Estimación de la magnitud sísmica a partir de registros y parámetros hipocentrales para la estación de Ñaña

La estimación de la magnitud de cualquier evento sísmico es fundamental en la Sismología porque permite tener una idea del tamaño del terremoto y de la energía sísmica liberada. El propósito de esta investigación es definir una ecuación empírica de magnitud sísmica obtenida mediante el modelo de regresión lineal múltiple, proponiéndose una dependencia con lo registrado en una estación sísmica mediante un sensor de banda muy ancha (VBB) triaxial, datos de distancia epicentral y profundidad focal. Se utilizaron señales sísmicas que son brindados en forma libre por Incorporated Research Institutions for Seismology (IRIS) a partir de lo registrado en la estación de Ñaña (NNA). Los valores de distancia epicentral fueron obtenidos a partir de la localización geográfica de la estación de registro y los epicentros de cada evento sísmico; mientras que lo datos de profundidad focal y magnitud fueron obtenidos de un catálogo del National Earthquake Information Center (NEIC). Se obtuvieron resultados de magnitud muy cercanos a los registrados por el NEIC, encontrándose una diferencia máxima de 0.3 entre ellos.

Modelado probabilístico de los sismos que afectan a la ciudad de Cumaná, Venezuela

En esta investigación, partiendo de un conjunto de datos sobre sismos que han impactado la ciudad de Cumaná, Venezuela, se realiza un modelaje probabilístico para determinar las zonas sismogénicas que afectan a esta urbe. Se utiliza la estadística de valores extremos para estudiar series de datos que exceden cierto umbral. Estas series están contenidas en un catálogo tomado de una investigación previa derivado de los registros instrumentales del Centro de Sismología de la Universidad de Oriente, de la Fundación Venezolana de Investigaciones Científicas (FUNVISIS), del National Earthquake Information Center (NEIC) y del United States Geological Survey (USGS). Se dividieron los datos según su profundidad focal; luego utilizando la densidad sísmica, la geología y la ocurrencia de sismos históricos se identificaron las zonas sismogénicas y utilizando la ley de recurrencia de Gutenberg-Ritcher se estiman los parámetros necesarios para obtener las funciones de densidad y de distribución de probabilidad asociadas.

Leveraging Deep Learning in Global 24/7 Real-Time Earthquake Monitoring at the National Earthquake Information Center

Machine-learning algorithms continue to show promise in their application to seismic processing. The U.S. Geological Survey National Earthquake Information Center (NEIC) is exploring the adoption of these tools to aid in simultaneous local, regional, and global real-time earthquake monitoring. As a first step, we describe a simple framework to incorporate deep-learning tools into NEIC operations. Automatic seismic arrival detections made from standard picking methods (e.g., short-term average/long-term average [STA/LTA]) are fed to trained neural network models to improve automatic seismic-arrival (pick) timing and estimate seismic-arrival phase type and source-station distances. These additional data are used to improve the capabilities of the NEIC associator. We compile a dataset of 1.3 million seismic-phase arrivals that represent a globally distributed set of source-station paths covering a range of phase types, magnitudes, and source distances. We train three separate convolutional neural network models to predict arrival time onset, phase type, and distance. We validate the performance of the trained networks on a subset of our existing dataset and further extend validation by exploring the model performance when applied to NEIC automatic pick data feeds. We show that the information provided by these models can be useful in downstream event processing, specifically in seismic-phase association, resulting in reduced false associations and improved location estimates.

Earthquake Magnitude Prediction using Probabilistic Classifiers

Background: Earthquake prediction plays an important role in preventing catastrophic damage. We present here a scheme for earthquake prediction using an improved version Tree Augmented Naïve Bayes (TAN). This approach can achieve an appropriate performance by extracting dependencies among seismicity features effectively. At first we use the simplest Discretization Method, Equal Interval Width, for discretization of six seismicity features (Time, Mean Magnitude, Energy, Slope, deviation, Magnitude deficit) drive from National Earthquake Information Center (NEIC), then in order to the magnitude of earthquake we utilize an improve version of Tree Augmented Naïve Bayes that is based on Decomposable Models. Results: Finally, we test our method by using two schemes and compare it to Tree Augmented Naïve Bayes and Hidden naive Bayes classifier. F-measure metric is used for evaluation of our results. Conclusion: Experimental results demonstrated proposed approach based on an improved version of Tree Augmented Naïve Bayes achieves a higher performance compared to two other methods.

Investigating the signatures of long-range persistence in seismic sequences along Circum-Pacific subduction zones

In the present paper, we analyze the signatures of long-range persistence in seismic sequences along Circum-Pacific subduction zones, from Chile to Kermadec, extracted from the National Earthquake Information Center (NEIC) catalog. This region, known as the Pacific Ring of Fire, is the world’s most active fault line, containing about 90% of the world’s earthquakes. We used the classical rescaled range (R/S) analysis to estimate the long-term persistence signals derived from a scaling parameter called the Hurst exponent, H. We measured the referred exponent and obtained values of H > 0.5, indicating that a long-term memory effect exists. We found a possible fractal relationship between H and the bs(q)-index, which emerges from the non-extensive Gutenberg-Richter law as a function of the asperity. Therefore, H can be associated with a mechanism that controls the level of seismic activity. Finally, we concluded that the dynamics associated with fragment-asperity interactions can be classified as a self-affine fractal phenomenon.Keywords: Applied geophysics; Fault and Fracture Analysis; Mathematics applied to geohysics; Seismology; Statistics;geostatistics ResumoNo presente artigo, analisamos as assinaturas de persistência long-range nas sequências sísmicas ao longo das zonas de subducção Circum-Pacific, do Chile até Kermadec, extraídas do catálogo do Centro Nacional de Informações sobre Terremotos (NEIC). Esta região, conhecida como Anel de Fogo do Pacífico, é a linha de falhas mais ativa do mundo, contendo cerca de 90% dos terremotos do mundo. Usamos a análise clássica R / S para estimar a assinatura de persistência a longo prazo derivada do parâmetro de escalonamento chamado expoente de Hurst, H. Como principal objeto de estudo}, medimos o referido expoente e obtivemos todos os valores de H> 0,5, indicando que existe um efeito de memória de longo prazo. A principal contribuição do nosso artigo foi encontrar uma possível relação entre H e o índice bs (q) - que emerge da lei de Gutenberg-Richter não-extensiva como uma função da aspereza, isto é, H pode estar associado ao mecanismo que controla o nível de atividade dos terremotos. Finalmente, concluímos que a dinâmica associada às interações fragilidade-aspereza pode ser classificado como um fenômeno fractal auto-afim.Palavras-chaves: Geofisica Aplicada; Analise de falhas e fraturas; Matematica Aplicada a Geofisica; Sismologia; Estatistica;geoestatistica

