Tracking algorithm for linear array signal processor for Fabry-Perot cross-correlation pattern and method of using same

• US 7,639,368 B2
• Filed: 09/11/2006
• Issued: 12/29/2009
• Est. Priority Date: 09/13/2005
• Status: Active Grant

• Current Assignee: Halliburton Energy Services Incorporated (Halliburton Company)
• Original Assignee: Halliburton Energy Services Incorporated (Halliburton Company)
• Inventors: Needham, David B.
• Primary Examiner(s): Chowdhury; Tarifur
• Assistant Examiner(s): COOK, JONATHON
• Application Number:
  US11/519,037
• Publication Number:
  US 20070064241A1
• Time in Patent Office: 1,205 Days
• Field of Search: 356/505, 356/506
• US Class Current: 356/505
• CPC Class Codes: G01B 11/14 for measuring distance or clearance between spaced objects or spaced apertures G01B11/26 takes precedence; rangefinders G01C3/00
• CPC Class Codes: G01B 2290/20 Dispersive element for generating dispersion
• CPC Class Codes: G01B 2290/25 Fabry-Perot in interferometer, e.g. etalon, cavity
• CPC Class Codes: G01B 9/02083 characterised by particular signal processing and presentation
• CPC Class Codes: G01B 9/0209 Low-coherence interferometers

Abstract

An algorithm and method for calculating an interferometric gap is disclosed that comprises providing an interferometric sensor having a first gap and an interferometric correlation element having a second gap placed in series with the first gap. A correlation burst waveform is generated having a plurality of features wherein the shape of the burst waveform evolves across the range of the second gap. Means are provided for tracking the features across the entire range of gaps and determining the dominant peak or dominant valley to determine the first gap.

Claims

1. A method for calculating an interferometric gap of an interferometric sensor from a cross-correlation of the interferometeric sensor and an interferometric correlation element, said method comprising:

providing an interferometric sensor having a first gap;

providing an interferometric correlation element having a substantially linear varying second gap, said second gap comprising a dispersive material placed in series with said first gap;

generating a correlation burst waveform having a plurality of features including peaks and valleys from a cross-correlation of said sensor and said correlation element wherein the shape of the burst waveform evolves due to dispersion across the range of the second gap;

collecting data on the correlation burst waveform;

tracking said features across an entire range of the first gap and determining a dominant peak or a dominant valley;

computing a peak margin, where the peak margin is a ratio of a second largest magnitude peak to the dominant peak subtracted from 100 percent; and

using the dominant peak or dominant valley and the computed peak margin to determine a length of the first gap that the dominant peak or dominant valley represents.

2. The method of claim 1 further comprising:

filtering said data to remove noise; and

computing the average intensity of the waveform through the centerline of the waveform measured in the Y-Axis.

3. The method of claim 1 wherein said peak margin is computed by an algorithm, where the algorithm is Peak Margin=100% −

abs(max(P.L2, P.R2)−

Avg)/P.C, where;

abs is a function that takes the absolute value of a number;

max is a function that chooses the largest value from a set of values;

P.L2 is a magnitude of a peak to the left of the dominant peak in the correlation burst waveform;

P.R2 is a magnitude of a peak to the right of the dominant peak in the correlation burst waveform;

Avg. is the average intensity of the waveform through the centerline of the waveform measured in the Y-Axis; and

P.C. is the magnitude of the dominant peak in the correlation burst waveform.

4. The method of claim 1 further comprising computing a valley margin which is a ratio of the second largest magnitude valley to the dominant valley subtracted from 100%.

5. The method of claim 4 wherein said valley margin is computed by an algorithm, where the algorithm is Valley Margin=100%−

abs(max(V.L2, V.R2)−

Avg/V.C, where abs is a function that takes the absolute value of a number;

max is a function that chooses the largest value from a set of values;

V.L2 is a magnitude of a valley to the left of the dominant valley in the correlation burst waveform;

V.R2 is a magnitude of a valley to the right of the dominant valley in the correlation burst waveform;

Avg. is the average intensity of the waveform through the centerline of the waveform measured in the Y-Axis; and

V.C. is a magnitude of the dominant valley in the correlation burst waveform.

6. The method of claim 4 further comprising computing a BurstType to identify the dominant peak or dominant valley.

7. The method of claim 6 wherein said BurstType is computed an algorithm, where the algorithm is BurstType=Peak Margin−

Valley Margin+0.5 so that when said BurstType is closer to zero, it is an indication of a dominant valley and when said BurstType is closer to one, it is an indication of a dominant peak and when the BurstType is close to 0.5, it is an indication of the nearing of a transition point.

8. The method of claim 7 wherein said first gap is comprised of a dispersive material, a non-dispersive material, or a vacuum.

9. The method of claim 8 wherein said second gap is comprised of a transparent oxide material.

10. The method of claim 9 wherein said correlation element is a wedge and the readout is the wedge attached to a CCD array.

