VERTICAL STACKING AS A REFLECTION FILTER

Geophysics ◽  
1977 ◽  
Vol 42 (5) ◽  
pp. 1045-1047
Author(s):  
Franklyn K. Levin
Keyword(s):  
Seismic Reflection ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Noise Ratio ◽  
The Individual

In order to improve the signal‐to‐noise ratio of noisy seismic reflection data before velocity determination by computer, it’s common practice to sum several traces having the same source‐to‐geophone (SGD) separation but different CDP points. We say we form a vertical stack. When reflectors are horizontal planes, vertical stacking simply reduces the noise without distorting the reflection waveforms. However, when a reflector is a dipping plane, the individual reflections from that plane may be identical; but they arrive at slightly different times. As a result, the sum reflection is distorted: vertical stacking acts as a filter.

INTEGRATED TECHNIQUES FOR DETERMINING THE VELOCITY FUNCTION IN THE SEISMIC REFLECTION: EXAMPLE OF LOW SIGNAL-TO-NOISE DATA

2016 ◽  
Vol 34 (2) ◽  
Author(s):  
Rodrigo Francis Revorêdo ◽  
Carlos César Nascimento da Silva
Keyword(s):  
Seismic Reflection ◽  
Signal To Noise Ratio ◽  
Hydrocarbon Exploration ◽  
Velocity Analysis ◽  
Signal To Noise ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Noise Data ◽  
Quality And Reliability ◽  
Seismic Images

ABSTRACT. In the hydrocarbon exploration activities, the reprocessing of old seismic reflection data, acquired with few channels and with low signal-to-noise ratio, is commonly undertaken to ameliorate the quality and reliability of the seismic images...Keywords: seismic processing, velocity analysis, CVS. RESUMO. É comum na exploração de hidrocarbonetos o reprocessamento de dados sísmicos antigos, por vezes com um baixo número de canais e baixa razão sinal/ruído, com o objetivo de gerar uma imagem de melhor qualidade e confiabilidade quando comparada àquelas já existentes...Palavras-chave: processamento sísmico, análise de velocidades, CVS. 

scholarly journals Application of the empirical mode decomposition and Hilbert-Huang transform to seismic reflection data

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. H29-H37 ◽  
Author(s):  
Bradley Matthew Battista ◽  
Camelia Knapp ◽  
Tom McGee ◽  
Vaughn Goebel
Keyword(s):  
Signal Processing ◽  
Seismic Reflection ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Seismic Reflection Data ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Reflection Data ◽  
Mode Decomposition ◽  
The Hilbert Transform

Advancements in signal processing may allow for improved imaging and analysis of complex geologic targets found in seismic reflection data. A recent contribution to signal processing is the empirical mode decomposition (EMD) which combines with the Hilbert transform as the Hilbert-Huang transform (HHT). The EMD empirically reduces a time series to several subsignals, each of which is input to the same time-frequency environment via the Hilbert transform. The HHT allows for signals describing stochastic or astochastic processes to be analyzed using instantaneous attributes in the time-frequency domain. The HHT is applied herein to seismic reflection data to: (1) assess the ability of the EMD and HHT to quantify meaningful geologic information in the time and time-frequency domains, and (2) use instantaneous attributes to develop superior filters for improving the signal-to-noise ratio. The objective of this work is to determine whether the HHT allows for empirically-derived characteristics to be used in filter design and application, resulting in better filter performance and enhanced signal-to-noise ratio. Two data sets are used to show successful application of the EMD and HHT to seismic reflection data processing. Nonlinear cable strum is removed from one data set while the other is used to show how the HHT compares to and outperforms Fourier-based processing under certain conditions.

Application of 3D seismic techniques to evaluate ore resources in the West Wits Line goldfield and portions of the West Rand goldfield, South Africa

Geophysics ◽  
2012 ◽  
Vol 77 (5) ◽  
pp. WC163-WC171 ◽  
Author(s):  
Musa S. D. Manzi ◽  
Mark A. S. Gibson ◽  
Kim A. A. Hein ◽  
Nick King ◽  
Raymond J. Durrheim
Keyword(s):  
Seismic Reflection ◽  
Signal To Noise Ratio ◽  
Mine Planning ◽  
3D Seismic ◽  
Borehole Data ◽  
The West ◽  
Seismic Reflection Data ◽  
Witwatersrand Basin ◽  
Reflection Data ◽  
Time Migration

