COMPREHENSIVE ANALYSIS OF SEISMIC SIGNALS FROM PACAYA VOLCANO USING DEEP LEARNING EVENT DETECTION

Pacaya volcano located 30 km SW of Guatemala City, Guatemala, has been erupting intermittently since 1961. Monitoring of seismicity is crucial to understanding current activity levels within Pacaya. Traditional methods of picking these small earthquakes in this noisy environment are imprecise. Pacaya produces many small events that can easily blend in with the background noise. A possible solution for this problem is a machine learning program to pick first arrivals for these earthquakes. We tested a deep learning algorithm (Mousavi et al., 2020) for fast and reliable seismic signal detection within a volcanic system. Data from multiple deployments were used, including permanent and temporary arrays from 2015 to 2022. Initially over 12,000 independent events were detected although most were unlocatable. A predetermined 1D velocity model calculated by Lanza & Waite (2018) was initially used to locate the earthquakes. This velocity model was updated using VELEST and the locations were calculated using new 1D P-wave and S-wave velocity models. This resulted in 512 events after a quality control filtering process. These events ranged in depths from -2.5 km (summit of Pacaya) to 0 km (sea level) all located directly beneath the vent. The detection process took about 2-3 hours per 15 days on each 3-component broadband seismometer. The method shows promise in providing an efficient and effective method to pick volcano tectonic seismic events, and it did well identifying the emergent arrivals in these datasets; however, it has shortcomings in detecting some low-frequency event types. This could be addressed through additional training of the algorithm. The very low speeds in our new P-wave and S-wave velocity models highlight the poor consolidation of the young MacKenney cone. Further study is encouraged to better understand the accuracy and type of earthquakes picked, especially the increased level of activity during or leading up to an eruption at Pacaya volcano

Tracking volcanic explosions using Shannon entropy at Volcán de Colima

The main objective of this work is to show that Shannon Entropy (SE) calculated on continuous seismic signals can be used in a volcanic eruption monitoring system. We analysed three years of volcanic activity of Volcán de Colima, México, recorded between January 2015 and May 2017. This period includes two large explosions, with pyroclastic and lava flows, and intense activity of less energetic explosion, culminating with a period of quiescence. In order to confirm the success of our results, we used images of the Visual Monitoring system of Colima Volcano Observatory. Another of the objectives of this work is to show how the decrease in SE values can be used to track minor explosive activity, helping Machine Learning algorithms to work more efficiently in the complex problem of distinguishing the explosion signals in the seismograms. We show that the two big eruptions selected were forecasted successfully (6 and 2 days respectively) using the decay of SE. We conclude that SE could be used as a complementary tool in seismic volcano monitoring, showing its successful behaviour prior to energetic eruptions, giving time enough to alert the population and prepare for the consequences of an imminent and well predicted moment of the eruption.FEMALE (PID2019-106260GB-I00)PROOF-FOREVER (EUR2022.134044) projectsMinisterio de Ciencia e Innovación del Gobierno de España (MCIN)Agencia Estatal de Investigación (AEI)Fondo Social Europeo (FSE)Programa Estatal de Promoción del Talento y su Empleabilidad en I+D+I Ayudas para contratos predoctorales para la formación de doctores 2020 (PRE2020-092719

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

Fiber Optic Acoustic Sensing to Understand and Affect the Rhythm of the Cities: Proof-of-Concept to Create Data-Driven Urban Mobility Models

In the framework of massive sensing and smart sustainable cities, this work presents an urban distributed acoustic sensing testbed in the vicinity of the School of Technology and Telecommunication Engineering of the University of Granada, Spain. After positioning the sensing technology and the state of the art of similar existing approaches, the results of the monitoring experiment are described. Details of the sensing scenario, basic types of events automatically distinguishable, initial noise removal actions and frequency and signal complexity analysis are provided. The experiment, used as a proof-of-concept, shows the enormous potential of the sensing technology to generate data-driven urban mobility models. In order to support this fact, examples of preliminary density of traffic analysis and average speed calculation for buses, cars and pedestrians in the testbed’s neighborhood are exposed, together with the accidental presence of a local earthquake. Challenges, benefits and future research directions of this sensing technology are pointed out.B-TIC-542-UGR20 funded by “Consejería de Universidad, Investigación e Innovacción de la Junta de AndalucíaERDF A way of making Europ

Classifying seismic waveforms from scratch: a case study in the alpine environment

