Browsing by Author "Inza Callupe, Lamberto Adolfo"
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Item Restricted Analysis of dynamics of vulcanian activity of Ubinas volcano, using multicomponent seismic antennas(Elsevier, 2014-01-15) Inza Callupe, Lamberto Adolfo; Métaxian, J. P.; Mars, J. I.; Bean, C. J.; O'Brien, G. S.; Macedo Sánchez, Orlando Efraín; Zandomeneghi, D.A series of 16 vulcanian explosions occurred at Ubinas volcano between May 24 and June 14, 2009. The intervals between explosions were from 2.1 h to more than 6 days (mean interval, 33 h). Considering only the first nine explosions, the average time interval was 7.8 h. Most of the explosions occurred after a short time interval (< 8 h) and had low energy, which suggests that the refilling time was not sufficient for large accumulation of gas. A tremor episode followed 75% of the explosions, which coincided with pulses of ash emission. The durations of the tremors following the explosions were longer for the two highest energy explosions. To better understand the physical processes associated with these eruptive events, we localized the sources of explosions using two seismic antennas that were composed of three-component 10 and 12 sensors. We used the high-resolution MUSIC-3C algorithm to estimate the slowness vector for the first waves that composed the explosion signals recorded by the two antennas assuming propagation in a homogeneous medium. The initial part of the explosions was dominated by two frequencies, at 1.1 Hz and 1.5 Hz, for which we identified two separated sources located at 4810 m and 3890 m +/− 390 altitude, respectively. The position of these two sources was the same for the full 16 explosions. This implies the reproduction of similar mechanisms in the conduit. Based on the eruptive mechanisms proposed for other volcanoes of the same type, we interpret the position of these two sources as the limits of the conduit portion that was involved in the fragmentation process. Seismic data and ground deformation recorded simultaneously less than 2 km from the crater showed a decompression movement 2 s prior to each explosion. This movement can be interpreted as gas leakage at the level of the cap before its destruction. The pressure drop generated in the conduit could be the cause of the fragmentation process that propagated deeper. Based on these observations, we interpret the position of the highest source as the part of the conduit under the cap, and the deeper source as the limit of the fragmentation zone.Item Restricted Automatic classification of volcano seismic signatures(American Geophysical Union, 2018-12) Malfante, Marielle; Dalla Mura, Mauro; Mars, Jérôme I.; Métaxian, Jean-Philippe; Macedo Sánchez, Orlando Efraín; Inza Callupe, Lamberto AdolfoThe prediction of volcanic eruptions and the evaluation of associated risks remain a timely and unresolved issue. This paper presents a method to automatically classify seismic events linked to volcanic activity. As increased seismic activity is an indicator of volcanic unrest, automatic classification of volcano‐seismic events is of major interest for volcano monitoring. The proposed architecture is based on supervised classification, whereby a prediction model is built from an extensive dataset of labeled observations. Relevant events should then be detected. Three steps are involved in the building of the prediction model: (i) signals pre‐processing ; (ii) represention of the signals in the feature space; (iii) use of an automatic classifier to train the model. Our main contribution lies in the feature space where the seismic observations are represented by 102 features gathered from both acoustic and seismic fields. Ideally, observations are separable in the feature space, depending on their class. The architecture is tested on 109,609 seismic events that were recorded between June 2006 and September 2011 at Ubinas Volcano, Peru. Six main classes of signals are considered: long period events, volcanic tremors, volcano‐tectonic events, explosions, hybrid events, and tornillos. Our model reaches 93.5% ± 0.50% accuracy, thereby validating the presented architecture and the features used. Furthermore, we illustrate the limited influence of the learning algorithm used (i.e., random forest, support vector machines) by showing that the results remain accurate regardless of the algorithm selected for the training stage. The model is then used to analyze 6 years of data.Item Open