1. Paolo Dell’Aversana, Gianluca Gabbriellini, Alfonso Amendola Eni SpA
6/24/2018
EAGE E-Lecture Series
Using digital music technology for geophysical data analysis and interpretation
Paolo Dell’Aversana, Gianluca Gabbriellini, Alfonso Amendola
Eni SpA 1

2. Introduction Methodology Examples Conclusions

3. Introduction (I) Key messages:
Simultaneous visual-audio display can improve interpretation of geophysical data.
Advanced digital music technology and musical pattern recognition algorithms can improve data mining and categorization.

4. Introduction (II) Motivations:
Technology used in the digital music industry is exported into the geophysical domain.
Combining musical and imaging attributes improves geophysical data interpretation.

5. Multi sensory perception areas of the brain:
Introduction (III)
Multi sensory perception areas of the brain:
Image
Sound
After
S. Cappelli, 2011
Improved cognition
and ‘target’ detection

6. Methodology (I) SEGY, ASCII, LAS …: geophysical domain MIDI, Wav, …:
musical domain
Seismic to MIDI
Frequency analysis
Automatic analysis:
MIR (Musical Information Retrieval)
Pattern recognition …
Interactive analysis:
Sequencer
Virtual Studio …

7. Methodology (II) Geophysical domain Mathematical transforms
(Stockwell, wavelets, …)
Musical domain

8. Examples

9. 1) Single trace analysis
Seismic trace
Transposed
Spectrogram

10. 1) Single trace analysis
Seismic trace
Transposed
Spectrogram

11. 1) Single trace analysis
Seismic trace
Transposed
Spectrogram
MIDI piano roll
display
See also: Dell’Aversana et al., 2016, Sonification of geophysical data through time-frequency transforms, Geophysical Prospecting, accepted for publication.

12. Zoom in the reservoir zone
Gas - oil
MIDI execution time

13. 2) Audio-video display: Skrugard field
Triassic
Tertiary
Permian
Carboniferous
Cretaceous A’
Skrugard
L 175 km A

14. Seismic section selected for the testA’ A
 175 km

15. Significant lateral change
Horizontal segy-to-MIDI transformation
Gradual
change of
geo-musical
facies
Skrugard
(projected) A’ A
Gas
chimney
1200 ms
Gas
Havis
(projected)
Turbidites
Oil
Turbidites
Sand
Brine
Chaotic
Shale - sand
Turbidites
Significant lateral change
Seismic section
 175 km

16. Significant lateral change
Horizontal segy-to-MIDI transformation
Gradual
change of
geo-musical
facies
Skrugard
(projected)
Gas
chimney
1200 ms
Gas
Havis
(projected)
Turbidites
Oil
Turbidites
Sand
Brine
Chaotic
Shale - sand
Turbidites
Significant lateral change
Seismic section
Horizontal seismic trace at 1200 ms
Spectrogram
at 1200 ms
 175 km
MIDI piano roll at 1200 ms (transposed upwards)

17. Significant lateral change
Horizontal segy-to-MIDI transformation
Gradual
change of
geo-musical
facies
Skrugard
(projected)
Gas
chimney
1200 ms
Gas
Havis
(projected)
Turbidites
Oil
Turbidites
Sand
Brine
Chaotic
Shale - sand
Turbidites
Significant lateral change
Seismic section
Horizontal seismic trace at 1200 ms
Spectrogram
at 1200 ms
 175 km
MIDI piano roll at 1200 ms (transposed upwards)

18. Significant lateral change
Horizontal segy-to-MIDI transformation
Gradual
change of
geo-musical
facies
Skrugard
(projected)
Gas
chimney
1200 ms
Gas
Havis
(projected)
Turbidites
Oil
Turbidites
Sand
Brine
Chaotic
Shale - sand
Turbidites
Significant lateral change
Seismic section
Horizontal seismic trace at 1200 ms
Spectrogram
at 1200 ms
MIDI piano roll at 1200 ms (transposed upwards)
 175 km

19. VIDEO OF SKRUGARD EXAMPLE
Web links to this and other examples of audio-video display:

20. 3) MIDI attribute display (test area: confidential)

21. A seismic section translated into instantaneous MIDI attributes.
See also: Amendola et al., 2016, Seismic Facies Analysis through musical attributes, in review on Geophysical Prospecting.

22. 4) MIDI attribute and pattern recognition Approach: application of algorithms of musical pattern recognition to MIDI attributes. Result: very efficient clustering of geological features. Benefit: improved analysis and interpretation of seismic data.

23. Pattern recognition on ensemble of MIDI attributes.
See also: Amendola et al., 2016, Seismic Facies Analysis through musical attributes, in review on Geophysical Prospecting.

24. Conclusions Expanded data analysis through audio-video display.
Improved analysis and interpretation through MIDI attributes and pattern recognition.
Wide range of applications:
hydrocarbon exploration
reservoir characterization
monitoring ….