Interactive Seismic Event Recognition and its Applications

2000 ◽  
Vol 31 (3) ◽  
pp. 469-472 ◽  
Author(s):  
Min Li ◽  
Iain Mason
Keyword(s):  
Seismic Event ◽  
Event Recognition

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.

Confidence Inflation and Event Recognition With Young and Older Adult Eyewitnesses

2004 ◽  
Author(s):  
Jeffrey S. Neuschatz ◽  
Michael P. Toglia ◽  
Elizabeth L. Preston ◽  
James M. Lampinen ◽  
Joseph S. Neuschatz ◽  
...  
Keyword(s):  
Older Adult ◽  
Event Recognition

scholarly journals Olympic Games Event Recognition via Transfer Learning with Photobombing Guided Data Augmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 12
Author(s):  
Yousef I. Mohamad ◽  
Samah S. Baraheem ◽  
Tam V. Nguyen
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Olympic Games ◽  
Event Recognition ◽  
Surveillance Systems ◽  
Video Captioning ◽  
Practical Applications ◽  
Sport Events ◽  
The Olympic Games

Automatic event recognition in sports photos is both an interesting and valuable research topic in the field of computer vision and deep learning. With the rapid increase and the explosive spread of data, which is being captured momentarily, the need for fast and precise access to the right information has become a challenging task with considerable importance for multiple practical applications, i.e., sports image and video search, sport data analysis, healthcare monitoring applications, monitoring and surveillance systems for indoor and outdoor activities, and video captioning. In this paper, we evaluate different deep learning models in recognizing and interpreting the sport events in the Olympic Games. To this end, we collect a dataset dubbed Olympic Games Event Image Dataset (OGED) including 10 different sport events scheduled for the Olympic Games Tokyo 2020. Then, the transfer learning is applied on three popular deep convolutional neural network architectures, namely, AlexNet, VGG-16 and ResNet-50 along with various data augmentation methods. Extensive experiments show that ResNet-50 with the proposed photobombing guided data augmentation achieves 90% in terms of accuracy.

Damage assessment of San Francesco Church in Amandola hit by Central Italy 2016-2017 seismic event

2020 ◽  
Author(s):  
Ersilia Giordano ◽  
Angela Ferrante ◽  
Elisa Ribilotta ◽  
Francesco Clementi ◽  
Stefano Lenci
Keyword(s):  
Damage Assessment ◽  
Seismic Event ◽  
Central Italy

How not to drown in a sea of information: An event recognition approach

2015 ◽  
Author(s):  
Elias Alevizos ◽  
Alexander Artikis ◽  
Kostas Patroumpas ◽  
Marios Vodas ◽  
Yannis Theodoridis ◽  
...  
Keyword(s):  
Event Recognition
Sign in / Sign up

Export Citation Format

Share Document