Recognition and reconstruction of coherent energy with application to deep seismic reflection data

Baan, Mirko van der; Ambert, Paul

doi:10.1190/1.1444763

Cited by 14 publications

(12 citation statements)

References 18 publications

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“…Both f ‐ k and τ ‐ p filters are often useful to suppress correlated noise such as ground roll or water column reverberations in large data volumes. Time‐ and space‐variant f ‐ k [ Duncan and Beresford , 1994] and τ ‐ p filters [e.g., van der Baan and Paul , 2000] are more sensitive to signal variations and can be employed in a data‐adaptive manner.…”

Section: Introductionmentioning

confidence: 99%

Frequency‐dependent phase coherence for noise suppression in seismic array data

2007

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[1] We introduce a coherence measure for seismic signal enhancement through incoherent noise attenuation. Our processing tool is designed for densely spaced arrays and identifies signals by their coherent appearance. The approach is based on the determination of the lateral phase coherence as function of distance, time, frequency, and slowness. The coherence is derived from the local phases of neighboring stations which we obtain from analytic signals through the S-transform. The coherence is used to attenuate incoherent components in the time-frequency representations of the seismic records. No waveforms are averaged in our approach to maintain local amplitude information. This way we construct a data-adaptive filter which enhances coherent signals using the frequency-dependent and slowness-dependent phase coherence. We explain the method and show its abilities and limitations with theoretical test data. Furthermore, we have selected an ocean bottom seismometer (OBS) record section from NW-Spain and a teleseismic event registered at Spanish broadband stations to show the filter performance on real array data. Incoherent noise has been attenuated in all cases to enable a less ambiguous signal detection. In our last example, the filter also reveals weak conversions/reflections at the 410-km and 660-km discontinuities which are hardly visible in the unfiltered input data.Citation: Schimmel, M., and J. Gallart (2007), Frequency-dependent phase coherence for noise suppression in seismic array data,

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Section: Introductionmentioning

confidence: 99%

Frequency‐dependent phase coherence for noise suppression in seismic array data

2007

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“…Va n der Baan and Paul (2000) have shown that the application of Gaussian statistics on local amplitude spectra after the application of a PCA allows for an efficient estimate of the presence of reflections and therefore their extraction. They used a very simple procedure to extract the desired reflections.…”

Section: An Examplementioning

confidence: 99%

Neural networks in geophysical applications

Baan

Jutten²

2000

GEOPHYSICS

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Neural networks are increasingly popular in geophysics. Because they are universal approximators, these tools can approximate any continuous function with an arbitrary precision. Hence, they may yield important contributions to finding solutions to a variety of geophysical applications.However, knowledge of many methods and techniques recently developed to increase the performance and to facilitate the use of neural networks does not seem to be widespread in the geophysical community. Therefore, the power of these tools has not yet been explored to their full extent. In this paper, techniques are described for faster training, better overall performance, i.e., generalization, and the automatic estimation of network size and architecture.

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“…However, these techniques do not consider that most of the energy of seismic signals is usually concentrated in a narrow slowness range. In this specific case, when the main signals are clearly separated, the inverse operation can be approximated by a sum of a small set of slowness components (Stoffa et al, 1981;Wenzel et al, 1982;van der Baan and Paul, 2000), or even by a single one (see, e.g., Gallart, 2003, 2004). Although full inverses are more accurate, a "lazy" inverse based on a single slowness component makes the inverse operation trivial, and it suffices for the applications of removing high-energy interfering waves and estimating low-energy waves, on which we focus.…”

Section: Filtering and Synthesismentioning

confidence: 99%

“…Widespread LSST applications are local-adaptive filters, e.g., the spatial-averaging filters on degree-of-polarization measures Gallart, 2003, 2004), or the adaptive f-k filters of Duncan and Beresford (1994); and instantaneous slowness measures (Milkereit, 1987;van der Baan and Paul, 2000;Hu and Stoffa, 2009). Hence, the LSST is found in leading-edge algorithms, such as CRS, to estimate the first-order-approximation parameters or stereotomography (Billette and Lambaré, 1998;Billette et al, 2003;Lambaré, 2008) to measure local slope in the event picking.…”

Section: Introductionmentioning

confidence: 98%

“…These techniques can be classified into several broad categories: (1) pie-slice f-k (frequency-wavenumber) filters (see, e.g., Embree et al, 1963;Yilmaz and Doherty, 2001); (2) filters in the τ-p (intercept-time, slope, or slowness) domain, also called linear Radon or slant-stack domains (Stoffa et al, 1981;Durrani and Bisset, 1984;Turner, 1990;Deans, 1993;Yilmaz and Doherty, 2001;Wilson and Guitton, 2007);…”

Section: Introductionmentioning

confidence: 99%

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Window length selection for optimum slowness resolution of the local-slant-stack transform

2012

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One of the most critical decisions in the design of a localslant-stack transform (LSST) is the selection of its aperture, or more precisely, the selection of the appropriate number of traces and their weighting coefficients for each slant stack. The challenge is to achieve a good compromise between the slowness and the spatial resolution. Conventionally, the window size is chosen in a more intuitive manner by visual inspection and some limited tests. We analyzed the LSST to establish rigorous criteria for the window selection to achieve the optimum slowness and spatial resolution in the transformed domain for a given data set. For this purpose, we estimated the slowness resolution in the LSST domain as a function of the spatial-window bandwidth and of the spectral characteristics of the waves. For a wave with a given bandpass spectrum, the slowness resolution, the stopband attenuation, and the wavefront-tracking capability are determined by the spatial window. For narrowband signals, the spatial window must be larger than the stopband bandwidth divided by the desired slowness resolution and the central frequency of the band. For wideband signals, the window length is determined by the lowest frequency components. Much longer windows can only be used when the slowness and the amplitude variations of the wavefront trajectories are small. We validated our approach with a synthetic example and applied it to a wideangle seismic profile to show the filter performance on real data in which the LSST-window length is determined in an automatic, data-adaptive manner.

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Recognition and reconstruction of coherent energy with application to deep seismic reflection data

Cited by 14 publications

References 18 publications

Frequency‐dependent phase coherence for noise suppression in seismic array data

Frequency‐dependent phase coherence for noise suppression in seismic array data

Neural networks in geophysical applications

Window length selection for optimum slowness resolution of the local-slant-stack transform

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