Wavelet decomposition and advanced denoising techniquesn for analysis and classification of seismic signals

This work describes an automatic classification procedure for seismic signals suitable for the analysis of complex, broad-band waveforms commonly associated with fluid-rock interaction in volcanic and hydrothermal systems. Based on Discrete Wavelet Transform, a set of significant seismic signal features that characterize the type of event is identified (e.g. noise, volcano tectonic, long period). These features are initially assessed for events whose category (class) can be previously determined by an expert analyst. A Bayesian Pattern Recognition supervised technique based on these features is adopted to classify a new ‘unlabelled pattern’, whose class is unknown. In this way values computed for known events are used to classify events of unknown identity ('supervised classification'). A test was performed on seismological data recorded at Campi Flegrei (Italy), which was divided into three classes. Automatic classification accuracy ranges from 82% to 100% over a broad range of datasets

Chapter Machine Learning in Volcanology: A Review

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

Automatic Classification of Seismic Signals at Mt. Vesuvius Volcano, Italy, Using Neural Networks

We present a new strategy for reliable automatic classification of local seismic signals and volcano-tectonic earthquakes (VT). The method is based on a supervised neural network in which a new approach for feature extraction from short period seismic signals is applied. To reduce the number of records required for the analysis we set up a specialized neural classifier, able to distinguish two classes of signals, for each of the selected stations. The neural network architecture is a multilayer perceptron (MLP) with a single hidden layer. Spectral features of the signals and the parameterized attributes of their waveform have been used as input for this network. Feature extraction is done by using both the linear predictor coding technique for computing the spectrograms, and a function of the amplitude for characterizing waveforms. Compared to strategies that use only spectral signatures, the inclusion of properly normalized amplitude features improves the performance of the classifiers, and allows the network to better generalize. To train the MLP network we compared the performance of the quasi-Newton algorithm with the scaled conjugate gradient method. We found that the scaled conjugate gradient approach is the faster of the two, with quite equally good performance. Our method was tested on a dataset recorded by four selected stations of the Mt. Vesuvius monitoring network, for the discrimination of low magnitude VT events and transient signals caused by either artificial (quarry blasts, underwater explosions) and natural (thunder) sources. In this test application we obtained 100% correct classification for one of the possible pairs of signal types (VT versus quarry blasts). Because this method was developed independently of this particular discrimination task, it can be applied to a broad range of other applications

Doppler radar monitoring of lava dome processes at Merapi Volcano, Indonesia

Merapi volcano in Central Java, Indonesia, is considered one of the most dangerous volcanoes worldwide. Due to the high viscosity of its magma, the lava emerging at the top the volcano cannot flow silently down the flanks of the volcano but builds a lava dome. An indicator for the stability of the lava dome are rockfalls and block and ash flows, which are caused by local instabilities at the dome. When the lava dome reaches a critical size, it collapses. This results in dangerous block and ash flows, which can reach several kilometers into the proximity of the volcano. In the past rockfall and block and ash flow activity has been observed visually or by seismic networks. However, visual observations are often impossible due to bad visibility conditions and until now seismic measurements allow only few insights into the dynamic processes that are involved in instability events, i.e. events of material breaks off the lava dome. In order to enhance monitoring of lava dome activity, a first prototype Doppler radar system has been installed at the western of the Merapi in October 2001. This system consists of a frequency modulated continuous wave (FMCW) 24GHz Doppler radar. The Doppler spectra recorded by the system give a relative measure of the amount of material moving through the beam as well as information about its velocities. Because the radar system is insensitive for clouds, the system provides first continuous "quasi-visual" observations of dome instabilities...thesi

Practical Volcano-Independent Recognition of Seismic Events: VULCAN.ears Project

Recognizing the mechanisms underlying seismic activity and tracking temporal and spatial patterns of earthquakes represent primary inputs to monitor active volcanoes and forecast eruptions. To quantify this seismicity, catalogs are established to summarize the history of the observed types and number of volcano-seismic events. In volcano observatories the detection and posterior classification or labeling of the events is manually performed by technicians, often suffering a lack of unified criteria and eventually resulting in poorly reliable labeled databases. State-of-the-art automatic Volcano-Seismic Recognition (VSR) systems allow real-time monitoring and consistent catalogs. VSR systems are generally designed to monitor one station of one volcano, decreasing their efficiency when used to recognize events from another station, in a different eruptive scenario or at different volcanoes. We propose a Volcano-Independent VSR (VI.VSR) solution for creating an exportable VSR system, whose aim is to generate labeled catalogs for observatories which do not have the resources for deploying their own systems. VI.VSR trains universal recognition models with data of several volcanoes to obtain portable and robust characteristics. We have designed the VULCAN.ears ecosystem to facilitate the VI.VSR application in observatories, including the pyVERSO tool to perform VSR tasks in an intuitive way, its graphical interface, geoStudio, and liveVSR for real-time monitoring. Case studies are presented at Deception, Colima, Popocatépetl and Arenal volcanoes testing VI.VSR models in challenging scenarios, obtaining encouraging recognition results in the 70–80% accuracy range. VI.VSR technology represents a major breakthrough to monitor volcanoes with minimal effort, providing reliable seismic catalogs to characterise real-time changes.European Union'sHorizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant 74924

Toward Knowledge Extraction in Classification of Volcano-Seismic Events: Visualizing Hidden States in Recurrent Neural Networks

ACKNOWLEDGMENT The authors would like to thank the Instituto Andaluz de Geofísica for providing us with the Decepction Island dataset and invaluable geophysical insight.Understanding how deep hierarchical models build their knowledge is a key issue in the usage of artificial intelligence to interpret the reality behind data. Depending on the discipline and models used, such knowledge may be represented in ways that are more or less intelligible for humans, limiting further improvements on the performance of the existing models. In order to delve into the characterization and modeling of volcano-seismic signals, this article emphasizes the idea of deciphering what and how recurrent neural networks (RNNs) model, and how this knowledge can be used to improve data interpretation.The key to accomplishing these objectives is both analyzing the hidden state dynamics associated with their hidden units as well as pruning/trimming based on the specialization of neurons. In this article, we process, analyze, and visualize the hidden states activation maps of two RNN architectures when managing different types of volcano-seismic events. As a result, the class-dependent discriminative behavior of most active neurons is analyzed, thereby increasing the comprehension of the detection and classification tasks. Arepresentative dataset fromthe deception island volcano (Antarctica), containing volcano-tectonic earthquakes, long period events, volcanic tremors, and hybrid events, is used to train the models. Experimental analysis shows how neural activity and its associated specialization skills change depending on the architecture chosen and the type of event analyzed.MINECO under Grant PID2019-106260GB-I00 FEMALEFEDER/Junta de Andalucia-Consejería de Economía y Conocimiento/ Proyecto A-TIC-215- UGR18