Determination of the intensity-energy response function of the ISA Riber MAC 2 electron analyser

1990 ◽  
Vol 16 (1-12) ◽  
pp. 209-212 ◽  
Author(s):  
M. Repoux ◽  
E. Darque-Ceretti ◽  
M. Casamassima ◽  
J. P. Contour
Keyword(s):  
Response Function ◽  
Energy Response

Response Function of Collimated Detector for Non Axial Detector-Source Geometry

2013 ◽  
Vol 772 ◽  
pp. 571-578
Author(s):  
Rahadi Wirawan ◽  
M. Djamal ◽  
A. Waris ◽  
Gunawan Handayani ◽  
Hong Joo Kim
Keyword(s):  
Monte Carlo ◽  
Response Function ◽  
Gamma Ray ◽  
Geant4 Simulation ◽  
Fundamental Parameter ◽  
Response Functions ◽  
Energy Response ◽  
Simulation Code ◽  
Source Geometry ◽  
Detector Axis

Response function is a fundamental parameter for all detectors in order to analyze the energy distribution of gamma ray which undergoes scattering interaction with the material. The response functions of a 3 in. x 3 in. NaI(Tl) collimated detector for non axis detector-source geometry has been calculated using a Monte Carlo approach from GEANT4 simulation code with 0.662 MeV of mono-energetic of photon gamma ray. Collimated Pb with 4 cm thickness and 2 cm of holes diameter were employed for shielding. The source was assumed as an isotropic point source and it is placed at various positions to the detector axis. The comparison between the measured energy response functions and the simulated energy response functions after normalization were also performed in order to validate the modeling results.

Statistical determination of geophysical well log response functions

Geophysics ◽  
1983 ◽  
Vol 48 (11) ◽  
pp. 1525-1535 ◽  
Author(s):  
Eugene A. Nosal
Keyword(s):  
Power Spectrum ◽  
Response Function ◽  
System Response ◽  
General Terms ◽  
Induction Logging ◽  
Conductivity Profile ◽  
Statistical Determination ◽  
The One

The vertical response function of induction logging tools is shown to be derivable from a power spectrum analysis of the measurement. The vertical response function is the one‐dimensional sequence of weights that characterizes how the tool combines the rock conductivities along the borehole to form an output called the apparent conductivity; it is the system impulse response. The value of knowing this function lies in the possible use of filter theory to aid in data processing and interpretation. Two general notions establish the framework for the analysis. The first is that logging is a linear, convolutional operation. Second, the earth’s conductivity profile forms a stochastic process. The probabilistic component is fleshed out by reasonably based assumptions about the occurrence of bed boundaries and nature of conductivity changes across them. Brought together, these tenets create a characterization of the conductivity sequence that is not a stationary process, but rather is intrinsic, as defined in the discipline of geostatistics. Such a process is described by a variogram, and it is increments of the process that are stationary. The connection between the power spectrum of the measurement and the system response function is made when the convolutional model is merged with the conductivity process. Some examples of induction log functions are shown using these ideas. The analysis is presented in general terms for possibly wider application.

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