Access Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD(Institute of Electrical and Electronics Engineers, 2020-03-27) Espinoza Lara, Pablo Eduardo; Rolim Fernandes, Carlos Alexandre; Inza Callupe, Lamberto Adolfo; Mars, Jérôme I.; Métaxian, Jean-Philippe; Dalla Mura, Mauro; Malfante, MarielleThis article proposes the design of an automatic classifier using the empirical mode decomposition (EMD) along with machine learning techniques for identifying the five most important types of events of the Ubinas volcano, the most active volcano in Peru. The proposed method uses attributes from temporal, spectral, and cepstral domains, extracted from the EMD of the signals, as well as a set of preprocessing and instrument correction techniques. Due to the fact that multichannel sensors are currently being installed in seismic networks worldwide, the proposed approach uses a multichannel sensor to perform the classification, contrary to the usual approach of the literature of using a single channel. The presented method is scalable to use data from multiple stations with one or more channels. The principal component analysis method is applied to reduce the dimensionality of the feature vector and the supervised classification is carried out by means of several machine learning algorithms, the support vector machine providing the best results. The presented investigation was tested with a large database that has a considerable number of explosion events, measured at the Ubinas volcano, located in Arequipa, Peru. The proposed classification system achieved a success rate of more than 90%.Item Open Access Compréhension des processus magmatiques et localisation de source sismo-volcanique avec des antennes sismiques multicomposantes(Université de Grenoble, 2013-05-30) Inza Callupe, Lamberto Adolfo; Mars, Jérôme I.; Métaxian, Jean-Philippe; Bean, ChristopherCette thèse, à la fois méthodologique et appliquée, a été financée par le programme de bourse IRD (l’Institut de Recherche pour le Développement). Elle concerne la localisation des sources liées à l’activité sismique volcanique. Cette tâche est cruciale pour mieux comprendre le mécanisme d’éruption volcanique. L’apparition émergente des capteurs sismiques à trois composantes (3C) permet d’utiliser des approches moderne de localisation, en particulier pour des séismes longue période liés à l’activité magmatique proche de la surface du volcan. Cette thèse se situe donc dans le contexte de la localisation de sources sismo-volcaniques enregistrées par de réseaux de capteurs. Une attention particulière est portée sur la surveillance de l’activité magmatique qui peut être détecté par des mesures sismiques autour du volcan. Les recherches scientifiques dans ce domaine et l’évaluation des risques éruptifs visent toujours le même objectif: á savoir comprendre les processus physiques de l’éruption. Les réseaux de capteurs sismiques déployés en antenne sismique sont devenus des outils puissants pour surveiller l’activité volcanique. Dans cette thèse, nous étudions le problème de la localisation de sources, basée sur les données enregistrées par des réseaux de capteurs composés de sismomètres à trois composantes (3C). Nous nous concentrerons sur le stratovolcan Ubinas, l’un des plus actifs au Pérou (environ 5400 m d’altitude et possedant une grande caldeira de 1.4 km). Les éruptions de ce volcan historiques et récentes ont présenté un style majoritairement vulcanian caractérisé par des explosions de courte durée et violentes...Item Open Access Earthquake Early Warning Starting From 3 s of Records on a Single Station With Machine Learning(American Geophysical Union, 2023-11-02) Lara, Pablo; Bletery, Quentin; Ampuero, Jean-Paul; Inza Callupe, Lamberto Adolfo; Tavera, HernandoWe introduce the Ensemble Earthquake Early Warning System (E3WS), a set of Machine Learning (ML) algorithms designed to detect, locate, and estimate the magnitude of an earthquake starting from 3 s of P-waves recorded by a single station. The system is made of six Ensemble ML algorithms trained on attributes computed from ground acceleration time series in the temporal, spectral, and cepstral domains. The training set comprises data sets from Peru, Chile, Japan, and the STEAD global data set. E3WS consists of three sequential stages: detection, P-phase picking, and source characterization. The latter involves magnitude, epicentral distance, depth, and back azimuth estimation. E3WS achieves an overall success rate