Importance of shear in plane layer point source modeling for prestack inversion

1984 ◽  
Author(s):  
A. D. McAulay
Keyword(s):  
Point Source ◽  
Plane Layer ◽  
Source Modeling ◽  
Prestack Inversion

Prestack inversion with plane‐layer point source modeling

Geophysics ◽  
1985 ◽  
Vol 50 (1) ◽  
pp. 77-89 ◽  
Author(s):  
Alastair D. McAulay
Keyword(s):  
Velocity Profile ◽  
Point Source ◽  
Spherical Wave ◽  
Normal Incidence ◽  
Plane Layer ◽  
Source Modeling ◽  
Low Frequencies ◽  
Prestack Inversion ◽  
Band Limited

Prestack inversion with point‐source plane‐layer modeling has many advantages over poststack or normal incidence inversion. For example, it permits the determination of absolute compressional and shear velocities, density variations, and the accurate accounting of interbed and surface multiples. I neglect shear effects in this paper by assuming that they are adequately suppressed by velocity filtering. In the forward modeling step, a spherical wave expansion into plane waves is used to account for the point source. The plane‐wave reflection response for a set of plane layers is extended to the nonnormal incidence case. I use a Hankel transform to account for cylindrical symmetry. Generalized linear inversion is used because the fast recursive approaches available for normal incidence inversion are no longer applicable. I provide the derivation for the required derivative matrix, and I take into account the band‐limited nature of the data in frequency, time, and space. I demonstrate that moveout of events on realistic simulated prestack data enables the determination of absolute compressional velocity in the velocity‐depth profile, even though the data are band‐limited in frequency. I assume that preprocessing has adequately removed the shear and surface effects and that density is constant. Low frequencies in the velocity profile may be obtained more accurately than with velocity analysis used for stacking, because interbed multiples and other modeling phenomena are accounted for in the computation. Autoregressive modeling procedures that predict into the low frequencies of the velocity profile are also less accurate and cannot generate absolute velocity. I suggest future research leading to cost‐effective inversion of real data.

Scattering attenuation in sediments modeled by ARMA processes—Validation of simple Q models

Geophysics ◽  
1994 ◽  
Vol 59 (12) ◽  
pp. 1813-1826 ◽  
Author(s):  
Claudia Kerner ◽  
Peter E. Harris
Keyword(s):  
Point Source ◽  
Moving Average ◽  
Normal Incidence ◽  
Distribution Functions ◽  
Depth Interval ◽  
Elastic Plane ◽  
Depth Range ◽  
Source Modeling ◽  
Scattering Attenuation ◽  
Sedimentary Sequences

We investigate the data requirements for a reliable analysis of frequency‐dependent Q caused by scattering in a finely layered geological structure. Numerical wave propagation experiments in stochastic models were performed. We set up autoregressive‐moving average [ARMA(1,1)] models for the reflection coefficients with non‐Gaussian distribution functions and used published parameter sets estimated for sedimentary sequences from real log data. For ARMA models, analytical expressions for the scattering attenuation α and the quality factor Q can be derived from the O’Doherty‐Anstey formula. The aim of this study is to investigate whether scattering attenuation as derived from the O’Doherty‐Anstey formula is measurable with sufficient accuracy with a traditional vertical seismic profile (VSP) configuration in realistic sedimentary sequences, and if so, whether the data can be inverted to yield the statistics of the sediment sequence. The main result is that reliable estimation of scattering attenuation requires VSP data over a considerable depth interval, depending on the magnitude of the attenuation with errors in the estimates increasing inversely as the depth range increases. Extensions of the O’Doherty‐Anstey theory to non‐normal incidence have been given in the literature. We examine the angle dependence of the results using both elastic plane‐wave modeling and acoustic point‐source modeling. For the weak medium variations considered, elastic effects (e.g., mode conversions) and point‐source effects are negligible at angles up to about 25 degrees.

scholarly journals Point source modeling of matched case-control data with multiple disease subtypes

2012 ◽  
Vol 31 (28) ◽  
pp. 3617-3637 ◽  
Author(s):  
Shi Li ◽  
Bhramar Mukherjee ◽  
Stuart Batterman
Keyword(s):  
Point Source ◽  
Case Control ◽  
Source Modeling ◽  
Control Data ◽  
Matched Case ◽  
Disease Subtypes

Modeling diffuse pollution with a distributed approach

2002 ◽  
Vol 45 (9) ◽  
pp. 149-156 ◽  
Author(s):  
L.F. León ◽  
E.D. Soulis ◽  
N. Kouwen ◽  
G.J. Farquhar
Keyword(s):  
Water Quality ◽  
Point Source ◽  
Geographical Information Systems ◽  
Diffuse Pollution ◽  
Geographical Information ◽  
Nutrient Concentrations ◽  
Source Modeling ◽  
Response Unit ◽  
Distributed Approach ◽  
Group Response

The transferability of parameters for non-point source pollution models to other watersheds, especially those in remote areas without enough data for calibration, is a major problem in diffuse pollution modeling. A water quality component was developed for WATFLOOD (a flood forecast hydrological model) to deal with sediment and nutrient transport. The model uses a distributed group response unit approach for water quantity and quality modeling. Runoff, sediment yield and soluble nutrient concentrations are calculated separately for each land cover class, weighted by area and then routed downstream. The distributed approach for the water quality model for diffuse pollution in agricultural watersheds is described in this paper. Integrating the model with data extracted using GIS technology (Geographical Information Systems) for a local watershed, the model is calibrated for the hydrologic response and validated for the water quality component. With the connection to GIS and the group response unit approach used in this paper, model portability increases substantially, which will improve non-point source modeling at the watershed scale level.

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