As expensive as 3D seismic reflection surveys are, their high cost is justified by improved imaging of certain ore horizons in some of the Witwatersrand basin gold mines. The merged historical 3D seismic reflection data acquired for Kloof and South Deep mines forms an integral part of their Ventersdorp Contact Reef mine planning and development programme. The recent advances in 3D seismic technology have motivated the reprocessing and reinterpretation of the old data sets using the latest algorithms, therefore significantly increasing the signal-to-noise ratio of the data. In particular, the prestack time migration technique has provided better stratigraphic and structural imaging in complex faulted areas, such as the Witwatersrand basin, relative to older poststack migration methods. Interpretation tools such as seismic attributes have been used to identify a number of subtle geologic structures that have direct impact on ore resource evaluation. Other improvements include more accurate mapping of the depths, dip, and strike of the key seismic horizons and auriferous reefs, yielding a better understanding of the interrelationship between fault activity and reef distribution, and the relative chronology of tectonic events. The 3D seismic data, when integrated with underground mapping and borehole data, provide better imaging and modeling of critical major fault systems and zones of reef loss. Many faults resolve as multifault segments that bound unmined blocks leading to the discovery and delineation of resources in faulted areas of the mines.

scholarly journals Noise reduction with reflection supervirtual interferometry

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. V249-V256
Author(s):  
Kai Lu ◽  
Zhaolun Liu ◽  
Sherif Hanafy ◽  
Gerard Schuster
Keyword(s):  
Noise Reduction ◽  
Field Data ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Data Set ◽  
Reflection Data ◽  
The Poor ◽  
The Earth ◽  
The Common ◽  
Noise Ratio

To image deeper portions of the earth, geophysicists must record reflection data with much greater source-receiver offsets. The problem with these data is that the signal-to-noise ratio (S/N) significantly diminishes with greater offset. In many cases, the poor S/N makes the far-offset reflections imperceptible on the shot records. To mitigate this problem, we have developed supervirtual reflection interferometry (SVI), which can be applied to far-offset reflections to significantly increase their S/N. The key idea is to select the common pair gathers where the phases of the correlated reflection arrivals differ from one another by no more than a quarter of a period so that the traces can be coherently stacked. The traces are correlated and summed together to create traces with virtual reflections, which in turn are convolved with one another and stacked to give the reflection traces with much stronger S/Ns. This is similar to refraction SVI except far-offset reflections are used instead of refractions. The theory is validated with synthetic tests where SVI is applied to far-offset reflection arrivals to significantly improve their S/N. Reflection SVI is also applied to a field data set where the reflections are too noisy to be clearly visible in the traces. After the implementation of reflection SVI, the normal moveout velocity can be accurately picked from the SVI-improved data, leading to a successful poststack migration for this data set.

scholarly journals Recognition and reconstruction of coherent energy with application to deep seismic reflection data

Geophysics ◽  
2000 ◽  
Vol 65 (2) ◽  
pp. 656-667 ◽  
Author(s):  
Mirko van der Baan ◽  
Anne Paul
Keyword(s):  
Seismic Reflection ◽  
Signal To Noise Ratio ◽  
Original Data ◽  
Direct Selection ◽  
Reconstruction Method ◽  
Seismic Reflection Data ◽  
Deep Seismic Reflection ◽  
Reflection Data ◽  
Joint Application ◽  
Recognition Technique

Reflections in deep seismic reflection data tend to be visible on only a limited number of traces in a common midpoint gather. To prevent stack degeneration, any noncoherent reflection energy has to be removed. In this paper, a standard classification technique in remote sensing is presented to enhance data quality. It consists of a recognition technique to detect and extract coherent energy in both common shot gathers and final stacks. This technique uses the statistics of a picked seismic phase to obtain the likelihood distribution of its presence. Multiplication of this likelihood distribution with the original data results in a “cleaned up” section. Application of the technique to data from a deep seismic reflection experiment enhanced the visibility of all reflectors considerably. Because the recognition technique cannot produce an estimate of “missing” data, it is extended with a reconstruction method. Two methods are proposed: application of semblance weighted local slant stacks after recognition, and direct recognition in the linear τ-p domain. In both cases, the power of the stacking process to increase the signal‐to‐noise ratio is combined with the direct selection of only specific seismic phases. The joint application of recognition and reconstruction resulted in data images which showed reflectors more clearly than application of a single technique.

scholarly journals Computer Enhancement of Weak-Beam Images

1973 ◽  
Vol 31 ◽  
pp. 274-275
Author(s):  
H.M. Horgen ◽  
R. E. Villagrana ◽  
D. M. Maher
Keyword(s):  
Signal To Noise Ratio ◽  
Image Contrast ◽  
Signal To Noise ◽  
Low Contrast ◽  
Weak Beam ◽  
Quantitative Image ◽  
Image Width ◽  
Contrast Analysis ◽  
Noise Ratio ◽  
The Individual

In order to perform quantitative image-contrast analysis of low-contrast electron micrographs one must first increase the signal to noise ratio. As an example, consider the weak-beam method of imaging defects by transmission electron microscopy. In this technique an increase in the resolution of closely spaced dislocations is obtained through a narrowing of the individual dislocation image widths. However, this reduction in image width is accompanied by a corresponding decrease in the signal to noise ratio, which in many instances renders quantitative image-contrast analysis impractical. In this note we present an example of the computer image enhancement of a weak-beam micrograph.

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

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