1. Controlled Noise Seismology Sherif M. Hanafy 2015 SEG Annual Meeting New Orleans, 18-23 October 2015

2. Outline Motivation Theory Field Data Example Introduction Data Recording Data Processing Active vs. CNS Extraction Refraction Waves Summery and Future Work

3. Outline Motivation Theory Field Data Example Introduction Data Recording Data Processing Active vs. CNS Extraction Refraction Waves Summery and Future Work

4. Motivation Problem: Recording active-source seismic data for shallow applications is expensive Solution: Use ambient noise seismology Disadvantages: 1.Passive data requires long recording times (weeks to months) 2.Only surface waves can be retrieved

5. Controlled Noise Seismology We propose to use controlled noise seismology (CNS) to generate ambient noise data, advantages include: 1.Short recording times (few hours in our field examples) 2.Retrieve surface waves which can be inverted for S-wave tomograms 3.Possibility of retrieve refraction waves (results are promising) Halliday, D., A. Curtis, and E. Kragh, 2008. Seismic surface waves in a suburban environment: Active and passive interferometric methods. The Leading Edge, Vol. 27, No. 2, pp. 210-218

6. Outline Motivation Theory Field Data Example Introduction Data Recording Data Processing Active vs. CNS Extraction Refraction Waves Summery and Future Work

7. Data Recording and Processing Recording and processing CNS data is a 2-steps process 1.Drove a vehicle around the geophone line and continuously record the generated noise. 2.The recorded data set is then correlated and the correlograms are stacked to generate the surface waves.

8. 1. Generate CNS Data In our field tests we Used a pick up truck Attached 2 tires and a wooden bar to the back of the truck Drove it in loops around the receiver line for 2.5 hours. Driving speed was 20 – 25 km/h Example of the recorded CNS data

9. 2. Process the CNS data To process the CNS data we QC the data and remove any bad traces Recorded data is divided into short windows A master trace is selected and then correlated with all other traces Result from different windows are stacked together [(|)]= where P(A) is the passive data recorded at receiver A, P(B) is the passive data recorded at receiver B, k is the wavenumber, denotes averaging, and G(A|B) is the virtual shot gather with a virtual shot at B and a receiver at A

10. Outline Motivation Theory Field Data Example Introduction Data Recording Data Processing Active vs. CNS Extraction Refraction Waves Summery and Future Work

11. Introduction to Field Examples

12. Outline Motivation Theory Field Data Example Introduction Data Recording Data Processing Active vs. CNS Extraction Refraction Waves Summery and Future Work

13. Recording CNS Data One seismic profile 120 receivers (4.5 Hz and 40 Hz) at 5 m intervals CNS: 2.5 hours of driving around the line Active Source Data: 120 CSGs. Shot interval is 5 m Total recorded time is 2 s ……………. 40 Hz 4.5 Hz 5 m

14. Recording CNS Data

15. Outline Motivation Theory Field Data Example Introduction Data Recording Data Processing Active vs. CNS Extraction Refraction Waves Summery and Future Work

16. Processing CNS Data Pre-correlation steps 1.Mute bad traces 2.Correlate all 60 traces with the selected reference trace 3.Stack the correlograms Post-correlation steps 1.Band-pass filter the correlated gathers (5/10 – 50/70 Hz) We do not need normalization

17. Processing CNS Data Raw data – 4.5 Hz receivers Time (s) X (m) 0.0600 0.0 200 Raw data – 40 Hz receivers Time (s) X (m) 0.0600 0.0 200 Total recorded time = 150 minutes Divided into 9 windows, each one is 1000 sec long

18. Outline Motivation Theory Field Data Example Introduction Data Recording Data Processing Active vs. CNS Extraction Refraction Waves Summery and Future Work

19. Virtual CSGs Active-source data – 40 Hz receivers Time (s) X (m) 0.0600 0.0 1.5 Virtual data – 40 Hz receivers Time (s) X (m) 0.0600 -1.5 1.5 0.0

20. Virtual CSGs Active-source data – 4.5 Hz receivers Time (s) X (m) 0.0600 0.0 1.5 Virtual data – 4.5 Hz receivers Time (s) X (m) 0.0600 -1.5 1.5 0.0

21. Effect of Total Recording Time Virtual data – 4.5 Hz receivers – 30 minutes Time (s) X (m) 0.0600 -1.5 1.5 0.0 Virtual data – 4.5 Hz receivers – 10 minutes Time (s) X (m) 0.0600 -1.5 1.5 0.0

22. Effect of Total Recording Time Virtual data – 40 Hz receivers – 30 minutes Time (s) X (m) 0.0600 -1.5 1.5 0.0 Virtual data – 40 Hz receivers – 10 minutes Time (s) X (m) 0.0600 -1.5 1.5 0.0

23. Outline Motivation Theory Field Data Example Introduction Data Recording Data Processing Active vs. CNS Extraction Refraction Waves Summery and Future Work Acknowledgements

24. Extract Refraction Waves from CNS Data Pre-correlation steps 1.Mute bad traces 2.Band-pass filter 3.Spectral whitening 4.Correlate all 60 traces with the selected reference trace 5.Stacked the results Post-correlation steps 1.Band-pass filter the correlated gathers (5/10 – 50/70 Hz) 2.Trace stacking within λ/2 offset

25. Extract Refraction Waves from CNS Data Active-source data – 4.5 Hz receivers Time (s) X (m) 0.0600 0.0 1.5 CNS data – 4.5 Hz receivers Refractions Time (s) X (m) 0.0600 0.0 1.5 Courtesy of Lei Fu

26. Outline Motivation Theory Field Data Example Introduction Data Recording Data Processing Active vs. CNS Extraction Refraction Waves Summery and Future Work

27. Summery We propose to use controlled noise seismology (CNS) to generate ambient noise data, advantages include: 1.Short recording times (2.5 hours in our field example) 2.Retrieve surface waves which can be inverted for S-wave tomogram 3.Possibility of retrieve refraction waves (results are promising) 150 minutes of CNS is successfully used to reconstruct surface waves. We were able to extract refraction waves from the CNS virtual CSGs

28. Future Work Test the proposed technique on other field data sets. Try it on 3D layout. Invert refraction times from CNS CSGs and compare its tomogram with that from controlled-source data set.

29. Acknowledgements I would like to thank Ahmed Metwally, Abdullah AlTheyab, Jing Li, and Lei Fu for all the help the provided in recording and processing this data Thank You, Questions?