scholarly journals Automatic seismic event recognition and later phase identification for broadband seismograms

1996 ◽  
Vol 86 (6) ◽  
pp. 1896-1909
Author(s):  
Cheng Tong ◽  
Brian L. N. Kennett
Keyword(s):  
Real Time ◽  
Seismic Event ◽  
Expert Knowledge ◽  
Epicentral Distance ◽  
Event Recognition ◽  
Phase Identification ◽  
Classification Problems ◽  
Real Time Analysis ◽  
Single Station ◽  
Broadband Seismograms

Abstract Knowledge of the patterns of frequently observed seismic phases associated with specific distances and depths have been well developed and applied by seismologists (see, e.g., Richter, 1958; Kulhánek, 1990). However, up till now, the expertise of recognizing seismic event patterns for teleseisms has not been translated into automatic processing procedure. A new approach is developed to automate this kind of heuristic human expertise in order to provide a means of improving preliminary event locations from a single site. An automatic interpretation system exploiting three-component broadband seismograms is used to recognize the pattern of seismic arrivals associated with the presence of a seismic event in real time accompanied by an identification of the individual phases. For a single station, such a real-time analysis can be used to provide a preliminary estimation of the location of the event. The inputs to the interpretation process are a set of features for detected phases produced by another real-time phase analyzer. The combinations of these features are investigated using a novel approach to the construction of an expert system. The automatic system exploits expert information to test likely assumptions about phase character and hence epicentral distance and depth. Some hypotheses about the nature of the event will be rejected as implausible, and for the remainder, an assessment is given of the likelihood of the interpretation based on the fit to the character of all available information. This event-recognition procedure provides an effective and feasible means of interprating events at all distances, and characterizing information between hundreds of different possible classes of patterns even when the observation is incomplete. The procedure is based on “assumption trees” and provides a useful tool for classification problems in which a number of factors have to be identified. The control set of expert knowledge used in testing hypotheses is maintained separately from the computational algorithm used in the assumption search; in consequence, the information base can be readily updated.

scholarly journals Severity Classification of a Seismic Event based on the Magnitude-Distance Ratio Using Only One Seismological Station

2015 ◽  
Vol 18 (2) ◽  
pp. 115-122 ◽  
Author(s):  
Luis Hernán Ochoa Gutiérrez ◽  
Luis F Niño ◽  
Carlos A. Vargas
Keyword(s):  
Seismic Event ◽  
Historical Data ◽  
Epicentral Distance ◽  
Magnitude Estimate ◽  
Onset Time ◽  
Stochastic Methods ◽  
Support Vector ◽  
Distance Ratio ◽  
Single Station ◽  
La Red

<p>Seismic event characterization is often accomplished using algorithms based only on information received at seismological stations located closest to the particular event, while ignoring historical data received at those stations. These historical data are stored and unseen at this stage. This characterization process can delay the emergency response, costing valuable time in the mitigation of the adverse effects on the affected population. Seismological stations have recorded data during many events that have been characterized by classical methods, and these data can be used as previous “knowledge” to train such stations to recognize patterns. This knowledge can be used to make faster characterizations using only one three-component broadband station by applying bio-inspired algorithms or recently developed stochastic methods, such as kernel methods. We trained a Support Vector Machine (SVM) algorithm with seismograph data recorded by INGEOMINAS’s National Seismological Network at a three-component station located near Bogota, Colombia. As input model descriptors, we used the following: (1) the integral of the Fourier transform/power spectrum for each component, divided into 7 windows of 2 seconds and beginning at the P onset time, and (2) the ratio between the calculated logarithm of magnitude (Mb) and epicentral distance. We used 986 events with magnitudes greater than 3 recorded from late 2003 to 2008.<br />The algorithm classifies events with magnitude-distance ratios (a measure of the severity of possible damage caused by an earthquake) greater than a background value. This value can be used to estimate the magnitude based on a known epicentral distance, which is calculated from the difference between P and S onset times. This rapid (&lt; 20 seconds) magnitude estimate can be used for rapid response strategies.<br />The results obtained in this work confirm that many hypocentral parameters and a rapid location of a seismic event can be obtained using a few seconds of signal registered at a single station. A cascade scheme of SVMs or other appropriate algorithms can be used to completely classify an event in a very short time with acceptable accuracy using data from only one station.</p><p> </p><p><strong>Resumen</strong></p><p>Los algoritmos de determinación de parámetros hipocentrales empleados en la actualidad, se basan específicamente en la información recibida en las estaciones de monitoreo mas cercanas al epicentro y no tienen en cuenta la valiosa información histórica registrada a lo largo del tiempo en dichas estaciones. Es por esto que los procesos de caracterización toman varios minutos, tiempo precioso que podría ser de gran utilidad en la generación de alertas tempranas que permitan una oportuna reacción ante el evento. El registro de información, a lo largo el tiempo, de sismos ocurridos en los alrededores de la estación, puede ser empleada para dotarla de algún grado de experiencia que le permita, mediante detección y clasificación de patrones, realizar una caracterización previa mucho mas rápida, mediante el empleo de técnicas modernas las cuales pueden ser algoritmos bio-inspirados o métodos estocásticos mas recientes conocidos como métodos Kernel. En el presente trabajo se emplea un método conocido como Maquinas de Soporte Vectorial (MSV), entrenando dicho algoritmo con información de la relación del área bajo la curva de la potencia de la transformada de Fourier de las componentes N-S, E-W y Vertical, calculada para 5 ventanas de 2 segundos, desde la onda p, de 123 sismos de magnitud superior a 3, desde 2004 hasta 2008, alrededor de la estación El Rosal, de la Red Sismológica Nacional de Ingeominas. El Algoritmo clasifica sismos que superen un umbral predeterminado de la relación entre el Logaritmo de la magnitud y la distancia, que refleja, de alguna manera, la intensidad del sismo. Con la obtención de este parámetro será posible conocer la magnitud del evento, debido a que la distancia puede ser calculada, con base en picado de la onda S, y de esta manera establecer una aproximación rápida de la magnitud en un tiempo aproximado de 20 segundos después del evento. Los resultados obtenidos permiten confirmar que con poco tiempo se señal en una sola estación sismológica es posible obtener información confiable para ser empleada en alertas tempranas.</p>

