�ݺ�ߣshows by User: Henry

�ݺ�ߣshows by User: Henry_cu / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: Henry_cu / Fri, 16 Jan 2015 15:38:02 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: Henry_cu A review of time?�\frequency methods /slideshow/a-review-of-timefrequency-methods/43599538 o2qrod4rikeulap865qp-signature-d80fe0d758cfe72fb528a5c08467e07b58d8be8f603adb06d8548d15ab015e6b-poli-150116153803-conversion-gate02
Spectral estimation, and corresponding time-frequency representation for nonstationary signals, is a cornerstone in geophysical signal processing and interpretation. The last 10�C15 years have seen the development of many new high-resolution decompositions that are often fundamentally different from Fourier and wavelet transforms. These conventional techniques, like the short-time Fourier transform and the continuous wavelet transform, show some limitations in terms of resolution (localization) due to the trade-off between time and frequency localizations and smearing due to the finite size of the time series of their template. Well-known techniques, like autoregressive methods and basis pursuit, and recently developed techniques, such as empirical mode decomposition and the synchrosqueezing transform, can achieve higher time-frequency localization due to reduced spectral smearing and leakage. We first review the theory of various established and novel techniques, pointing out their assumptions, adaptability, and expected time-frequency localization. We illustrate their performances on a provided collection of benchmark signals, including a laughing voice, a volcano tremor, a microseismic event, and a global earthquake, with the intention to provide a fair comparison of the pros and cons of each method. Finally, their outcomes are discussed and possible avenues for improvements are proposed.]]>
Spectral estimation, and corresponding time-frequency representation for nonstationary signals, is a cornerstone in geophysical signal processing and interpretation. The last 10�C15 years have seen the development of many new high-resolution decompositions that are often fundamentally different from Fourier and wavelet transforms. These conventional techniques, like the short-time Fourier transform and the continuous wavelet transform, show some limitations in terms of resolution (localization) due to the trade-off between time and frequency localizations and smearing due to the finite size of the time series of their template. Well-known techniques, like autoregressive methods and basis pursuit, and recently developed techniques, such as empirical mode decomposition and the synchrosqueezing transform, can achieve higher time-frequency localization due to reduced spectral smearing and leakage. We first review the theory of various established and novel techniques, pointing out their assumptions, adaptability, and expected time-frequency localization. We illustrate their performances on a provided collection of benchmark signals, including a laughing voice, a volcano tremor, a microseismic event, and a global earthquake, with the intention to provide a fair comparison of the pros and cons of each method. Finally, their outcomes are discussed and possible avenues for improvements are proposed.]]> Fri, 16 Jan 2015 15:38:02 GMT /slideshow/a-review-of-timefrequency-methods/43599538 Henry_cu@slideshare.net(Henry_cu) A review of time?�\frequency methods Henry_cu Spectral estimation, and corresponding time-frequency representation for nonstationary signals, is a cornerstone in geophysical signal processing and interpretation. The last 10�C15 years have seen the development of many new high-resolution decompositions that are often fundamentally different from Fourier and wavelet transforms. These conventional techniques, like the short-time Fourier transform and the continuous wavelet transform, show some limitations in terms of resolution (localization) due to the trade-off between time and frequency localizations and smearing due to the finite size of the time series of their template. Well-known techniques, like autoregressive methods and basis pursuit, and recently developed techniques, such as empirical mode decomposition and the synchrosqueezing transform, can achieve higher time-frequency localization due to reduced spectral smearing and leakage. We first review the theory of various established and novel techniques, pointing out their assumptions, adaptability, and expected time-frequency localization. We illustrate their performances on a provided collection of benchmark signals, including a laughing voice, a volcano tremor, a microseismic event, and a global earthquake, with the intention to provide a fair comparison of the pros and cons of each method. Finally, their outcomes are discussed and possible avenues for improvements are proposed. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/o2qrod4rikeulap865qp-signature-d80fe0d758cfe72fb528a5c08467e07b58d8be8f603adb06d8548d15ab015e6b-poli-150116153803-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Spectral estimation, and corresponding time-frequency representation for nonstationary signals, is a cornerstone in geophysical signal processing and interpretation. The last 10�C15 years have seen the development of many new high-resolution decompositions that are often fundamentally different from Fourier and wavelet transforms. These conventional techniques, like the short-time Fourier transform and the continuous wavelet transform, show some limitations in terms of resolution (localization) due to the trade-off between time and frequency localizations and smearing due to the finite size of the time series of their template. Well-known techniques, like autoregressive methods and basis pursuit, and recently developed techniques, such as empirical mode decomposition and the synchrosqueezing transform, can achieve higher time-frequency localization due to reduced spectral smearing and leakage. We first review the theory of various established and novel techniques, pointing out their assumptions, adaptability, and expected time-frequency localization. We illustrate their performances on a provided collection of benchmark signals, including a laughing voice, a volcano tremor, a microseismic event, and a global earthquake, with the intention to provide a fair comparison of the pros and cons of each method. Finally, their outcomes are discussed and possible avenues for improvements are proposed.