in the discrimination between earthquakes and noise of 99.9%, with no false positive (noise mis-classified as earthquakes) and very few false negatives (earthquakes mis-classified as noise). All false negatives correspond to M ≤ 4.3 earthquakes, which are unlikely to cause any damage. For P-phase picking, the Mean Absolute Error is 0.14 s, small enough for earthquake early warning purposes. For source characterization, the E3WS estimates are virtually unbiased, have better accuracy for magnitude estimation than existing single-station algorithms, and slightly better accuracy for earthquake location. By updating estimates every second, the approach gives time-dependent magnitude estimates that follow the earthquake source time function. E3WS gives faster estimates than present alert systems relying on multiple stations, providing additional valuable seconds for potential protective actions.Item Open Access El terremoto de Arequipa del 3 de abril de 1999 (Mw=6.6.)(Instituto Geofísico del Perú, 1999-04) Tavera, Hernando; Fernández, Efraín; Pérez Pacheco, Ivonne; Salas, Henry; Rodríguez, Simeón; Vilcapoma, Luis; Sánchez, B.; Inza Callupe, Lamberto Adolfo; Agüero, ConsueloEl terremoto de Arequipa del 3 de Abril, es el primero que ocurre en la región Sur de Perú durante el presente año a niveles intermedios de profundidad. Este terremoto de tipo "tensional", se produjo en respuesta a los esfuerzos extensivos generados por el propio peso de la placa de Nazca que tiende a introducirse en el manto. Las localidades mas afectadas fueron Caraveli, Ocofta, Aplao, Camana y Arequipa. De acuerdo con la destrucción causada por el terremoto y otros efectos se observó una intensidad máxima, restringida, de VI en la escala de Mercalli Modificada. El sistema de Defensa Civil de Perú, informó de la muerte de una persona por desplome de una pared y heridos leves en las localidades de Caraveli y Camana. Así mismo, se ha observado daños materiales de consideración en 15 viviendas aproximadamentey daños menores (fisuras y rajaduras) en mayor número en las localidades de Camana, Ocofta, Caraveliy Arequipa. El objetivo del presente informe es presentar los parámetros hipocentrales del terremoto de Arequipa del 3 de Abril de 1999, sus implicaciones sismotectónicas y describir las características y efectos del terremoto en base a los estudios preliminares realizados por el Instituto Geofisico del Perú.Item Restricted Global quieting of high-frequency seismic noise due to COVID-19 pandemic lockdown measures(American Association for the Advancement of Science, 2020-09-11) Lecocq, Thomas; Hicks, Stephen P.; Van Noten, Koen; Van Wijk, Kasper; Koelemeijer, Paula; De Plaen, Raphael S. M.; Massin, Frédérick; Hillers, Gregor; Anthony, Robert E.; Apoloner, Maria-Theresia; Arroyo-Solórzano, Mario; Assink, Jelle D.; Büyükakpınar, Pinar; Cannata, Andrea; Cannavo, Flavio; Carrasco, Sebastian; Caudron, Corentin; Chaves, Esteban J.; Cornwell, David G.; Craig, David; Den Ouden, Olivier F. C.; Diaz, Jordi; Donner, Stefanie; Evangelidis, Christos P.; Evers, Läslo; Fauville, Benoit; Fernandez, Gonzalo A.; Giannopoulos, Dimitrios; Gibbons, Steven J.; Girona, Társilo; Grecu, Bogdan; Grunberg, Marc; Hetényi, György; Horleston, Anna; Inza Callupe, Lamberto Adolfo; Irving, Jessica C. E.; Jamalreyhani, Mohammadreza; Kafka, Alan; Koymans, Mathijs R.; Labedz, Celeste R.; Larose, Eric; Lindsey, Nathaniel J.; McKinnon, Mika; Megies, Tobias; Miller, Meghan S.; Minarik, William; Moresi, Louis; Márquez-Ramírez, Víctor H.; Möllhoff, Martin; Nesbitt, Ian M.; Niyogi, Shankho; Ojeda, Javier; Oth, Adrien; Proud, Simon; Pulli, Jay; Retailleau, Lise; Rintamäki, Annukka E.; Satriano, Claudio; Savage, Martha K.; Shani-Kadmiel, Shahar; Sleeman, Reinoud; Sokos, Efthimios; Stammler, Klaus; Stott, Alexander E.; Subedi, Shiba; Sørensen, Mathilde B.; Taira, Taka'aki; Tapia, Mar; Turhan, Fatih; Van der Pluijm, Ben; Vanstone, Mark; Vergne, Jerome; Vuorinen, Tommi A. T.; Warren, Tristram; Wassermann, Joachim; Xiao, HanHuman activity causes vibrations that propagate into the ground as high-frequency seismic waves. Measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic caused widespread changes in human activity, leading to a months-long reduction in seismic noise of up to 50%. The 2020 seismic noise quiet period is the longest and most prominent global anthropogenic seismic noise reduction on record. Although the reduction is strongest at surface seismometers in populated areas, this seismic quiescence extends for many kilometers radially and hundreds of meters in depth. This quiet period provides an opportunity to detect subtle signals from subsurface seismic sources that would have been concealed in noisier times and to benchmark sources of anthropogenic noise. A strong correlation between seismic noise and independent measurements of human mobility suggests that seismology provides an absolute, real-time estimate of human activities.Item Restricted Localization with multicomponent seismic array(Institute of Electrical and Electronics Engineers, 2011) Inza Callupe, Lamberto Adolfo; Mars, J.; Métaxian, J.