An Interactive Minicomputer Program for the Indexing and Simulation of Electron Diffraction Patterns

1976 ◽  
Vol 34 ◽  
pp. 542-543
Author(s):  
R.P. Goehner ◽  
W.T. Hatfield ◽  
Prakash Rao
Keyword(s):  
Electron Diffraction ◽  
Real Time ◽  
Pattern Analysis ◽  
Flow Diagram ◽  
Hard Copy ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Transmission Electron ◽  
One Step ◽  
Diffraction Patterns

Computer programs are now available in various laboratories for the indexing and simulation of transmission electron diffraction patterns. Although these programs address themselves to the solution of various aspects of the indexing and simulation process, the ultimate goal is to perform real time diffraction pattern analysis directly off of the imaging screen of the transmission electron microscope. The program to be described in this paper represents one step prior to real time analysis. It involves the combination of two programs, described in an earlier paper(l), into a single program for use on an interactive basis with a minicomputer. In our case, the minicomputer is an INTERDATA 70 equipped with a Tektronix 4010-1 graphical display terminal and hard copy unit.A simplified flow diagram of the combined program, written in Fortran IV, is shown in Figure 1. It consists of two programs INDEX and TEDP which index and simulate electron diffraction patterns respectively. The user has the option of choosing either the indexing or simulating aspects of the combined program.

scholarly journals Real-Time Analysis of Animal Feeding Behavior With a Low-Calculation-Power CPU

2020 ◽  
Vol 67 (4) ◽  
pp. 1197-1205 ◽  
Author(s):  
Yuki Totani ◽  
Susumu Kotani ◽  
Kei Odai ◽  
Etsuro Ito ◽  
Manabu Sakakibara
Keyword(s):  
Feeding Behavior ◽  
Real Time ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Animal Feeding

A threshold-based, real-time analysis in early detection of endpoint anomalies using SIEM expertise

2021 ◽  
Vol 2021 (4) ◽  
pp. 7-16
Author(s):  
Sivaraman Eswaran ◽  
Aruna Srinivasan ◽  
Prasad Honnavalli
Keyword(s):  
Early Detection ◽  
Real Time ◽  
Time Analysis ◽  
Real Time Analysis

Chromo-fluorogenic sensors for chemical warfare agents in real-time analysis: journey towards accurate detection and differentiation

2021 ◽  
Vol 57 (28) ◽  
pp. 3430-3444
Author(s):  
Vinod Kumar
Keyword(s):  
Real Time ◽  
Chemical Warfare Agents ◽  
Chemical Warfare ◽  
Chemical Probes ◽  
Time Analysis ◽  
Specific Detection ◽  
Success Stories ◽  
Real Time Analysis ◽  
Warfare Agents ◽  
Unique Chemical

This article describes our journey and success stories in the development of chemical warfare detection, detailing the range of unique chemical probes and methods explored to achieve the specific detection of individual agents in realistic environments.

scholarly journals A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

2021 ◽  
Vol 77 (2) ◽  
pp. 98-108
Author(s):  
R. M. Churchill ◽  
C. S. Chang ◽  
J. Choi ◽  
J. Wong ◽  
S. Klasky ◽  
...  
Keyword(s):  
Real Time ◽  
International Collaboration ◽  
Large Scale ◽  
Data Transfer ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Scale Data
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