A review of time�ĥ�M�\requency methods from UT Technology

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0 Automated seismic-to-well ties? /slideshow/eage2012-dtw-hvdb/32620396 eage2012dtwhvdb-140322204609-phpapp01
Automatic seismic to well tying. See the video for the maple leaf to signal conversion here: https://www.youtube.com/watch?v=lmWDgTrsgw4 ]]>
Automatic seismic to well tying. See the video for the maple leaf to signal conversion here: https://www.youtube.com/watch?v=lmWDgTrsgw4 ]]> Sat, 22 Mar 2014 20:46:09 GMT /slideshow/eage2012-dtw-hvdb/32620396 Henry_cu@slideshare.net(Henry_cu) Automated seismic-to-well ties? Henry_cu Automatic seismic to well tying. See the video for the maple leaf to signal conversion here: https://www.youtube.com/watch?v=lmWDgTrsgw4 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/eage2012dtwhvdb-140322204609-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Automatic seismic to well tying. See the video for the maple leaf to signal conversion here: https://www.youtube.com/watch?v=lmWDgTrsgw4

Automated seismic-to-well ties? from UT Technology

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0 Time-Frequency Representation of Microseismic Signals using the SST /slideshow/cseg-sst-henry2013/26677305 csegssthenry2013-130929210307-phpapp01
Resonance frequencies could provide useful information on the deformation occurring during fracturing experiments or CO2 management, complementary to the microseismic events distribution. An accurate time-frequency representation is of crucial importance to interpret the cause of resonance frequencies during microseismic experiments. The popular methods of Short-Time Fourier Transform (STFT) and wavelet analysis have limitations in representing close frequencies and dealing with fast varying instantaneous frequencies and this is often the nature of microseismic signals. The synchrosqueezing transform (SST) is a promising tool to track these resonant frequencies and provide a detailed time-frequency representation. Here we apply the synchrosqueezing transform to microseismic signals and also show its potential to general seismic signal processing applications.]]>
Resonance frequencies could provide useful information on the deformation occurring during fracturing experiments or CO2 management, complementary to the microseismic events distribution. An accurate time-frequency representation is of crucial importance to interpret the cause of resonance frequencies during microseismic experiments. The popular methods of Short-Time Fourier Transform (STFT) and wavelet analysis have limitations in representing close frequencies and dealing with fast varying instantaneous frequencies and this is often the nature of microseismic signals. The synchrosqueezing transform (SST) is a promising tool to track these resonant frequencies and provide a detailed time-frequency representation. Here we apply the synchrosqueezing transform to microseismic signals and also show its potential to general seismic signal processing applications.]]> Sun, 29 Sep 2013 21:03:07 GMT /slideshow/cseg-sst-henry2013/26677305 Henry_cu@slideshare.net(Henry_cu) Time-Frequency Representation of Microseismic Signals using the SST Henry_cu Resonance frequencies could provide useful information on the deformation occurring during fracturing experiments or CO2 management, complementary to the microseismic events distribution. An accurate time-frequency representation is of crucial importance to interpret the cause of resonance frequencies during microseismic experiments. The popular methods of Short-Time Fourier Transform (STFT) and wavelet analysis have limitations in representing close frequencies and dealing with fast varying instantaneous frequencies and this is often the nature of microseismic signals. The synchrosqueezing transform (SST) is a promising tool to track these resonant frequencies and provide a detailed time-frequency representation. Here we apply the synchrosqueezing transform to microseismic signals and also show its potential to general seismic signal processing applications. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/csegssthenry2013-130929210307-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Resonance frequencies could provide useful information on the deformation occurring during fracturing experiments or CO2 management, complementary to the microseismic events distribution. An accurate time-frequency representation is of crucial importance to interpret the cause of resonance frequencies during microseismic experiments. The popular methods of Short-Time Fourier Transform (STFT) and wavelet analysis have limitations in representing close frequencies and dealing with fast varying instantaneous frequencies and this is often the nature of microseismic signals. The synchrosqueezing transform (SST) is a promising tool to track these resonant frequencies and provide a detailed time-frequency representation. Here we apply the synchrosqueezing transform to microseismic signals and also show its potential to general seismic signal processing applications.