-P.; O'Brien, G.; Macedo Sánchez, Orlando EfraínSeismo-volcano source localization is essential to improve our understanding of volcano systems. The lack of clear seismic wave phases prohibits the use of classical location methods. Seismic antennas composed of one-component (1C) seismometers provide a good estimate of the back-azimuth of the waveeld. The depth estimation, on the other hand, is difficult or impossible to determine. In order to determine the source location parameters (back-azimuth and depth), we extend the 1C seismic antenna approach to 3Cs. This communication discusses a high-resolution location method using a 3C array survey (3C-MUSIC algorithm) with data from two seismic antennas installed on an andesitic volcano in Peru (Ubinas volcano). After introducing the 3C MUSIC processing, we evaluate the robustness of the location method on a full waveeld 3D synthetic dataset generated using a digital elevation model of Ubinas volcano and an homogeneous velocity model. Results show that the back-azimuth determined using the 3C array has a smaller error than a 1C array. Only the 3C method allows the recovery of the source depths. Finally, we applied the 3C-MUSIC to two seismic events recorded in 2009. Therefore, extending 1C arrays to 3C arrays in volcano monitoring allows a more accurate determination of the source epicenter and now an estimate for the depth.Item Open Access Long-period seismic events at Ubinas volcano (Peru): their implications and potentiality as monitoring tool(EGU General Assembly, 2012) Zandomeneghi, D.; Inza Callupe, Lamberto Adolfo; Metaxian, J-P.; Macedo Sánchez, Orlando EfraínUbinas volcano (Southern Peru) is an active andesitic stratovolcano, located 75 km East of Arequipa City, with an average occurrence of 6-7 eruptions per century and persistent fumarolic and phreatic activity. The most recent eruption, accompanied by explosions and by the extrusion of a lava dome, started on March 2006 with an increase of seismicity and observed fumarole occurrence followed in April by more intense explosions, recorded until May 2009. To monitor the volcanic activity, the Geophysical Institute of Peru and the Institut de Recherche pour le Développment (France), built up a seismic network around the volcano, installing 4 permanent stations and deploying 8 supplementary temporary broadband seismometers. In addition, in the period May to July 2009, a seismic experiment was carried out on the volcano flanks with 2 cross-shaped dense antennas with broadband seismometers. As the seismic activity was characterized by recurring low-frequency waveforms, we identify their pattern of occurrence through waveform cross-correlation technique, with respect to major eruptive phases and other observations (as volcano ground deformation from tiltmeters, volcanic product composition, etc). Once established their likely association with the eruptive sequence, we utilize both local network and dense-array data and analyze their location, changes in location, spectral content variations and possible physical explanation. The final aim is to introduce this kind of analysis as quantitative tool to understand ongoing eruptive phases at andesitic volcanoes and possibly to forecast magma/fluid significant movements.Item Restricted Machine learning for volcano-seismic signals: challenges and perspectives(Institute of Electrical and Electronics Engineers Inc. (IEEE), 2018-03-09) Malfante, Marielle; Dalla Mura, Mauro; Metaxian, Jean-Phillipe; Mars, Jerome I.; Macedo Sánchez, Orlando Efraín; Inza Callupe, Lamberto AdolfoEnvironmental monitoring is a topic of increasing interest, especially concerning the matter of natural hazards prediction. Regarding volcanic unrest, effective methodologies along with innovative and operational tools are needed to monitor, mitigate, and prevent risks related to