Nowadays, an increasing amount of seismic data is collected by daily observatory routines. The basic step for successfully analyzing those data is the correct detection of various event types. However, the visually scanning process is a time-consuming task. Applying standard techniques for detection like the STA/LTA trigger still requires the manual control for classification. Here, we present a useful alternative. The incoming data stream is scanned automatically for events of interest. A stochastic classifier, called hidden Markov model, is learned for each class of interest enabling the recognition of highly variable waveforms. In contrast to other automatic techniques as neural networks or support vector machines the algorithm allows to start the classification from scratch as soon as interesting events are identified. Neither the tedious process of collecting training samples nor a time-consuming configuration of the classifier is required. An approach originally introduced for the volcanic task force action allows to learn classifier properties from a single waveform example and some hours of background recording. Besides a reduction of required workload this also enables to detect very rare events. Especially the latter feature provides a milestone point for the use of seismic devices in alpine warning systems. Furthermore, the system offers the opportunity to flag new signal classes that have not been defined before. We demonstrate the application of the classification system using a data set from the Swiss Seismological Survey achieving very high recognition rates. In detail we document all refinements of the classifier providing a step-by-step guide for the fast set up of a well-working classification syste

PICOSS: Python Interface for the Classification of Seismic Signals

Over the last decade machine learning has become increasingly popular for the analysis and characterization of volcano-seismic data. One of the requirements for the application of machine learning methods to the problem of classifying seismic time series is the availability of a training dataset; that is a suite of reference signals, with known classification used for initial validation of the machine outcome. Here, we present PICOSS (Python Interface for the Classification of Seismic Signals), a modular data-curator platform for volcano-seismic data analysis, including detection, segmentation and classification. PICOSS has exportability and standardization at its core; users can select automatic or manual workflows to select and label seismic data from a comprehensive suite of tools, including deep neural networks. The modular implementation of PICOSS includes a portable and intuitive graphical user interface to facilitate essential data labelling tasks for large-scale volcano seismic studies

Volcanic Seismic Event Classification basedon CNN Architectures

This paper explores the use of convolutional neural network architectures in the context of volcanic seismic event classification through the use of gray-level spectrogram images of longperiod and volcano-tectonic seismic events. We combined the architectures with a set of hyperparameter configurations that produced 720 classification models, which were able to learn the morphological pattern described by the gray-level spectrogram images of seismic events...Este artículo explora el uso de arquitecturas de redes neuronales convolucionales en el contexto de clasificación de eventos sísmicos volcánicos mediante el uso de imágenes de espectrogramas en escala de grises de eventos sísmicos de período largo y volcano-tectónicos. Combinamos las arquitecturas con un conjunto de configuraciones de hiperparámetros que produjeron 720 modelos de clasificación, los cuales fueron capaces de aprender los patrones morfológicos descritos por las imágenes de espectrogramas en escala de grises..

Identification of Seismo-Volcanic Regimes at Whakaari/White Island (New Zealand) Via Systematic Tuning of an Unsupervised Classifier

We present an algorithm based on Self-Organizing Maps (SOM) and k-means clustering to recognize patterns in a continuous 12.5-year tremor time series recorded at Whakaari/White Island volcano, New Zealand (hereafter referred to as Whakaari). The approach is extendable to a variety of volcanic settings through systematic tuning of the classifier. Hyperparameters are evaluated by statistical means, yielding a combination of “ideal” SOM parameters for the given data set. Extending from this, we applied a Kernel Density Estimation approach to automatically detect changes within the observed seismicity. We categorize the Whakaari seismic time series into regimes representing distinct volcano-seismic states during recent unrest episodes at Whakaari (2012/2013, 2016, and 2019). There is a clear separation in classification results between background regimes and those representing elevated levels of unrest. Onset of unrest is detected by the classifier 6 weeks before the August 2012 eruption, and ca. 3.5 months before the December 2019 eruption, respectively. Regime changes are corroborated by changes in commonly monitored tremor proxies as well as with reported volcanic activity. The regimes are hypothesized to represent diverse mechanisms including: system pressurization and depressurization, degassing, and elevated surface activity. Labeling these regimes improves visualization of the 2012/2013 and 2019 unrest and eruptive episodes. The pre-eruptive 2016 unrest showed a contrasting shape and nature of seismic regimes, suggesting differing onset and driving processes. The 2016 episode is proposed to result from rapid destabilization of the shallow hydrothermal system, while rising magmatic gases from new injections of magma better explain the 2012/2013 and 2019 episodes