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This algorithm The first equation is obtained from the eikonal by using the inverse takes advantage of the almost independent ray calculation at functions x(γ,τ) and z(γ,τ) instead of the original τ(x,z) every point of a wavefront. The speedup of this method is near and γ(x,z). The second equation states that in isotropic media 9 times with respect to the sequential version, specially when rays are orthogonal to wavefronts.
A transgressive sedimentary sequence assessment by multioffset GPR data
Martins, Saulo (Universidade Federal do Rio de Janeiro (UFRJ)) | De Gomes, Ellen (Federal University of Pará) | Travassos, Jandyr (Universidade Federal do Rio de Janeiro (UFRJ)) | Mansur, Webe (Universidade Federal do Rio de Janeiro (UFRJ))
ABSTRACT In this work, the GPR mono-channel data is acquired in Marambaia barrier island. These data are processed to simulate multi-channels and thus allow the estimation of the 2D velocity model in time of the subsurface area data. The imaging data obtained from these reflectors focused best shown with greater number of refractions being collapsed, when compared with the imaging obtained in conventional manner GPR data, which is used 1D velocity model. Presentation Date: Wednesday, October 19, 2016 Start Time: 8:50:00 AM Location: Lobby D/C Presentation Type: POSTER
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (1.00)
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (0.94)
ABSTRACT Prediction-error filters (PEFs) play an important role in seismic deconvolution and other geophysical estimation problems. We show that non-stationary multidimensional PEFs can be computed in a "streaming" manner, where the filter gets updated incrementally by accepting one new data point at a time. The computational cost of computing a streaming PEF reduces to the cost of a single convolution. In other words, the cost of PEF design while filtering equals the cost of applying the filter. Moreover, the non-linear operation of finding and applying a streaming PEF is invertible at the same cost, which enables a fast approach to missing data interpolation. Presentation Date: Wednesday, October 19, 2016 Start Time: 9:15:00 AM Location: 148 Presentation Type: ORAL
- Information Technology > Modeling & Simulation (0.64)
- Information Technology > Data Science (0.39)
ABSTRACT The task of scattered data gridding is to reconstruct data on a regular grid from data given at irregular locations while providing a certain degree of smoothness in the result. I describe a fast algorithm for this task, which consists of three steps: (1) computing the distance function from the scattered data locations to the grid points, (2) spreading the data values from irregular locations to nearest points on the grid, (3) smoothing. Each step is highly efficient and has the computational cost of (), where is the number of grid points. To improve accuracy, the steps can be combined using iterative shaping regularization, which increases the cost by a small factor. In application to seismic data reconstruction, a similar approach works by replacing the second step with predictive painting and replacing the third step with structure-oriented smoothing. Presentation Date: Wednesday, October 19, 2016 Start Time: 8:00:00 AM Location: 166 Presentation Type: ORAL
- Europe > Netherlands (0.29)
- North America > United States (0.29)
ABSTRACT Diffraction imaging aims to emphasize small-scale subsurface heterogeneities such as faults, pinch-outs, fracture swarms, channels, etc. and can play an important role in seismic reservoir characterization. The key step in diffraction imaging work-flow is based on the separation procedure suppressing higher-energy reflections and emphasizing diffractions, after which diffractions can be imaged independently. We propose an imaging strategy based on a path-integral imaging operator and a least-squares migration algorithm with sparse model regularization. Sparsity is an inherent property of diffraction scatterers with an intermittent and spiky spatial distribution. The effectiveness of the proposed approach is confirmed by synthetic and field data examples. Presentation Date: Wednesday, October 19, 2016 Start Time: 10:45:00 AM Location: 171/173 Presentation Type: ORAL
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.69)
Recovering the most from big gaps using least-squares inversion
Zu, Shaohuan (China University of Petroleum–Beijing) | Pan, Xiao (China University of Petroleum–Beijing) | Shuwei, Gan (China University of Petroleum–Beijing) | Zhou, Hui (China University of Petroleum–Beijing) | Chen, Yangkang (University of Texas–Austin) | Zhang, Dong (China University of Petroleum–Beijing) | Xie, Chunlin (E&D Research Institute, Daqing Oilfield Company)
ABSTRACT Seismic data are inadequately or irregularly sampled, particularly when there are big gaps, which will produce artifacts in the seismic imaging. The reconstruction can be posed as an inverse problem, which is known to be ill-posed and requires constraints to achieve unique and stable solutions. In this abstract, we propose an iterative scheme to reconstruct big gaps using least-squares method with slope constraint. In the proposed method, the slope estimation is a very important step. We apply an iterative scheme to estimate the slope field. In the first iteration, the smooth radius must be large to estimate smooth dip from the decimated data to guarantee the stability of inversion. In the later iterations, the smooth radius will be shortened in order to get more accurate dip estimation and good reconstruction result. We compare the proposed method and the well-known projection onto convex sets (POCS) method on the synthetic and field data examples. The interpolation results illustrate the advantage of the proposed method in constructing big gaps. Presentation Date: Tuesday, October 18, 2016 Start Time: 3:45:00 PM Location: 148 Presentation Type: ORAL
