Spectral yield estimation of NTS explosions

1991 ◽  
Vol 81 (4) ◽  
pp. 1292-1308
Author(s):  
Steven R. Taylor ◽  
Farid U. Dowla
Keyword(s):  
Shock Wave ◽  
Template Matching ◽  
Spectral Shape ◽  
Source Material ◽  
Matching Function ◽  
Spectral Matching ◽  
Matching Technique ◽  
Regression Techniques ◽  
The Difference ◽  
Unknown Event

Abstract The yields of 299 NTS explosions have been estimated from Pn, Pg and Lg spectra (between 0.1 and 10 Hz) at four regional seismic stations. A spectral template matching technique is used where the spectra from an explosion of unknown yield are compared with the spectra of explosions of known yield. A matching function is defined that is a scaled inverse of the difference between the spectra from the known and unknown explosions. The yields from the seven closest matching explosions are then averaged to estimate the yield of the unknown event. The spectral matching technique appears to perform as well as standard regression techniques utilizing mb(Pn) and mb(Lg) measurements except that no geologic information (such as gas-filled porosity) is required. However, the spectral matching technique is only applicable to very well-calibrated test sites. The key to spectral matching is that the spectral shape is sensitive to the near-source geology. In addition to affecting the absolute spectral levels (i.e., coupling), the dynamic response of the near source material to the radiated shock wave is a major factor controlling the shape of the radiated spectra. The spectral shape can therefore be used as an indicator for predicting the coupling of an explosion, which can be subsequently used to predict its yield.

scholarly journals Concentration of Banking Sector and Loan Markups in Short Term: An Example from Poland

2014 ◽  
Vol 5 (2) ◽  
pp. 26-44
Author(s):  
Krysztof Drachal
Keyword(s):  
Monetary Policy ◽  
Banking Sector ◽  
Mortgage Market ◽  
Future Research ◽  
Simple Linear Regression ◽  
Short Term ◽  
Monthly Data ◽  
Regression Techniques ◽  
The Difference ◽  
The Relationship

The aim of this paper is to present an analysis of the relationship between concentration of the banking sector and banks' markups on offered loans. The markup is understood as the difference between the rate offered by banks and the reference rate fixed by the Monetary Policy Council. The period between 2009 and 2013 was analyzed. Monthly data from the Polish banking sector were considered. This paper also consists of the literature review, which focuses on the mortgage market. The methodology used for the analysis is based mainly on simple linear regression techniques. It is found that such methods are not sufficient to give conclusive answers. Therefore additional future research is proposed.

scholarly journals Hierarchical Region Based Template Matching Technique for Global Motion Reduction of Coronary Cineangiograms

2015 ◽  
Vol 7 (2) ◽  
pp. 156-161 ◽  
Author(s):  
Kulathilake K. A. S. H. ◽  
Ranathunga L. ◽  
Constantine G. R. ◽  
Abdullah N. A.
Keyword(s):  
Template Matching ◽  
Global Motion ◽  
Matching Technique

Techniques to Predict Agricultural Droughts

2005 ◽  
Author(s):  
Zekai Şen
Keyword(s):  
Crop Yield ◽  
Satellite Data ◽  
Wheat Yield ◽  
Weather Variables ◽  
Short Term ◽  
Yield Model ◽  
Regression Techniques ◽  
The Difference ◽  
Effects Of Drought

In general, the techniques to predict drought include statistical regression, time series, stochastic (or probabilistic), and, lately, pattern recognition techniques. All of these techniques require that a quantitative variable be identified to define drought, with which to begin the process of prediction. In the case of agricultural drought, such a variable can be the yield (production per unit area) of the major crop in a region (Kumar, 1998; Boken, 2000). The crop yield in a year can be compared with its long-term average, and drought intensity can be classified as nil, mild, moderate, severe, or disastrous, based on the difference between the current yield and the average yield. Regression techniques estimate crop yields using yield-affecting variables. A comprehensive list of possible variables that affect yield is provided in chapter 1. Usually, the weather variables routinely available for a historical period that significantly affect the yield are included in a regression analysis. Regression techniques using weather data during a growing season produce short-term estimates (e.g., Sakamoto, 1978; Idso et al., 1979; Slabbers and Dunin, 1981; Diaz et al., 1983; Cordery and Graham, 1989; Walker, 1989; Toure et al., 1995; Kumar, 1998). Various researchers in different parts of the world (see other chapters) have developed drought indices that can also be included along with the weather variables to estimate crop yield. For example, Boken and Shaykewich (2002) modifed the Western Canada Wheat Yield Model (Walker, 1989) drought index using daily temperature and precipitation data and advanced very high resolution radiometer (AVHRR) satellite data. The modified model improved the predictive power of the wheat yield model significantly. Some satellite data-based variables that can be used to predict crop yield are described in chapters 5, 6, 9, 13, 19, and 28. The short-term estimates are available just before or around harvest time. But many times long-term estimates are required to predict drought for next year, so that long-term planning for dealing with the effects of drought can be initiated in time.

Development of a multiple-template matching technique for removal of false positives in a computer-aided diagnostic scheme

2001 ◽  
Author(s):  
Qiang Li ◽  
Shigehiko Katsuragawa ◽  
Roger M. Engelmann ◽  
Samuel G. Armato III ◽  
Heber MacMahon ◽  
...  
Keyword(s):  
Template Matching ◽  
False Positives ◽  
Computer Aided ◽  
Matching Technique ◽  
Multiple Template
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