Reflection coefficients of fractured rocks: A numerical study

2004 ◽  
Author(s):  
Oliver S. Krüger ◽  
Erik H. Saenger ◽  
Stefan Buske ◽  
Serge A. Shapiro
Keyword(s):  
Fractured Rocks ◽  
Numerical Study ◽  
Reflection Coefficients

DKP equation with smooth barrier

2021 ◽  
Author(s):  
O. Langueur ◽  
M. Merad ◽  
A. Rassoul
Keyword(s):  
Hypergeometric Function ◽  
Numerical Study ◽  
Confluent Hypergeometric Function ◽  
Reflection Coefficients ◽  
Step Potential ◽  
Dkp Equation ◽  
Transmission And Reflection Coefficients ◽  
Special Cases ◽  
Rectangular Barrier ◽  
Transmission And Reflection

In this paper, we study the Duffin–Kemmer–Petiau (DKP) equation in the presence of a smooth barrier in dimensions space–time (1+1) dimensions. The eigenfunctions are determined in terms of the confluent hypergeometric function [Formula: see text]. The transmission and reflection coefficients are calculated, special cases as a rectangular barrier and step potential are analyzed. A numerical study is presented for the transmission and reflection coefficients graphs for some values of the parameters [Formula: see text] are plotted.

Machine learning and geophysical inversion — A numerical study

2019 ◽  
Vol 38 (7) ◽  
pp. 512-519 ◽  
Author(s):  
Brian Russell
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Numerical Study ◽  
Learning Algorithms ◽  
Mathematical Structure ◽  
Machine Learning Algorithms ◽  
Reflection Coefficients ◽  
Geophysical Inversion ◽  
The Difference ◽  
Tuning Frequency

As geophysicists, we are trained to conceptualize geophysical problems in detail. However, machine learning algorithms are more difficult to understand and are often thought of as simply “black boxes.” A numerical example is used here to illustrate the difference between geophysical inversion and inversion by machine learning. In doing so, an attempt is made to demystify machine learning algorithms and show that, like inverse problems, they have a definite mathematical structure that can be written down and understood. The example used is the extraction of the underlying reflection coefficients from a synthetic seismic response that was created by convolving a reflection coefficient dipole with a symmetric wavelet. Because the dipole is below the seismic tuning frequency, the overlapping wavelets create both an amplitude increase and extra nonphysical reflection coefficients in the synthetic seismic data. This is a common problem in real seismic data. In discussing the solution to this problem, the topics of deconvolution, recursive inversion, linear regression, and nonlinear regression using a feedforward neural network are covered. It is shown that if the inputs to the deconvolution problem are fully understood, this is the optimal way to extract the true reflection coefficients. However, if the geophysics is not fully understood and large amounts of data are available, machine learning can provide a viable alternative to geophysical inversion.

scholarly journals Numerical study of fluid flow of deforming fractured rocks using dual permeability model

2002 ◽  
Vol 151 (2) ◽  
pp. 452-468 ◽  
Author(s):  
Xing Zhang ◽  
David J. Sanderson ◽  
Andrew J. Barker
Keyword(s):  
Fluid Flow ◽  
Fractured Rocks ◽  
Numerical Study ◽  
Permeability Model

scholarly journals Numerical Study of a Phased Array-Based Ultrasonic Polar Scan to Determine Plane-Wave Reflection Coefficients of Plates

2019 ◽  
Vol 66 (12) ◽  
pp. 1874-1886
Author(s):  
Jannes Daemen ◽  
Arvid Martens ◽  
Mathias Kersemans ◽  
Erik Verboven ◽  
Steven Delrue ◽  
...  
Keyword(s):  
Plane Wave ◽  
Phased Array ◽  
Wave Reflection ◽  
Numerical Study ◽  
Reflection Coefficients ◽  
Ultrasonic Polar Scan

scholarly journals Fracture Unclogging: A Numerical Study of Seismically Induced Viscous Shear Stresses in Fluid‐Saturated Fractured Rocks

2019 ◽  
Vol 124 (11) ◽  
pp. 11705-11727 ◽  
Author(s):  
Nicolás D. Barbosa ◽  
Jürg Hunziker ◽  
Simón Lissa ◽  
Erik H. Saenger ◽  
Matteo Lupi
Keyword(s):  
Fractured Rocks ◽  
Numerical Study ◽  
Shear Stresses ◽  
Viscous Shear
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