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Locating Trapped Miners Using Time Reversal Mirrors Sherif M. Hanafy Weiping CaoKim McCarter Gerard T. Schuster November 12, 2008.

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Presentation on theme: "Locating Trapped Miners Using Time Reversal Mirrors Sherif M. Hanafy Weiping CaoKim McCarter Gerard T. Schuster November 12, 2008."— Presentation transcript:

1 Locating Trapped Miners Using Time Reversal Mirrors Sherif M. Hanafy Weiping CaoKim McCarter Gerard T. Schuster November 12, 2008

2 Motivation RTM Methodology Field Examples Practical Problems Super-resolution Tests Summary and Conclusions Outline

3 Motivation RTM Methodology Field Examples Practical Problems Super-resolution Tests Summary and Conclusions Outline

4 Motivation Problem: Miners are lost in a mine collapse, death could happens Proposed Solution: Time Reversal Mirror (TRM) with super resolution and super stacking properties

5 Motivation RTM Methodology Field Examples Practical Problems Super-resolution Tests Summary and Conclusions Outline

6 Step # 1: before the collapse G1 …………………………………… Gn Receiver Line Ground Surface Subsurface Mine 3 Step RTM Methodology Geophones are planted on the ground surface above the mine. Select some communication points inside the mine From each communication point a band-limited natural Green’s function is recorded

7 Step # 2: get the SOS call Receiver Line Ground Surface Subsurface Mine After a collapse occurs, G1G2G3 trapped miners should go to the nearest communication point and hit the mine wall at this point This (SOS) call will be recorded by the geophones on the ground surface 3 Step RTM Methodology

8 Does the recorded SOS looks like one of our previously recorded band-limited calibration Green’s functions? G1G1 G2G2 G3G3 ……….GnGn Recorded SOS NO YesNO The location of the trapped miners is the location of the calibration Green’s functions that best match the recorded SOS We can use a pattern matching approach between the recorded SOS and the calibration Green’s function gathers Step # 3: where are the trapped miner(s)? 3 Step RTM Methodology

9 Mathematically, better match means higher d & g dot product value Dot product results Recorded SOS call Band-limited Green’s function Refers to the location of the communication point 3 Step RTM Methodology Time Reversal Mirror equation Post Stack Migration

10 ……....... ………....... ………… Subsurface Mine Location of trapped miners G1G2G3 3 Step RTM Methodology

11 Motivation RTM Methodology Field Examples Practical Problems Super-resolution Tests Summary and Conclusions Outline

12 Number of receivers = 120 @ 1m interval Number of communication points = 25 Comm. point interval; Points 1- 6 & 20 – 25 = 4 m Points 6 – 20 = 0.5 m Distance from receiver line to tunnel = 35 m U of U Test Field Examples Number of receivers = 120 @ 0.5 m interval Number of communication points = 25 @ 0.5 & 0.75 m intervals Distances from receiver-line to tunnel are 30 & 45 m, respectively Tucson, AZ Test

13 Generating both Green’s function and SOS call U of U Test

14 Tucson, Arizona Test Generating both Green’s function and SOS call

15 Sample results from U of U and Tucson Tests Dot Product Results X (m) Normalized m(x,0) X (m) Normalized m(x,0)

16 Motivation RTM Methodology Field Examples Practical Problems Super-resolution Tests Summary and Conclusions Outline

17 Amplitude Time Shift Unknown SOS Excitation Time We use a simple time shift test Excitation Time

18 Unknown SOS Excitation Time Excitation Time Location of Trapped Miner -0.25 0.25 0 45 1 X (m) Time Shift (ms) Normalized Amplitude 0.0 45.0 0.0 45.0 U of U TestTucson, AZ Test Mine Depth = 35 mMine Depth = 45 m

19 Low S/N ratio of the SOS call We generated a random noise CSG This random-noise CSG is added to the recorded SOS The results are then used in our calculations +=

20 Results with Random Noise Results without adding noiseResults with adding noiseNew S/N U of U1:1738 Tucson1:2670 Normalized m(x,0) X (m) Normalized m(x,0) X (m) Normalized m(x,0) X (m) Normalized m(x,0) X (m)

21 Motivation RTM Methodology Field Examples Practical Problems Super-resolution Tests Summary and Conclusions Outline

22 Rayleigh Spatial Resolution Spatial resolution is defined by Sheriff (1991) as the ability to separate two features that are very close together, i.e., the minimum separation of two bodies before their individual identities are lost. Ground Surface 2L Z

23 Expected Spatial Resolution U of U Test Tucson Test Tunnel # 1Tunnel # 2 10 m Z35 m30 m45 m L60 m30 m xx Rayleigh resolution 3 m5 m7.5 m

24 Can Scatterers Beat the Resolution Limit? Recorded shot gathers (SOS & G) are divided into: - Full aperture & direct arrivals- Full aperture & scattered arrivals - Half aperture & direct arrivals - Half aperture & scattered arrivals

25 Super-Resolution 1.Spatial resolution of correlating traces with scatterer-only events is much higher. 2.Spatial resolution of correlating traces with direct-only events depends on the aperture width. X (m) Results using traces with only - Direct waves, full aperture width - Direct waves, half aperture width - Scattered waves, full aperture width - Scattered waves, half aperture width

26 Expected Spatial Resolution U of U Test Tucson Test Tunnel # 1Tunnel # 2 10 m Z35 m30 m45 m L60 m30 m xx 3 m5 m7.5 m Rayleigh resolution 0.5 m0.5 m0.75 m Scatterers resolution Our approach shows a resolution 6 – 10 times better than the expected Rayleigh resolution limit.

27 Motivation RTM Methodology Field Examples Practical Problems Super-resolution Tests Summary and Conclusions Outline

28 We have successfully introduced a TRM method to locate trapped miners in a collapsed mine Two field tests are made to validate the proposed TRM method Field tests show that TRM can successfully locate trapped miners with signal-to-noise ratio as low as 0.0005 Summary and Conclusions

29 Super Resolution – Using traces with scatterer only improve data resolution 6- 10 times – Aperture width does not change the scatterer only results, while direct only waves is highly affected by the aperture width

30 To the best of our knowledge, our work is the first time super-stack and super-resolution properties are validated with field seismic data. Summary and Conclusions For the first time in EM waves, Lerosey et al. (2007) succeeded to get a resolution of /30

31 Implication Hydro-Frac Monitoring – Time reversal mirrors (TRM) approach has super stack property – No velocity model is required – Small aperture width gives good results If we have the exact velocity model – Reverse time migration (RTM) has both super-stack and super-resolution properties. Increasing the RTM resolution by 3-7 times deserves the effort of finding the exact velocity model.

32 Thank You

33 Motivation and Introduction Methodology Field Examples – University of Utah test – Tucson, Arizona test Practical Problems – Time shift test – Super-stack results – Trapped between two communication points – Two groups are trapped – Complex example Super-resolution Tests Summary and Conclusions Outline

34 CP SOS Miners are trapped between two CP CP SOS CP SOS CP SOS Example from U of U test

35 Motivation and Introduction Methodology Field Examples – University of Utah test – Tucson, Arizona test Practical Problems – Time shift test – Super-stack results – Trapped between two communication points – Two groups are trapped – Complex example Super-resolution Tests Summary and Conclusions Outline

36 Two groups of miners sending SOS call CP SOS 1SOS 2 Example from U of U test

37 Motivation and Introduction Methodology Field Examples – University of Utah test – Tucson, Arizona test Practical Problems – Time shift test – Super-stack results – Trapped between two communication points – Two groups are trapped – Complex example Super-resolution Tests Summary and Conclusions Outline

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