Mixing Unsupervised and Knowledge-Based Analysis for Heterogeneous Object Delineation in Seismic Data

Author(s):  
P. Le Bouteiller ◽  
J. Charléty ◽  
F. Delprat-Jannaud ◽  
D. Granjeon ◽  
C. Gorini
1990 ◽  
Vol 80 (6B) ◽  
pp. 1852-1873 ◽  
Author(s):  
Steven R. Bratt ◽  
Henry J. Swanger ◽  
Richard J. Stead ◽  
Floriana Ryall ◽  
Thomas C. Bache

Abstract The Intelligent Monitoring System (IMS) integrates advanced technologies in a knowledge-based distributed system that automates most of the seismic data interpretation process. Results from IMS during its first 8 weeks of operation (1 October through 25 November 1989) are analyzed to evaluate its performance. During this test period, the IMS processed essentially all data recorded by the NORESS and ARCESS high-frequency arrays in Norway. The emphasis was on detection and location of regional events within 2,000 km of these arrays. All events were reviewed and corrected if necessary by a skilled analyst. The final IMS Bulletin for the period includes 1,580 regional events (∼280 events/day). Approximately 55 per cent were smaller than MLg 1, with the largest just over MLg 3. Comparison of IMS locations in southern Finland and northwestern USSR (800 to 900 km from both arrays) with event locations from the University of Helsinki's local network bulletin are used to assess the detection and location capabilities of the system. Two or more phases (minimum needed to locate) were detected for 96 per cent of the events with magnitude greater than 2.5. The median separation between the IMS and Helsinki locations for all common events was 23.5 km. A consistent bias in arrival-time and azimuth residuals was observed for events in small geographic areas, indicating that refined travel-time models and path corrections could further improve location accuracy. The knowledge base in this first version of IMS was based on analysis of NORESS data, and many of the errors in interpretation corrected by the analysts can be attributed to differences encountered when this knowledge is used to interpret ARCESS data. Nevertheless, nearly 60 per cent of the events appearing in the final bulletin are automatic solutions approved without change or moved (by analyst corrections) less than 25 km from the automatic locations. The IMS had the most difficulty interpreting the overlapping signals generated by closely spaced explosions commonly detonated at mines in the Kola Peninsula and northern Sweden. Using the knowledge acquisition facilities included in the system, the deficiencies responsible for these and other errors are isolated, leading to development of new knowledge to be incorporated in the next version of the IMS knowledge base.


2020 ◽  
pp. 875529302097097
Author(s):  
Azad Yazdani ◽  
Mohammad-Sadegh Shahidzadeh ◽  
Tsuyoshi Takada

In this article, Bayes factors (BFs) are used for selecting and weighting the ground motion prediction equations (GMPEs). BFs are defined as the posterior probability of a model being the best model describing data. The Bayesian framework allows for merging information gathered from available seismic data and the experts’ opinion thus allowing for a bridge between data-driven and non-data-driven methods. A multi-dimensional likelihood function is used to account for earthquake-to-earthquake and record-to-record variability. A study is performed to identify the effects of model uncertainty and dataset variations on Bayesian weights by using simulated data. It was found that for a given median prediction, by increasing standard deviation the relative weights increase until it reaches a maximum and then start to decrease. The standard deviation corresponding to the maximum weights corresponds to the scatter of data used for calculating the weights. The method was applied to a local region with nine preselected local and regional GMPEs. The ranking, selection, and weighting are performed using a local dataset and the results are compared with four available ranking methods. While various methods may yield similar or different ranking results, the proposed method is the only one that provides scientific means of selecting appropriate models from a set of initially selected GMPEs.


2017 ◽  
Vol 38 (3) ◽  
pp. 133-143 ◽  
Author(s):  
Danny Osborne ◽  
Yannick Dufresne ◽  
Gregory Eady ◽  
Jennifer Lees-Marshment ◽  
Cliff van der Linden

Abstract. Research demonstrates that the negative relationship between Openness to Experience and conservatism is heightened among the informed. We extend this literature using national survey data (Study 1; N = 13,203) and data from students (Study 2; N = 311). As predicted, education – a correlate of political sophistication – strengthened the negative relationship between Openness and conservatism (Study 1). Study 2 employed a knowledge-based measure of political sophistication to show that the Openness × Political Sophistication interaction was restricted to the Openness aspect of Openness. These studies demonstrate that knowledge helps people align their ideology with their personality, but that the Openness × Political Sophistication interaction is specific to one aspect of Openness – nuances that are overlooked in the literature.


1994 ◽  
Author(s):  
Gregory Barker ◽  
Keith Millis ◽  
Jonathan M. Golding
Keyword(s):  

2013 ◽  
Author(s):  
Valerio Santangelo ◽  
Simona Arianna Di Francesco ◽  
Serena Mastroberardino ◽  
Emiliano Macaluso

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