Common focus point approach to complex near surface effects

2001 ◽  
Author(s):  
C. O. H. Hindriks ◽  
D. J. Verschuur
Keyword(s):  
Surface Effects ◽  
Near Surface ◽  
Focus Point

Investigation of surface and near-surface effects on hydrogen desorption kinetics of MgH2

2014 ◽  
Vol 39 (2) ◽  
pp. 862-867 ◽  
Author(s):  
Sandra Kurko ◽  
Igor Milanović ◽  
Jasmina Grbović Novaković ◽  
Nenad Ivanović ◽  
Nikola Novaković
Keyword(s):  
Surface Effects ◽  
Hydrogen Desorption ◽  
Desorption Kinetics ◽  
Near Surface ◽  
Kinetics Of

scholarly journals Near-Surface Effects of Free Atmosphere Stratification in Free Convection

2015 ◽  
Vol 159 (1) ◽  
pp. 69-95 ◽  
Author(s):  
Juan Pedro Mellado ◽  
Chiel C. van Heerwaarden ◽  
Jade Rachele Garcia
Keyword(s):  
Free Convection ◽  
Surface Effects ◽  
Free Atmosphere ◽  
Near Surface

Elastic tilted orthorhombic (and simpler) wave modeling including free-surface topography

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. T93-T108 ◽  
Author(s):  
Stig Hestholm
Keyword(s):  
Free Surface ◽  
Boundary Conditions ◽  
Surface Topography ◽  
Surface Effects ◽  
Hydrocarbon Exploration ◽  
Computational Method ◽  
Orthorhombic Symmetry ◽  
Surface Boundary ◽  
Wave Modeling ◽  
Near Surface

Computational resources have increased in capacity over time — mostly by speed, partly by memory. Consequently, people have continuously explored the possibilities of performing wave modeling and inversion of increasing physical complexity. Achieving a detailed as possible image of the earth’s subsurface improves the success of hydrocarbon exploration, and it is important for other applications, such as archeology, mining, and engineering. I have developed an accurate computational method for elastic wave modeling up to tilted orthorhombic symmetry of anisotropy. The model may be covered by an arbitrary topographic function along the free surface. Through snapshots and seismograms of the wavefield, I confirm known effects from applying the code to plane, free surfaces (horizontal or tilted) as well as more complex topographies. The method is based on adapting a curved grid to a free-surface topography at hand, and transforming the wave equations and the topography free-surface boundary conditions from this grid to a rectangular grid, where finite-difference (FD) calculations can be performed. Free-surface topography boundary conditions for the particle velocities originate from locally setting the normal stress components to zero at the curved grid free surface. Vanishing normal traction is achieved by additionally imposing mirror conditions on stresses across the free surface. This leads me to achieve a more accurate modeling of free-surface waves (Rayleigh — Rg-waves in particular), using either FDs or any other numerical discretization method. Statics correction, muting, and destructive processing, which all consider free-surface effects as noise, can hence be avoided in inversion/imaging because surface effects can be more accurately simulated. By including near-surface effects in the full wavefield, we ultimately obtain superior inversion for interior earth materials, also for deeper physical medium properties.

LONG WAVELENGTH STATIC ESTIMATION

Geophysics ◽  
1976 ◽  
Vol 41 (5) ◽  
pp. 939-959 ◽  
Author(s):  
Aaron H. Booker ◽  
A. Frank Linville ◽  
Cameron B. Wason
Keyword(s):  
Surface Effects ◽  
Mackenzie Delta ◽  
Model Data ◽  
Static Structure ◽  
Near Surface ◽  
Long Wavelength ◽  
Time Profiles ◽  
Common Depth Point ◽  
Theoretical Performance ◽  
Structural Anomalies

Estimation and removal of near‐surface effects in common‐depth‐point (CDP) data have been frequently discussed in the literature. A common problem with many automated statics techniques is their inability to extract statics whose spatial wavelengths are longer than a spread length. This, of course, can result in false structural anomalies. This paper describes an approach which extends the useful static estimation bandwidth to wavelengths of the order of 4 to 8 spread lengths. Traveltimes from one or more reflecting horizons are picked at each depth point and CDP offset. The time profiles are then decomposed into source static, receiver static, structure, and residual normal moveout (RNMO) estimates, and the process is iterated if required. A suite of analytical displays provides the user with direct QC measures of the traveltime picking performance. The technique will be demonstrated on model data to illustrate the theoretical performance over slowly changing near‐surface weathering anomalies. In addition, field examples will be shown from the Mackenzie Delta where permafrost variability in the near‐surface can create large traveltime anomalies.

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