Weighted stacking of seismic AVO data using hybrid AB semblance and local similarity

Pan, Deng; Zhou, Hua‐wei; Tan, Yuyang; Chen, Yangkang

doi:10.1190/ice2016-6260208.1

Cited by 3 publications

(18 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…coherent noise). Common criteria employed to estimate these weights involve measuring SNR (Neelamani et al 2006) or correlation (Liu et al 2009;Sanchis & Hanssen 2011;Deng et al 2016) to a previously calculated reference trace. Nonlinear methods such as the median stack (Claerbout & Muir 1973) and the alphatrimmed average (Bednar & Watt 1984;Rashed 2008) help rejecting coherent noise.…”

Section: Introductionmentioning

confidence: 99%

Extracting surface waves, hum and normal modes: time-scale phase-weighted stack and beyond

Ventosa¹,

Schimmel²,

Stutzmann

2017

Geophysical Journal International

View full text Add to dashboard Cite

Stacks of ambient noise correlations are routinely used to extract empirical Green's functions (EGFs) between station pairs. The time-frequency phase-weighted stack (tf-PWS) is a physically intuitive nonlinear denoising method that uses the phase coherence to improve EGF convergence when the performance of conventional linear averaging methods is not sufficient. The high computational cost of a continuous approach to the time-frequency transformation is currently a main limitation in ambient noise studies. We introduce the timescale phaseweighted stack (ts-PWS) as an alternative extension of the phase-weighted stack that uses complex frames of wavelets to build a time-frequency representation that is much more efficient and fast to compute and that preserve the performance and flexibility of the tf-PWS. In addition, we propose two strategies: the unbiased phase coherence and the two-stage ts-PWS methods to further improve noise attenuation, quality of the extracted signals and convergence speed. We demonstrate that these approaches enable to extract minor-and major-arc Rayleigh waves (up to the sixth Rayleigh wave train) from many years of data from the GEOSCOPE global network. Finally we also show that fundamental spheroidal modes can be extracted from these EGF.

show abstract

Section: Introductionmentioning

confidence: 99%

Extracting surface waves, hum and normal modes: time-scale phase-weighted stack and beyond

Ventosa¹,

Schimmel²,

Stutzmann

2017

Geophysical Journal International

View full text Add to dashboard Cite

show abstract

“…. 6.31 Gulf of Mexico Reference Trace Profile: An illustration of the referencetrace profiles where a) depicts the near-offset traces as reference traces (Deng et al 2016), and b) depicts the conventionally stacked and detrended traces as reference traces. .…”

Section: 7mentioning

confidence: 99%

“…In the presence of AVO/AZ anomalies, this assumption can lead to destruction of signal in the stack, particularly in the presence of polarity reversals. Deng et al (2016) developed a weighted stacking method to deal with this limitation. Firstly, Deng et al (2016) use local-similarity weighted stacking developed by Liu et al (2009) to reduce noise and enhance the signal of the stack.…”

Section: Stackingmentioning

confidence: 99%

“…Deng et al (2016) developed a weighted stacking method to deal with this limitation. Firstly, Deng et al (2016) use local-similarity weighted stacking developed by Liu et al (2009) to reduce noise and enhance the signal of the stack. Local-similarity weighting requires that each trace in a gather be compared with a reference trace in order to give a metric of local-similarity or synonymously local-correlation.…”

Section: Stackingmentioning

confidence: 99%

“…As just mentioned the conventional stack, in the presence of polarity reversals from AVO/AZ anomalies deteriorate in quality. To account for this, Deng et al (2016) assume that the near offset trace is a suitable representative for a reference trace in the presence of AVO anomaly. Deng et al (2016) successfully apply this methodology and account for AVO anomaly in the stacking procedure whilst also enhancing the SNR.…”

Section: Stackingmentioning

confidence: 99%

See 2 more Smart Citations

A general framework for anisotropic and wavefront moveout parameter analysis

Wilson¹

View full text Add to dashboard Cite

Seismic data processing is essential in the development of seismic images. Seismic images are utilised for interpretation purposes in which insights into the underlying geology and structure are obtained. In the search for resource plays, imaging and interpretation are essential. To correctly image the subsurface, there are many important steps in a typical seismic data processing workflow. Of particular interest in this thesis are the moveout analysis, moveout correction and stacking processes. Moveout analysis involves the determination of moveout parameters that optimise the performance of the moveout correction and stacking processes. Moveout correction utilises the moveout parameters by using them in a model which transforms the spatial and temporal location of reflection impulses in the underlying seismic gathers so that they are optimally aligned in preparation for stacking. Stacking involves applying a weighted sum (typically the arithmetic mean) along the spatial axis of the underlying seismic gather. Limitations in these processes can lead to erroneous results which ultimately affect the quality of the seismic imaging and interpretation. Moveout analysis performance is determined by the presence of amplitude variation with offset (AVO) anomalies, the fold and quality of the data, the choice of moveout approximation and the number of moveout parameters, the method of moveout parameter determination, and the resolution provided by the semblance operator. Moveout correction performance is controlled by choice of moveout approximation and the method of stretch removal utilised. Stacking performance is dependent on fold, noise, quality of moveout correction and moveout stretch. Financial support No financial support was provided to fund this research.

show abstract

Denoising of pre‐stack seismic data using subspace estimation methods

Elumalai

Kumar

Lall

et al. 2018

IET signal process.

View full text Add to dashboard Cite

Denoising is one of the core steps in seismic data processing flow. The seismic gather consists of multiple traces captured at different receivers. A set of receivers observe waves which are reflected from the same reflection point. Those traces need to be grouped together as they contain the same information about the earth subsurface layers. This is done by finding a common mid-point (CMP) between the source and geophones. The time delay between CMP gathered traces are corrected by the normal move out correction method but the individual traces are corrupted by noise. In this paper we, propose a method for denoising individual traces. The set of traces can be modelled as belonging to a low-dimensional subspace of an ambient signal space. This allows for construction of sparse representations of each trace in terms of other traces in the CMP gather. The resulting sparse representations are subsequently utilised to construct approximations of individual traces and thus, noise is suppressed. We constructed, the approximations using orthogonal matching pursuit. We applied proposed method to synthetic and field seismic data, the proposed technique performs better on established benchmarks while capturing the true locations of weak reflections and effectively attenuating the random noise. Nomenclature M number of traces N length of each trace Y seismic trace matrix A noise-free trace matrix G Gaussian noise matrix E impulse noise matrix w source wavelet L length of source wavelet W wavelet dictionary D number of shifted wavelets (atoms) in the wavelet dictionary R reflection matrix K number of non-zero rows in reflection matrix n time index, n ∈ [N] i trace index in the CMP gather, i ∈ [M]

show abstract

Weighted stacking of seismic AVO data using hybrid AB semblance and local similarity

Cited by 3 publications

References 0 publications

Extracting surface waves, hum and normal modes: time-scale phase-weighted stack and beyond

Extracting surface waves, hum and normal modes: time-scale phase-weighted stack and beyond

A general framework for anisotropic and wavefront moveout parameter analysis

Denoising of pre‐stack seismic data using subspace estimation methods

Contact Info

Product

Resources

About