Attribute-assisted footprint suppression using a 2D continuous wavelet transform

2018 ◽  
Vol 6 (2) ◽  
pp. T457-T470 ◽  
Author(s):  
Abdulmohsen Alali ◽  
Gabriel Machado ◽  
Kurt J. Marfurt
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Seismic Data ◽  
Wave Impedance ◽  
P Wave ◽  
Continuous Wavelet ◽  
Seismic Attribute ◽  
Attribute Data ◽  
Amplitude Data ◽  
Seismic Acquisition

Acquisition footprint manifests itself on 3D seismic data as a repetitive pattern of noise, anomalously high amplitudes, or structural shifts on time or horizon slices that is correlated to the location of the sources and receivers on the earth’s surface. Ideally, footprint suppression should be handled by denser seismic acquisition and more careful prestack processing prior to seismic imaging. In the case in which only legacy data exist, or when economic and time constraints preclude more expensive acquisition and more careful processing, interpreters must deal with data contaminated by footprint. Although accurate time-structure maps can be constructed from footprint-contaminated data, the effect of footprint on subsequent attributes, such as coherence, curvature, spectral components, and P-wave impedance will be exacerbated. We have developed a workflow that uses a 2D continuous wavelet transform to suppress coherent and incoherent noise on poststack seismic data. The method involves decomposing time slices of amplitude and attribute data into voices and magnitudes using 2D wavelets. We exploit the increased seismic attribute sensitivity to the acquisition footprint to design a mask to suppress the footprint on the original amplitude data. The workflow is easy to apply and improves the interpretability of the data and improves the subsequent attribute resolution.

Relative acoustic impedance from wavelet transform

2014 ◽  
Vol 2 (1) ◽  
pp. SA107-SA118 ◽  
Author(s):  
Marcílio Castro de Matos ◽  
Rodrigo Penna ◽  
Paulo Johann ◽  
Kurt Marfurt
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Seismic Data ◽  
Acoustic Impedance ◽  
Real Data ◽  
Velocity Model ◽  
Continuous Wavelet ◽  
Reconstruction Process ◽  
Seismic Wavelet ◽  
Morlet Continuous Wavelet Transform

Most deconvolution algorithms try to transform the seismic wavelet into spikes by designing inverse filters that remove an estimated seismic wavelet from seismic data. We assume that seismic trace subtle discontinuities are associated with acoustic impedance contrasts and can be characterized by wavelet transform spectral ridges, also called modulus maxima lines (WTMML), allowing us to improve seismic resolution by using the wavelet transform. Specifically, we apply the complex Morlet continuous wavelet transform (CWT) to each seismic trace and compute the WTMMLs. Then, we reconstruct the seismic trace with the inverse continuous wavelet transform from the computed WTMMLs with a broader band complex Morlet wavelet than that used in the forward CWT. Because the reconstruction process preserves amplitude and phase along different scales, or frequencies, the result looks like a deconvolution method. Considering this high-resolution seismic representation as a reflectivity approximation, we estimate the relative acoustic impedance (RAI) by filtering and trace integrating it. Conventional deconvolution algorithms assume the seismic wavelet to be stochastic, while the CWT is implicitly time varying such that it can be applied to both depth and time-domain data. Using synthetic and real seismic data, we evaluated the effectiveness of the methodology on detecting seismic events associated with acoustic impedance changes. In the real data examples, time and in-depth RAI results, show good correlation with real P-impedance band-pass data computed using more rigorous commercial inversion software packages that require well logs and low-frequency velocity model information.

scholarly journals Frequency bandwidth enhancement using continuous wavelet transform and random noise attenuation by f-xy filtering technique – a case study from Badin oilfield, Indus basin, Pakistan

2019 ◽  
Vol 133 ◽  
pp. 01007
Author(s):  
Asad Taimur ◽  
Akinniyi Akinsunmade ◽  
Sylwia Tomecka-Suchon ◽  
Fahad Mehmood
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Seismic Data ◽  
Random Noise ◽  
Resolving Power ◽  
Indus Basin ◽  
Frequency Resolution ◽  
Continuous Wavelet ◽  
Bandwidth Extension ◽  
Time Frequency

Routine seismic data processing does not always meet the quantitative interpreters’ expectations especially in areas like Badin, where prospective thin bed B – sand interval is ambiguous throughout the seismic volume. Continuous Wavelet Transform (CWT) provides detailed description of seismic signal in both time and frequency without compromising on window length and a fixed time-frequency resolution over time-frequency spectrum. We present enhancement of seismic data for effective interpretation using the bandwidth extension technique. Implementing bandwidth extension, the dominant frequency increases from 18 Hz to 30 Hz and the frequency content boosted from 40 Hz to 60 Hz. Noise inclusion by the technique was suppressed by F-XY predictive filter and F-XY deconvolution with edge preserve smoothing. Phase and spectral balancing were applied to partial angle stacks to stabilize the phase rotation across the 3D survey, particularly for far offset stack. Frequency was balanced using surface consistent spectrum balancing, and subjected to trace scaling for amplitudes balance and preservation. Results of the techniques yielded unique improvement on the data resolution and subtle information about the thin sand beds were better delineated. Tuning thickness analysis reveals the usefulness of bandwidth extension, with an increase of 30% in the resolving power of thin beds.

Multiattribute Seismic Analysis With Fractal Dimension and 2D and 3D Continuous Wavelet Transform

2014 ◽  
Vol 17 (04) ◽  
pp. 436-443 ◽  
Author(s):  
Puneet Saraswat ◽  
Vijay Raj ◽  
Mrinal K. Sen ◽  
Arun Narayanan
Keyword(s):  
Fractal Dimension ◽  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Seismic Data ◽  
Seismic Analysis ◽  
Seismic Attributes ◽  
Structural Features ◽  
Primary Concern ◽  
Continuous Wavelet ◽  
Salt Domes

Summary The 3D post-stack seismic attributes provide an intuitive and effective way of using seismic volumes for reservoir characterization and development, and further identification of exploration targets. Some of the seismic attributes can aid in the precise prediction of the geometry and heterogeneity of subsurface geological settings. These also can provide useful information on petrophysical and lithological properties when combined with well-log information. There exist numerous seismic attributes that provide a unique interpretation on some aspects of subsurface geology. Of these, the proper demarcation of structural features— such as location and edges of faults and salt domes, and their throw and extent—always has been of primary concern. In this paper, we propose new multiattribute seismic algorithms by using fractal dimension and 2D/3D continuous wavelet transform (CWT). The use of higher-dimensional wavelets incorporates information from the ensemble of traces and can correlate information between neighboring traces in seismic data. The spectral decomposition that is based on the CWT aids in resolving various features of geological interest at a particular scale or frequency, which, when rendered with fractal attribute, demarcates the boundaries between those. We apply these two algorithms separately to a seismic amplitude volume and co-render output volumes together with some weights to yield a final attribute volume incorporating information from the aforementioned algorithms. We demonstrate the efficacy of these two algorithms in terms of the resolution and proper demarcation of various geological structures on real seismic data. The application of these algorithms results in better illumination and proper demarcation of various geological features such as salt domes, channels, and faults, and it illustrates how these simple tools can help to extract detailed information from seismic data.

Time‐frequency attribute of seismic data using continuous wavelet transform

2003 ◽  
Author(s):  
Satish K. Sinha ◽  
Partha S. Routh ◽  
Phil D. Anno ◽  
John P. Castagna
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Seismic Data ◽  
Continuous Wavelet ◽  
Time Frequency
Sign in / Sign up

Export Citation Format

Share Document