Advanced signal recognition methods applied to seismo-volcanic events from Planchon Peteroa Volcanic Complex: Deep Neural Network classifier

Martínez, Verónica L.; Titos, Manuel; Benítez, Carmen; Badi, Gabriela; Casas, José Augusto; Craig, Victoria Hipatia Olivera; Ibáñez, Jesús M.

doi:10.1016/j.jsames.2020.103115

Cited by 14 publications

(10 citation statements)

References 39 publications

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“…e extracted two data pieces are matched according to the above collected dance action and music beat characteristic data. Since there is a corresponding relationship between dance action and music beat before training [18], the corresponding relationship between them is regarded as a corresponding model before matching, which is set as…”

Section: Dance Action and Music Beat Data Matchmentioning

confidence: 99%

Recognition Method of Matching Error between Dance Action and Music Beat Based on Data Mining

Zhu

2022

Security and Communication Networks

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Aiming at the problems of low error recognition accuracy and low correlation of data matching in the existing dance action and music beat matching error recognition methods, a dance action and music beat matching error recognition method based on data mining is designed. The quaternion array is used to represent the joint coordinate points of the dance movement, and the coordinates are rotated to obtain the curve change of the dance movement. The dance movement characteristics are regarded as a set of positive and negative sample data, the initial weights of different sample data are calculated, the absolute value is processed for the music beat signal, the interference factors are filtered with the help of Gauss filter, and according to the rhythm law of the music beat signal, the characteristics of music beat signal are extracted and the feature extraction of dance action matching is completed with music beat. The corresponding relationship between dance action and music beat is regarded as the corresponding model, the pairwise occurrence probability between music beat and action is determined, the matching model between dance action and music beat is discretized, and the peak point of correlation data is introduced to complete the matching between dance action and music beat. The features of matching data are extracted and segmented by short-time Fourier transform, the segmented matching data is transformed into matching data, the matching error identification model is established with the help of support vector mechanism, and the constraint conditions of error detection are set to complete the matching error identification. The experimental results show that the proposed method has high recognition accuracy and high data matching correlation.

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Section: Dance Action and Music Beat Data Matchmentioning

confidence: 99%

Recognition Method of Matching Error between Dance Action and Music Beat Based on Data Mining

Zhu

2022

Security and Communication Networks

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“…Las técnicas de reconocimiento y clasificación se tornan invaluables para el seguimiento de volcanes activos, además, la búsqueda de procesos que ilustren eficacia y eficiencia con el tratamiento de grandes conjuntos de información conlleva al uso de nuevas metodologías, dentro de las cuales, se encuentra la inteligencia artificial. Martinez y otros [26], ilustran en su investigación denominada "Advanced signal recognition methods applied to seismo-volcanic events from Planchon Peteroa Volcanic Complex: Deep Neural Network classifier", la capacidad de las redes neuronales profundas mediante el uso de parametrizaciones de eventos como una herramienta confiable, fuente de un catálogo sísmico, logrando de esta forma, caracterizar escenarios volcánicas aún no analizados. Lo anterior fue consecuente con el objetivo de esta investigación, puesto que, contempla flujos de trabajo parecidos, más no iguales, para el monitoreo de volcanes.…”

Section: Reconocimiento De Señales De Eventos Sismo-volcánicos Con Re...unclassified

“…Con base en el análisis de la información y las librerías ofrecidas por el Software R, se realizó una red neuronal cuya variable dependiente es el valor en radianes de la fase y las variables exploratorias se conforman de las veintiséis (26) ilustradas en la Tabla 9, se tomaron 2 redes ocultas, con cinco (5) y tres (3) neuronas correspondientemente.…”

Section: Creación De Las Redes Neuronales Y Predicciónunclassified

“…De acuerdo a lo anterior, el suministro de información espacial permite el desarrollo de nuevos métodos estadísticos aplicados a problemas geográficos, entre ellos, las nacientes tecnologías que involucran la inteligencia artificial, permitiendo la construcción de predicción en escenarios volcánicos no analizados, con base en el entrenamiento de sitios debidamente estudiados [26]. Por consiguiente, el presente artículo pretende el estudio de las deformaciones de la corteza con base en interferogramas sin desenrollar [6] que permitan la identificación de zonas con movimiento en el volcán activo Nevado del Ruíz, utilizando variables troposféricas de múltiples resoluciones espaciales que permitan el entrenamiento de una red neuronal, cuyo fin, sea la predicción de puntos de fase en el intervalo [-π, π], para sitios con condiciones similares a las encontradas en la zona de estudio.…”