- Asia > China > Heilongjiang > Songliao Basin > Daqing Field > Yian Formation (0.99)
- Asia > China > Heilongjiang > Songliao Basin > Daqing Field > Mingshui Formation (0.99)
ABSTRACT Time-frequency decomposition can capture the nonstationary character of seismic data. In this paper, we propose a new method of time-frequency analysis based on regularized nonstationary autoregression coupled with Hilbert-Huang spectrum (RNARHHS). RNARHHS is an empirical-mode-decomposition like method but uses regularized nonstationary autoregression to construct its intrinsic mode functions (IMFs). Examples of synthetic and field seismic data show that this method achieves high time-frequency resolution and can detect low-frequency anomalies. Presentation Date: Tuesday, October 18, 2016 Start Time: 8:00:00 AM Location: 170/172 Presentation Type: ORAL
ABSTRACT Estimating and correcting time shifts between time-lapse images is at the heart of comparing repeated seismic images in the same time domain and extracting 4D signal. The conventional assumption behind the methods developed for extraction of 4D signal, is that propagation of seismic signals can be modeled using the simple convolutional model. According to the convolutional model, seismic traces are 1D normal-incidence records, which is strictly true only in the case of horizontal layers. When the subsurface contains dipping layers, seismic normal-incidence reflections occur normal to reflectors and not in the vertical direction, thus the simple covolutional assumption is violated and stretching and squeezing the travel-time of the seismic signal vertically instead of normal to reflectors causes inaccuracy in the registration results. We propose to extract 4D signal in the stratigraphic coordinate system, where the vertical direction corresponds to normal to reflectors and the 1D convolutional model is honored. We apply the proposed method to data from Cranfield CO experiment. Presentation Date: Tuesday, October 18, 2016 Start Time: 10:20:00 AM Location: 150 Presentation Type: ORAL
- Geophysics > Time-Lapse Surveying > Time-Lapse Seismic Surveying (1.00)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling (1.00)
- Europe > Norway > Norwegian Sea > Halten Terrace > PL 128 > Block 6608/10 > Norne Field > Tofte Formation (0.99)
- Europe > Norway > Norwegian Sea > Halten Terrace > PL 128 > Block 6608/10 > Norne Field > Not Formation (0.99)
- Europe > Norway > Norwegian Sea > Halten Terrace > PL 128 > Block 6608/10 > Norne Field > Ile Formation (0.99)
- (9 more...)
ABSTRACT The K-SVD algorithm has been successfully utilized for adaptively learning the sparse dictionary in 2D seismic denoising. Because of the high computational cost of many SVDs in the K-SVD algorithm, it is not applicable in practical applications, especially in 3D or 5D problems. In this paper, I extend the dictionary based denoising approach from 2D to 3D. To address the computational efficiency problem in K-SVD, I proposed a fast dictionary learning approach based on the sequential generalized K-means (SGK) algorithm for denoising multidimensional seismic data. I summarized the sparse dictionary learning algorithm using K-SVD, and introduce SGK algorithm together with its detailed mathematical implications. 2D field data example, 3D synthetic and field data examples are used to demonstrate the performance of both K-SVD and SGK algorithms. It has been shown that SGK algorithm can significantly increase the computational efficiency without degrading the denoising performance. Presentation Date: Wednesday, October 19, 2016 Start Time: 11:10:00 AM Location: 148 Presentation Type: ORAL
Sun (1997) extended Claerbout's idea to the which eliminates the effects of "NMO stretch" and restores a case of depth-variable velocity. The inverse NMO stack operator wider frequency band by replacing conventional stacking with applied depends on hyperbolic moveout relation and a regularized inversion to zero offset. The resulting stack is a can be employed to remove non-hyperbolic events and random model that best fits the data using additional constraints imposed noise. Trickett (2003) uses a variation of Claerbout's inverse by shaping regularization. We introduce a recursive NMO stack in his stretch-free stacking method to avoid stacking scheme using plane-wave construction in the backward "NMO stretch". Trickett's results tend to be higher frequency operator of shaping regularization to achieve a higher but noisier than a conventional stack. Multiple other algorithms resolution stack. The advantage of using recursive stacking have been proposed that aim to reduce NMO stretching along local slopes in the application to NMO and stack is that effects (Byun and Nelan, 1997; Hicks, 2001; Hilterman and it avoids "stretching effects" caused by NMO correction and Schuyver, 2003; Rupert and Chun, 1975; Perroud and Tygel, is insensitive to non-hyperbolic moveout in the data. Numerical 2004; Masoomzadeh et al., 2010; Zhang et al., 2013; Kazemi tests demonstrate the algorithm's ability to attain a higher and Siahkoohi, 2011).