Beyond Earthworm: Keeping the Promise

The founding of the Advanced National Seismic System (ANSS) vision was originally presented in U.S. Geological Survey Circular 1188 (U.S. Geological Survey [USGS], 1999), after many years of discussions and workshops, described in detail by Filson and Arabasz (2016). Much has been accomplished in the ensuing two decades. Disparate and sometimes divergent developments that had been previously explored at individual private and public universities were finally centralized with increased efficiency and coherency of effort. The stated mission of the ANSS is to “… provide accurate and timely data and information products for seismic events, including their effects on buildings and structures, employing modern monitoring methods and technologies.” In this article, an approach (xQuake) is proposed that does not interfere in any way with the mission of the National Earthquake Information Center and ANSS but instead restores much of the community focus and international collaboration that has been lost over the past two decades. xQuake uses an executable graph framework in a pipeline architecture; this framework can be seamlessly integrated into current ANSS quake monitoring systems. This new approach incorporates modern approaches to computer analytics, including multitopic Kafka exchange rings, cloud computing, a self-configuring phase associator, and machine learning. The xGraph system is free for noncommercial use, open source, hardware agnostic (Windows, Linux, Mac), with no requirement for commercial datastores.

GLASS3: A Standalone Multiscale Seismic Detection Associator

The automated global real‐time association of phase picks into seismic sources comes with unique challenges when simultaneously monitoring at local, regional, and global scales. High spatial variability in seismic station density, transitory seismic data availability, and time‐varying noise characteristics of individual stations must be considered in the design of an associator that is fast and accurate with a low false association rate. These challenges are particularly apparent at the U.S. Geological Survey National Earthquake Information Center (NEIC), which monitors seismicity in near‐real time on local, regional, and global scales using seismic data from roughly 2100 real‐time seismic stations. To fully leverage this large dataset, NEIC developed a standalone self‐configuring seismic phase associator, GLobal ASSociator 3 (GLASS3) that simultaneously processes variably scaled 3D association webs, each with a unique set of nucleation criteria (e.g., nucleation stack threshold). GLASS3 has many useful features for real‐time monitoring including its computational efficiency, instantaneous pick processing, and on‐the‐fly configurability such as the creation and removal of targeted association webs and updates to supporting station metadata. GLASS3 runs both as part of a real‐time event processing system and as a configurable standalone associator that can be applied to a large variety of seismic problems. Here, we describe the GLASS3 algorithm and demonstrate (including input data and configuration files) its use in associating phase‐ambiguous picks on multiple scales.

THE IDEA OF APPLICATION THE DEGREE OF A CONTINUOUS MAPPING FOR EARTHQUACKE PREDICTION

The paper describes the possibility of constructing continuous mappings and calculating the degree of mapping with reference to the prediction earthquakes. At the beginning of the article, a definition of the degree of mapping is given. Then several approaches to the construction of possible vector fields are described. It is assumed that the fixed point of these vector fields will be destructive earthquake. In the first approach, the vector fields under consideration describe the geological behavior of the Earth, namely its motion and the plane of discontinuity in the epicenters of earthquakes. In the second approach fields connect foreshocks that are earthquakes that precede the destructive event. At the end of the article, examples of the successful and unsuccessful application of second approach are given. The study based on data from catalog of the NEIC (National Earthquake Information Center).

Características espacio-tiempo de la sismicidad superficial en la Región Sur del Perú durante el período de 1976 a 2005

El objetivo de este estudio consiste en identificar la presencia de asperezas y agrupamientos de sismos a partir de la distribución espacio-tiempo de sismicidad superficial en el Sur del Perú, entre las coordenadas13°-19° Sur y 69°-78° Oeste. (Ver figura 1.)En este estudio se ha utilizado el Catálogo de National Earthquake Information Center (NEIC) (1976 - 2005), con 696 sismos de foco superficial (h < 70 km) y magnitud mínima de 4,5 Mb. La restricción de datos se produce en su nivel máximo de profundidad, debido a que se estima que a la profundidad de 60 km se encuentra el límite de contacto entre las superficies de las placas de Nasca y Sudamericana, dentro del proceso de subducción (Heras, 2002). Se eliminaron sismos menores a 4,5 Mb de magnitud,porque al no ser registrados en un gran número de las estaciones de la red mundial, sus parámetros hipocentrales no son de buena calidad. Asimismo, se ha procedido a eliminar las réplicas de sismos grandes, asumiendo una distribución temporal de 20 días.Palabras clave: sismicidad, subducción, Placa de Nasca, Placa Sudamericana.DOI: http://dx.doi.org/10.21503/CienciayDesarrollo.2006.v7.05