Processing of the Field Data using Predictive Deconvolution Yuqing Chen Aydar Zaripov Dias Urozayev King Abdullah University of Science and Technology 08/12/2015

Outline 1) Experiment Description 2) Objective 3) Processing Methodology 4) Results and Interpretation 5) Conclusions

Outline 1) Experiment Description 2) Objective 3) Processing Methodology 4) Results and Interpretation 5) Conclusions

Area of Study Seismic profile AB is located along the road between the stadium and the construction site. B A Construction Construction

Outline 1) Experiment Description 2) Objective 3) Processing Methodology 4) Results and Interpretation 5) Conclusions

Objectives: Problem : noise (harmonic) Solution : Use Prediction deconvolution to eliminate harmonic noise meanwhile compress wavelet (improve resolution)

Outline 1) Experiment Description 2) Objective 3) Processing Methodology 4) Results and Interpretation 5) Conclusions

Deconvolution Convolutional model (The forward problem): x(t) – recorded seismogram w(t) – seismic wavelet r(t) – reflectivity Deconvolution (The inverse problem): e(t) – Earth’s impulse respoones

Least Square Deconvolution Minimize the misfit function! x(t) – Recorded seismogram a(t) – Filter y(t) – Actually output d(t) – Desired output

Predictive Deconvolution Noise attenuated Periodic event (multiple, harmonic noise) Prediction Filter 10

Predictive Deconvolution : Predicted time-advanced seismogram : Actual time-advanced seismogram x(t) : current and past seismogram a(t) : Predict Filter     We can predict the predictable part of the seismogram like multiples and harmonic noise, etc.

Outline 1) Experiment Description 2) Objective 3) Processing Methodology 4) Results and Interpretation 5) Conclusions

Results and Interpretation 3 critical parameter in predictive deconvolution: (1) Predict length (2) Filter length (3) Whiten noise A cosine harmonic noise His autocorrelation map

Results and Interpretation Use the second maximum value of autocorrelation map.

Results and Interpretation Second maximum

Results and Interpretation Shot120 (Raw data)

Results and Interpretation Shot120 (After bandpass)

Results and Interpretation Shot120 (After PEF_Predict length: 30)

Results and Interpretation Shot120 (After PEF_Predict length: 2)

Results and Interpretation Shot120 (After PEF_Predict length: 60)

Results and Interpretation Shot 40 (After Bandpass)

Results and Interpretation Shot 40 (After PEF)

Results and Interpretation 3 critical parameter in predictive deconvolution: (1) Predict length (2) Filter length (3) Whiten noise The filter length should at least longer than the predict length 2. We chose the filter length also from the spectrum. Seismic trace

Results and Interpretation 50 200 100 300 150 400

Results and Interpretation 3 critical parameter in predictive deconvolution: (1) Predict length (2) Filter length (3) Whiten noise Whitening 0.01%

Results and Interpretation 3 critical parameter in predictive deconvolution: (1) Predict length (2) Filter length (3) Whiten noise Whitening 0.05%

Conclusion Predictable noise is mitigated Improved temporal resolution Minimum phase assumption Random reflectivity assumption High level of noise restricts the implementation

Recommendations 2D predictive deconvolution Compute different PEF’s for different segments of a seismogram Change seismogram to minimum phase before using PEF