos Alexandre Rolim; Inza, Adolfo; Mars, Jérôme; Métaxian, Jean-Philippe; Mura, Mauro Dalla; Malfante, Marielle

doi:10.1109/jstars.2020.2982714

Cited by 22 publications

(23 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RF is parallelizable, performs well for high dimensional signals, quick in prediction/training, robust to outliers and non-linear data, handles unbalanced data and low bias [43]. Lara et al [44] presented a fair comparison of different classifiers (multilayer perceptron neural network (MLP), linear discriminant analysis (LDA), RF, and SVM) for the volcano-seismic signal, where it was shown that SVM outperformed all other classifiers, LDA was the worst performing classifier, and the performance of MLP and RF were approximately the same. Note that RF has lower complexity than MLP.…”

Section: T46mentioning

confidence: 99%

Automated Platform for Microseismic Signal Analysis: Denoising, Detection, and Classification in Slope Stability Studies

Stanković

Pytharouli

2021

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

Microseismic monitoring has been increasingly used in the past two decades to illuminate (sub)surface processes such as landslides, due to its ability to record small seismic waves generated by soil movement and/or brittle behaviour of rock. Understanding the evolution of landslide processes is of paramount importance in predicting or even avoiding an imminent failure. Microseismic monitoring recordings are often continuous, noisy and consist of signals emitted by various sources. Manually detecting and distinguishing the signals emitted by an unstable slope is challenging. Research on automated end-to-end denoising, detection, and classification of microseismic events, as an early warning system, is still in its infancy. To this effect, our work is focused on jointly evaluating and developing suitable approaches for signal denoising, accurate event detection, non site-specific feature construction, feature selection and event classification. We propose an automated end-to-end system that can process big data sets of continuous seismic recordings fast and demonstrate applicability and robustness to a wide range of events (distant and local earthquakes, slidequakes, anthropogenic noise etc.). Algorithmic contributions lie in novel signal processing and analysis methods with fewer tunable parameters than the state of the art, evaluated on two field datasets and benchmarked against the state of the art.

show abstract

Section: T46mentioning

confidence: 99%

Automated Platform for Microseismic Signal Analysis: Denoising, Detection, and Classification in Slope Stability Studies

Stanković

Pytharouli

2021

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

show abstract

“…A scalable multi-station, multi-channel classifier, using also the empirical mode decomposition (EMD) first proposed by [31] was applied to Ubinas volcano (Peru). The principal component analysis is used to reduce the dimensionality of the feature vector and a supervised classification is carried out using various methods, with SVM obtaining the best performance [116]. Of course, with a multi-station approach particular care has to be taken in order to build a system which is robust with respect to the loss of one or more seismic stations due to volcanic activity or technical failures.…”

Section: Applications To Seismo-volcanic Datamentioning

confidence: 99%

Machine Learning in Volcanology: A Review

Carniel¹,

Guzmán²

2021

Updates in Volcanology - Transdisciplinary Nature of Volcano Science

View full text Add to dashboard Cite

A volcano is a complex system, and the characterization of its state at any given time is not an easy task. Monitoring data can be used to estimate the probability of an unrest and/or an eruption episode. These can include seismic, magnetic, electromagnetic, deformation, infrasonic, thermal, geochemical data or, in an ideal situation, a combination of them. Merging data of different origins is a non-trivial task, and often even extracting few relevant and information-rich parameters from a homogeneous time series is already challenging. The key to the characterization of volcanic regimes is in fact a process of data reduction that should produce a relatively small vector of features. The next step is the interpretation of the resulting features, through the recognition of similar vectors and for example, their association to a given state of the volcano. This can lead in turn to highlight possible precursors of unrests and eruptions. This final step can benefit from the application of machine learning techniques, that are able to process big data in an efficient way. Other applications of machine learning in volcanology include the analysis and classification of geological, geochemical and petrological “static” data to infer for example, the possible source and mechanism of observed deposits, the analysis of satellite imagery to quickly classify vast regions difficult to investigate on the ground or, again, to detect changes that could indicate an unrest. Moreover, the use of machine learning is gaining importance in other areas of volcanology, not only for monitoring purposes but for differentiating particular geochemical patterns, stratigraphic issues, differentiating morphological patterns of volcanic edifices, or to assess spatial distribution of volcanoes. Machine learning is helpful in the discrimination of magmatic complexes, in distinguishing tectonic settings of volcanic rocks, in the evaluation of correlations of volcanic units, being particularly helpful in tephrochronology, etc. In this chapter we will review the relevant methods and results published in the last decades using machine learning in volcanology, both with respect to the choice of the optimal feature vectors and to their subsequent classification, taking into account both the unsupervised and the supervised approaches.

show abstract

“…The first step of the preprocessing stage is the subtraction of the time-average mean of the signals. After that, the instrumental correction is done by computing the deconvolution associated with the transfer function of the sensor, expressing the seismic signals in their original unity, similarly as in [7]. The goal is to standardize the velocity waveforms obtained by the sensors, originally measured in Seismic Counts, to the unit meter per second (m/s), making the classifier independent on the type of sensor used.…”

Section: A Preprocessingmentioning

confidence: 99%

“…In addition, the work [2] uses attributes in the temporal, spectral, and cepstral domains for the extraction of features, along with the SVM method. In [7], an automatic classification system for volcano events is presented using the empirical mode decomposition (EMD). More recently, the work [8] explored deep learning by using Convolutional Neural Networks (CNNs) to classify spectrograms of seismic events from a South American volcano.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, contrary to the standard approach of the literature of using only a single channel sensor, the database of the present work was recorded with sensors that have 3 channels: vertical, east and north. The use of triaxial sensors has shown to offer a better representation of the seismic signals in comparison to single-channel sensors [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tensor-Based Learning Framework for Automatic Multichannel Volcano-Seismic Classification

Peixoto

Fernandes

Lara

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

This work proposes a supervised tensor-based learning framework for classifying volcano-seismic events from signals recorded at the Ubinas volcano, in Peru, during a period of great activity in 2009. The proposed method is fully tensorial, as it integrates the three main steps of the automatic classification system (feature extraction, dimensionality reduction and classifier) in a general multidimensional framework for tensor data, joining tensor learning techniques such as the Multilinear Principal Component Analysis (MPCA) and the Support Tensor Machine (STuM). By exploiting the use of multiple multichannel triaxial sensors, operating simultaneously in two seismic stations, the tensor patterns are constructed as: stations × channels × features. The multidimensional structure of the data is then preserved, avoiding the tensor vectorization that often leads to a feature vector with a large dimension, which increases the number of parameters and may cause the "curse of dimensionality". Moreover, the array vectorization breaks down the multidimensional structure of the data, which usually leads to performance degradation. The results showed a good performance of the proposed multilinear classification system, significantly outperforming its vectorial counterparts. The best result was obtained with the STuM classifier along with the MPCA.

show abstract

Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD

Cited by 22 publications

References 32 publications

Automated Platform for Microseismic Signal Analysis: Denoising, Detection, and Classification in Slope Stability Studies

Automated Platform for Microseismic Signal Analysis: Denoising, Detection, and Classification in Slope Stability Studies

Machine Learning in Volcanology: A Review

Tensor-Based Learning Framework for Automatic Multichannel Volcano-Seismic Classification

Contact Info

Product

Resources

About