volcanic hazards. In general, the current approaches for volcanoes monitoring are mainly based on the manual analysis of various parameters, including gas leaps, deformations measurements, and seismic signals analysis. However, due to the large amount of data acquired by in situ sensors for long-term monitoring, manual inspection is no longer a viable option. As in many big data situations, classic machine-learning approaches are now considered to automatize the analysis of years of recorded signals, thereby enabling monitoring on a larger scale.Item Open Access Seismo-volcano source localization with triaxial broad-band seismic array(Oxford University Press, 2011-10) Inza Callupe, Lamberto Adolfo; Mars, J. I.; Métaxian, J. P.; O'Brien, G. S.; Macedo Sánchez, Orlando EfraínSeismo-volcano source localization is essential to improve our understanding of eruptive dynamics and of magmatic systems. The lack of clear seismic wave phases prohibits the use of classical location methods. Seismic antennas composed of one-component (1C) seismometers provide a good estimate of the backazimuth of the wavefield. The depth estimation, on the other hand, is difficult or impossible to determine. As in classical seismology, the use of three-component (3C) seismometers is now common in volcano studies. To determine the source location parameters (backazimuth and depth), we extend the 1C seismic antenna approach to 3Cs. This paper discusses a high-resolution location method using a 3C array survey (3C-MUSIC algorithm) with data from two seismic antennas installed on an andesitic volcano in Peru (Ubinas volcano). One of the main scientific questions related to the eruptive process of Ubinas volcano is the relationship between the magmatic explosions and long-period (LP) swarms. After introducing the 3C array theory, we evaluate the robustness of the location method on a full wavefield 3-D synthetic data set generated using a digital elevation model of Ubinas volcano and an homogeneous velocity model. Results show that the backazimuth determined using the 3C array has a smaller error than a 1C array. Only the 3C method allows the recovery of the source depths. Finally, we applied the 3C approach to two seismic events recorded in 2009. Crossing the estimated backazimuth and incidence angles, we find sources located 1000 ± 660 m and 3000 ± 730 m below the bottom of the active crater for the explosion and the LP event, respectively. Therefore, extending 1C arrays to 3C arrays in volcano monitoring allows a more accurate determination of the source epicentre and now an estimate for the depth.Item Open Access Short term forecasting of explosions at Ubinas volcano, Perú(American Geophysical Union, 2011-11) Traversa, P.; Lengliné, O.; Macedo Sánchez, Orlando Efraín; Metaxian, J. P.; Grasso, J. R.; Inza Callupe, Lamberto Adolfo; Taipe, EduMost seismic eruption forerunners are described using Volcano‐Tectonic earthquakes, seismic energy release, deformation rates or seismic noise analyses. Using the seismic data recorded at Ubinas volcano (Perú) between 2006 and 2008, we explore the time evolution of the Long Period (LP) seismicity rate prior to 143 explosions. We resolve an average acceleration of the LP rate above the background level during the 2–3 hours preceding the explosion onset. Such an average pattern, which emerges when stacking over LP time series, is robust and stable over all the 2006–2008 period, for which data is available. This accelerating pattern is also recovered when conditioning the LP rate on the occurrence of an other LP event, rather than on the explosion time. It supports a common mechanism for the generation of explosions and LP events, the magma conduit pressure increase being the most probable candidate. The average LP rate acceleration toward an explosion is highly significant prior to the higher energy explosions, supposedly the ones associated with the larger pressure increases. The dramatic decay of the LP activity following explosions, still reinforce the strong relationship between these two processes. We test and we quantify the retrospective forecasting power of these LP rate patterns to predict Ubinas explosions. The prediction quality of the forecasts (e.g. for 17% of alarm time, we predict 63% of Ubinas explosions, with 58% of false alarms) is evaluated using error diagrams. The prediction results are stable and the prediction algorithm validated, i.e. its performance is better than the random guess.