Time-Frequency Representation of Microseismic Signals using the SST from UT Technology

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0 Short-time homomorphic wavelet estimation /Henry_cu/2012-geo-conv-sthweherrera-v2 2012geoconvsthweherrerav2-130113130528-phpapp02
Wavelet estimation plays an important role in many seismic processes like impedance inversion, amplitude versus offset (AVO) and full waveform inversion (FWI). Statistical methods of wavelet estimation away from well control are a desirable tool to support seismic signal processing. One of these methods based on Homomorphic analysis has long intrigued as a potentially elegant solution to the wavelet estimation problem. Yet a successful implementation has proven difficult. We propose here a method based short-time homomorphic analysis which includes elements of the classical cepstrum analysis and log spectral averaging. Our proposal increases the number of segments, thus reducing estimation variances. Results show good performance on realistic synthetic examples.]]>
Wavelet estimation plays an important role in many seismic processes like impedance inversion, amplitude versus offset (AVO) and full waveform inversion (FWI). Statistical methods of wavelet estimation away from well control are a desirable tool to support seismic signal processing. One of these methods based on Homomorphic analysis has long intrigued as a potentially elegant solution to the wavelet estimation problem. Yet a successful implementation has proven difficult. We propose here a method based short-time homomorphic analysis which includes elements of the classical cepstrum analysis and log spectral averaging. Our proposal increases the number of segments, thus reducing estimation variances. Results show good performance on realistic synthetic examples.]]> Sun, 13 Jan 2013 13:05:28 GMT /Henry_cu/2012-geo-conv-sthweherrera-v2 Henry_cu@slideshare.net(Henry_cu) Short-time homomorphic wavelet estimation Henry_cu Wavelet estimation plays an important role in many seismic processes like impedance inversion, amplitude versus offset (AVO) and full waveform inversion (FWI). Statistical methods of wavelet estimation away from well control are a desirable tool to support seismic signal processing. One of these methods based on Homomorphic analysis has long intrigued as a potentially elegant solution to the wavelet estimation problem. Yet a successful implementation has proven difficult. We propose here a method based short-time homomorphic analysis which includes elements of the classical cepstrum analysis and log spectral averaging. Our proposal increases the number of segments, thus reducing estimation variances. Results show good performance on realistic synthetic examples. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2012geoconvsthweherrerav2-130113130528-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Wavelet estimation plays an important role in many seismic processes like impedance inversion, amplitude versus offset (AVO) and full waveform inversion (FWI). Statistical methods of wavelet estimation away from well control are a desirable tool to support seismic signal processing. One of these methods based on Homomorphic analysis has long intrigued as a potentially elegant solution to the wavelet estimation problem. Yet a successful implementation has proven difficult. We propose here a method based short-time homomorphic analysis which includes elements of the classical cepstrum analysis and log spectral averaging. Our proposal increases the number of segments, thus reducing estimation variances. Results show good performance on realistic synthetic examples.

Short-time homomorphic wavelet estimation from UT Technology

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0 https://cdn.slidesharecdn.com/profile-photo-Henry_cu-48x48.jpg?cb=1623276713 Developing signal and image processing techniques in Non-Destructive Testing. https://cdn.slidesharecdn.com/ss_thumbnails/o2qrod4rikeulap865qp-signature-d80fe0d758cfe72fb528a5c08467e07b58d8be8f603adb06d8548d15ab015e6b-poli-150116153803-conversion-gate02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/a-review-of-timefrequency-methods/43599538 A review of time?�\freq... https://cdn.slidesharecdn.com/ss_thumbnails/eage2012dtwhvdb-140322204609-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/eage2012-dtw-hvdb/32620396 Automated seismic-to-w... https://cdn.slidesharecdn.com/ss_thumbnails/csegssthenry2013-130929210307-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/cseg-sst-henry2013/26677305 Time-Frequency Represe...