Section: Introductionunclassified

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Predicción de la deformación en la corteza del Volcán – Nevado del Ruíz en el año 2020 mediante información troposférica TROPOMI, técnica DInsar y redes neuronales

Santana

2022

rev.digit.educ.ing

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El Volcán Nevado del Ruíz situado entre los límites de los Departamentos del Caldas y Tolima en Colombia presentó un comportamiento inestable en el trascurso del año 2020, dicha actividad volcánica conllevó a efectos secundarios en la corteza, es por ello que la predicción de deformaciones se torna como menester de los geocientíficos. En el transcurso de la investigación presentada se dispuso el uso de variables troposféricas, tales como evapotranspiración, índice de aerosoles UV, monóxido de carbono, dióxido de nitrógeno, metano, temperatura superficial, entre otros; para entrenar un conjunto de redes neuronales que puedan predecir el comportamiento de la fase resultante de un interferograma sin desenrollar con la técnica DInSAR, cuyo objetivo principal sea identificar y caracterizar el comportamiento de la corteza con base en las condiciones del entorno. Para lo anterior, se realizó la recolección de variables, la conformación de un modelo lineal generalizado y el conjunto de redes neuronales. Posterior al entrenamiento de la red, se realizó la validación con los datos de testeo, otorgando un MSE de 0,17598 y, un r – cuadrado asociado de aproximadamente 0,88454. El modelo resultante permitió la obtención de un conjunto de datos con exactitud temática buena, reflejando el comportamiento del volcán en el año 2020 dado un conjunto de características del ambiente.

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“…Various techniques have been proposed for modelling subsurface dynamics, including Deep Learning and Machine Learning (e.g., Titos et al, 2018;Bueno et al, 2019;Bueno et al, 2021;Martínez et al, 2021), satellite remote sensing (e.g., Ganci et al, 2020), among others (e.g., Saccorotti and Lokmer, 2021). However, tomographic analysis based on seismic velocity and attenuation remains one of the best tools because it can provide direct links between changes in wave-field properties and the physical conditions of the medium (Castro-Melgar et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Small-Scale Volcanic Structures of the Aeolian Volcanic Arc Revealed by Seismic Attenuation

et al. 2021

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We present the first two-dimensional (2-D) spatial distribution of seismic scattering and intrinsic attenuation beneath the Aeolian Islands arc. The Aeolian Islands archipelago represents one of the best examples of a small dimension volcanic island arc characterised by the alternation of different structural domains. Using the seismic wave diffusion model as the basis for the analysis, and using data from an active seismic experiment (TOMO-ETNA), we analysed more than 76,700 seismic paths marked by epicentre-seismic station pairs. Based on frequencies of 4–24 Hz, we identified high regional attenuation, comparable with other volcanic areas of the world. We used two different seismogram lengths, reflecting two different sampling depths, which allowed us to observe two different attenuative behaviours. As in most volcanic regions, scattering attenuation predominates over intrinsic attenuation, but some characteristics are area-specific. Volcanic structures present the highest contribution to scattering, especially in the low frequency range. This behaviour is interpreted to reflect the small size of the islands and the potentially relatively small size of individual magmatic feeding systems. In addition, strong scattering observed in one zone is associated with the northernmost part of the so-called Aeolian-Tindari-Letojanni fault system. In contrast, away from the volcanic islands, intrinsic attenuation dominates over scattering attenuation. We interpret this shift in attenuative behaviour as reflecting the large volume of sedimentary material deposited on the seabed. Owing to their poorly consolidated nature, sediments facilitate intrinsic attenuation via energy dissipation, but in general present high structural homogeneity that is reflected by low levels of scattering. Our results show that this region is not underlain by a large volcanic structural complex such as that beneath nearby Mt. Etna volcano. Instead, we observe dimensionally smaller and isolated subsurface volcanic structures. The identification of such features facilitates improved geological interpretation; we can now separate consolidated marine structures from independent subsurface volcanic elements. The results of this study provide a model for new research in similar regions around the world.

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Advanced signal recognition methods applied to seismo-volcanic events from Planchon Peteroa Volcanic Complex: Deep Neural Network classifier

Cited by 14 publications

References 39 publications

Recognition Method of Matching Error between Dance Action and Music Beat Based on Data Mining

Recognition Method of Matching Error between Dance Action and Music Beat Based on Data Mining

Predicción de la deformación en la corteza del Volcán – Nevado del Ruíz en el año 2020 mediante información troposférica TROPOMI, técnica DInsar y redes neuronales

Small-Scale Volcanic Structures of the Aeolian Volcanic Arc Revealed by Seismic Attenuation

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