The oxford dictionary defines an attribute as, “a quality ascribed to any person or thing”. We have extended this definition to: “seismic attributes are all the information obtained from seismic data, either by direct measurements or by logical or experience based reasoning In reflection seismology : aseismic attributes is a quality extracted or derived from seismic data that can be analyzed in order to enhance information that might be more subtle in a traditional seismic image , leading to a better geological or geophysical interpretation of the data Over the past decades, we have witnessed attribute developments track breakthroughs in reflector acquisition and mapping, fault identification, bright spot identification, frequency loss, thin bed tuning, seismic stratigraphy.

1. Ain Shams University
Faculty of science
Geophysics Department
SEISMIC ATTRIBUTES
By / Dalia Hassan Mohamed
4th year student B.SC essay
Submitted To
Geophysics Department
Supervision
PROF.DR.ADEELNASER HELAL
Cairo, Egypt
2015-2016
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2. OUTLINES
1- Introduction
2- A time line of seismic attributes
development
3- Geometrical seismic attributes
4- Physical seismic attributes
5- Conclusions
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3. INTRODUCTION
 The oxford dictionary defines an attribute as, “a quality ascribed to any
person or thing”. We have extended this definition to: “seismic
attributes are all the information obtained from seismic data, either by
direct measurements or by logical or experience based reasoning.
 In reflection seismology : aseismic attributes is a quality extracted or
derived from seismic data that can be analyzed in order to enhance
information that might be more subtle in a traditional seismic image ,
leading to a better geological or geophysical interpretation of the data.
 The principal objectives of the attributes are to provide accurate and
detailed information to the interpreter on structural, stratigraphic and
lithological parameters of the seismic prospect.
 Seismic Attributes are divided into two general classifications;
I) Physical Attributes,
II) Geometric attributes.
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4. Fig A time line of seismic attributes developments and their relation to key
advance in seismic exploration technology (After Marfurt, K.J& Chopra, S, 2005)
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5. Geometric seismic attributes (structural )
enhance the visibility of the geometrical characteristics
of seismic data such as dip , Azmith & continuity
Coherency Curvature Fracture orientation
Fig overview of geometrical seismic attributes (Schutle, B& Manthei, D,
2014, Talisman Energy)
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6. Fig Examples of lateral variations in seismic waveforms:
(a) A flat, laterally invariant, or coherent, waveform
(b) A synclinal, but otherwise laterally invariant, or coherent, waveform,
(c) A laterally variable waveform indicative of lateral changes in impedance or thickness
(Chopra, S & Marfurt, K.J, 2007, Seismic Attributes for Prospect Identification and
Reservoir Characterization, SEG Geophysical Development Series No.11)
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7. Fig Time slices, at t = 1.200 s, through (a) a seismic data volume and (b) the
corresponding coherence volume calculated using an 11-trace semblance algorithm.
Although channels (white arrows) and faults (gray arrows) can be seen on the seismic
time slice, lineaments such as that indicated by the black arrow are more ambiguous.
The coherence slice allows us to interpret our data more confidently in the time-slice
mode. The black arrow clearly indicates the edge of a channel. Previously
unrecognizable faults and channels now appear. After Marfurt et al. (1998)
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8. SURFACE CURVATURE
Figure (5) an illustrated definition of 2D curvature (After Roberts, A, 2001)
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9. N
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max
VOLUME CURVATURE
Fig Curvature in three- dimensions. X and y represent the map axes, with z representing
the time or depth axis. Note the intersection of two orthogonal planes with the surface,
which describes the maximum curvature, K max and the minimum curvature, Kmin. Two
other orthogonal normal curvatures, the dip curvature, Kd and the strike curvature, Ks are
also drawn on the surface. See text for a more detailed description. (After Roberts, A,
2001)
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10. Fig Comparison of (a) the original seismic profile and
(b) the corresponding structural oriented filtered with edge protection one. (After
Liu, W & He, and Y. 2012)
STRUCTURE-ORIENTED FILTERING
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11. Physical seismic
attributes
Pre stack Post stack
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12. Fig complex seismic trace ( first described by tanner 1979)
COMPLEX TRACE
CT (t) =T (t) +iH (t)
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13. Fig Instantaneous phase (After, Taner, M, 2001)
INSTANTANEOUS PHASE (post- stack )
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14. INSTANTANEOUS FREQUENCY (post- stack )
Fig Instantaneous Frequency (After, Taner, M, 2001)
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15. THIN BED INDICATOR (post – stack )
Fig segment of an inline from a) input seismic data, (b) equivalent inline from filtered
thin – bed reflectivity derived from the input seismic data (After Chopra, S & Edmonds,
M. 2011)
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16. INSTANTANEOUS RELATIVE ACOUSTIC
IMPEDANCE (post –stack )
Fig Show A Relative Acoustic Impedance (which can highlight higher porosity and
stratigraphic edge – like sequence boundaries). (After Cooper, R, 2011)
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17. RMS VELOCITIES OF REFLECTORS (pre- stack )
Fig RMS velocities of reflector (http://ocw.tudelft.nl )
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18. CONCLUSIONS
 Aseismic attribute is a quantitative measure of a seismic characteristic of
interest.
 Good seismic attributes and attribute analysis tools mimic a good
interpreter.
 Over the past decades, we have witnessed attribute developments track
breakthroughs in reflector acquisition and mapping, fault identification,
bright spot identification, frequency loss, thin bed tuning, seismic
stratigraphy.
 Complex seismic trace attributes have become important qualitative and
quantitative measures for geophysical exploration.
 Attributes have made it possible to define seismic data in a multidimensional
form and neural network technology enables us to unravel the complex
nonlinear relationships between seismic data and rock and fluid properties.
 Recently published case histories clearly show that multiple attributes
overcome the failures associated with single attribute usage.
 Combined attributes translated by neural networks are becoming principal
tools for lithology prediction and reservoir characterization.
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19. hank ou!