11. A method for calculating a magnitude of an interferometric gap from a cross-correlation of an interferometeric sensor and an interferometric correlation element, said method comprising:

providing a Fabry-Perot interferometric sensor having a first gap;

providing a wedge or Fizeau interferometric correlation element having a second gap comprising a dispersive material, said correlation element placed in series with said Fabry-Perot sensor;

generating a correlation burst waveform having a plurality of features including peaks and valleys from the cross-correlation of said sensor and said correlation element wherein the shape of the burst waveform evolves across the range of first gaps as the first gap changes;

tracking said features across an entire range of first gaps and determining a dominant peak or a dominant valley;

computing a peak margin with a first algorithm, where the first algorithm is Peak Margin=100%−

abs(max(P.L2, P.R2)−

Avg)/P.C, where;

abs is a function that takes the absolute value of a number;

max is a function that chooses the largest value from a set of values;

P.L2 is a magnitude of a peak to the left of the dominant peak in the correlation burst waveform;

P.R2 is a magnitude of a peak to the right of the dominant peak in the correlation burst waveform;

Avg. is the average intensity of the waveform through the centerline of the correlation burst waveform measured in the Y-Axis; and

P.C. is the magnitude of the dominant peak in the correlation burst waveform; and

using the dominant peak or dominant valley to determine a magnitude of the first gap that the dominant peak or dominant valley represents.

12. The method of claim 11 further comprising:

collecting data on the correlation burst waveform;

computing a valley margin by a second algorithm, where the second algorithm is Valley Margin=100%−

abs(max(V.L2, V.R2)−

Avg/V.C, where

abs is a function that takes the absolute value of a number;

max is a function that chooses the largest value from a set of values;

V.L2 is a magnitude of a valley to the left of the dominant valley in the correlation burst waveform;

V.R2 is a magnitude of a valley to the right of the dominant valley in the correlation burst waveform;

Avg. is the average intensity of the waveform through the centerline of the waveform measured in the Y-Axis; and

V.C. is a magnitude of the dominant valley in the correlation burst waveform; and

computing a BurstType by a third algorithm, where the third algorithm is BurstType=Peak Margin−

Valley Margin+0.5 so that when said BurstType is closer to zero, it is an indication of the dominant valley and when said BurstType is closer to one, it is an indication of a dominant peak and when said BurstType is close to 0.5, it is an indication of the nearing of a transition point.

13. The method of claim 12 wherein said first gap is comprised of a dispersive material, a non-dispersive material, or a vacuum.

14. The method of claim 13 wherein said second gap is comprised of a transparent oxide material.

15. The method of claim 14 wherein said second gap is a substantially linear varying gap having a continuum of gaps including gaps that are greater than, less than, or equal to said first gap.

16. A linear array signal processor (LASP) comprising:

an interferometric sensor having a first gap;

an interferometric correlation element having a second gap comprised of a dispersive material, said correlation element placed in series with said first sensor;

a CCD array attached to said correlation element to read out a correlation pattern;

means for identifying and collecting data on a correlation burst waveform having a plurality of features including peaks and valleys from the cross-correlation of said sensor and said correlation element wherein the shape of the burst waveform evolves across the range of first gaps as the first gap changes;

means for computing a peak margin for the correlation burst waveform, where the peak margin is a ratio of the magnitude of the second largest magnitude peak to magnitude of the dominant peak subtracted from 100%;

means for tracking said features across an entire range of first gaps and determining the dominant peak or dominant valley; and

means for calculating the magnitude of the first gap represented by the dominant peak.

17. The linear array signal processor of claim 16 wherein said means for computing peak margin comprises:

means for filtering said collected data to remove noise; and

means for computing the average intensity of the waveform through the centerline of the waveform measured in the Y-Axis.

18. The linear array signal processor of claim 17 wherein said peak margin is computed by a first algorithm, where the first algorithm is Peak Margin=100%−

abs(max(P.L2, P.R2)−

Avg)/P.C, where;

abs is a function that takes the absolute value of a number;

max is a function that chooses the largest value from a set of values;

P.L2 is a magnitude of a peak to the left of the dominant peak in the correlation burst waveform;

P.R2 is a magnitude of a peak to the right of the dominant peak in the correlation burst waveform;

Avg. is the average intensity of the waveform through the centerline of the correlation burst waveform measured in the Y-Axis; and

P.C. is the magnitude of the dominant peak in the correlation burst waveform.

19. The linear array signal processor of claim 18 further comprising:

means for computing a valley margin for the correlation burst waveform, which is a ratio of the magnitude of a second largest valley to the dominant valley subtracted from 100%.

20. The linear array signal processor of claim 19 wherein said valley margin is computer by a second algorithm, where the second algorithm is Valley Margin=100%−

abs(max(V.L2, V.R2)−

Avg/V.C, where

abs is a function that takes the absolute value of a number;

max is a function that chooses the largest value from a set of values;

V.L2 is a magnitude of a valley to the left of the dominant valley in the correlation burst waveform;

V.R2 is a magnitude of a valley to the right of the dominant valley in the correlation burst waveform;

Avg. is the average intensity of the waveform through the centerline of the waveform measured in the Y-Axis; and

V.C. is a magnitude of the dominant valley in the correlation burst waveform.

21. The linear array signal processor of claim 20 further comprising:

means for computing a BurstType to identify the dominant peak or dominant valley.

22. The linear array signal processor of claim 21 wherein said BurstType is computed by a third algorithm, where the third algorithm is BurstType=Peak Margin−

Valley Margin+0.5 so that when said BurstType is closer to zero, it is an indication of a dominant valley and when said BurstType is closer to one, it is an indication of a dominant peak and when the BurstType is close to 0.5, it is an indication of the nearing of a transition point.

23. The linear array signal processor of claim 22 wherein said first gap is comprised of a dispersive material, a non-dispersive material, or a vacuum.

24. The linear array signal processor of claim 23 wherein said second gap is comprised of a transparent oxide material.