Linear Inversion Imaging Method Based on Jointed Primary Reflection Waves and Surface-related Multiples

2021 ◽  
Author(s):  
Min Ouyang ◽  
Shen Sheng ◽  
Shou-Wei Liu ◽  
Bo Feng
Author(s):  
Aparajita Nath ◽  
Dirk J Verschuur

Abstract To get the best result for seismic imaging using primary reflections, data with densely-spaced sources and receivers are ideally preferred. However, dense acquisition can sometimes be hindered by various obstacles, like platforms or complex topography. Such areas with large data gaps may deter exploration or monitoring, as conventional imaging strategies would either provide poor seismic images or turn out to be very expensive. Surface-related multiples travel along different paths compared to primaries, illuminating a wider subsurface area and hence making them valuable in case of data with large gaps. We propose different strategies of using surface-related multiples to get around the problem of imaging in the case of a large data gap. Conventional least-squares imaging methods that incorporate surface-related multiples do so by re-injecting the measured wavefield in the forward-modelling process, which makes it still sensitive to missing data. We introduce a ‘non-linear’ inversion approach in which the surface multiples are modelled from the original source field. This makes the method less dependent on the receiver geometry, therefore, effectively exploiting the information from surface multiples in cases of limited illumination. However, such an approach is sensitive to the knowledge of the source properties. Therefore, we propose a ‘hybrid’ method that combines the non-linear imaging method with the conventional ‘linear’ multiple imaging method, which further improves our imaging result. We test the methods on numerical as well as field data. The results indicate substantial removal of artefacts in the image derived from linear imaging methods due to incomplete data, by exploiting the surface multiples to a maximum extent.


Author(s):  
T. Y. Tan ◽  
W. K. Tice

In studying ion implanted semiconductors and fast neutron irradiated metals, the need for characterizing small dislocation loops having diameters of a few hundred angstrom units usually arises. The weak beam imaging method is a powerful technique for analyzing these loops. Because of the large reduction in stacking fault (SF) fringe spacing at large sg, this method allows for a rapid determination of whether the loop is faulted, and, hence, whether it is a perfect or a Frank partial loop. This method was first used by Bicknell to image small faulted loops in boron implanted silicon. He explained the fringe spacing by kinematical theory, i.e., ≃l/(Sg) in the fault fringe in depth oscillation. The fault image contrast formation mechanism is, however, really more complicated.


Author(s):  
Akira Tonomura

Electron holography is a two-step imaging method. However, the ultimate performance of holographic imaging is mainly determined by the brightness of the electron beam used in the hologram-formation process. In our 350kV holography electron microscope (see Fig. 1), the decrease in the inherently high brightness of field-emitted electrons is minimized by superposing a magnetic lens in the gun, for a resulting value of 2 × 109 A/cm2 sr. This high brightness has lead to the following distinguished features. The minimum spacing (d) of carrier fringes is d = 0.09 Å, thus allowing a reconstructed image with a resolution, at least in principle, as high as 3d=0.3 Å. The precision in phase measurement can be as high as 2π/100, since the position of fringes can be known precisely from a high-contrast hologram formed under highly collimated illumination. Dynamic observation becomes possible because the current density is high.


2011 ◽  
Vol 59 (S 01) ◽  
Author(s):  
S Ihlenburg ◽  
A Rüffer ◽  
T Radkow ◽  
A Purbojo ◽  
M Glöckler ◽  
...  

2008 ◽  
Vol 39 (01) ◽  
Author(s):  
AJ Fallgatter ◽  
AC Ehlis ◽  
MM Richter ◽  
M Schecklmann ◽  
MM Plichta

2013 ◽  
Vol E96.B (7) ◽  
pp. 2014-2023 ◽  
Author(s):  
Ryo YAMAGUCHI ◽  
Shouhei KIDERA ◽  
Tetsuo KIRIMOTO

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