1,721,059 research outputs found
Basis Pursuit Receiver Function
Get PDFReceiver functions (RFs) are derived by deconvolution of the horizontal (radial or transverse) component of ground motion from the vertical component, which segregates the PS phases. Many methods have been proposed to employ deconvolution in frequency as well as in time domain. These methods vary in their approaches to impose regularization that addresses the stability problem. Here, we present application of a new time-domain deconvolution technique called basis pursuit deconvolution (BPD) that has recently been applied to seismic exploration data. Unlike conventional deconvolution methods, the BPD uses an L1 norm constraint on model reflectivity to impose sparsity. In addition, it uses an overcomplete wedge dictionary based on a dipole reflectivity series to define model constraints, which can achieve higher resolution than that obtained by the traditional methods. We demonstrate successful application of BPD based RF estimation from synthetic data for a crustal model with a near-surface thin layer of thickness 5, 7, 10, and 15 km. The BPD can resolve these thin layers better with much improved signal-to-noise ratio than the conventional methods. Finally, we demonstrate application of the BPD receiver function (BPRF) method to a field dataset from Kutch, India, where near-surface sedimentary layers are known to be present. The BPRFs are able to resolve reflections from these layers very well.Jackson Chair funds at the Jackson School of Geosciences, University of Texas, AustinCouncil of Scientific and Industrial Research twelfth five year plan project at the Council of Scientific and Industrial Research National Geophysical Research Institute (CSIR-NGRI), HyderabadInstitute for Geophysic
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Computational Methods for Parameter Estimation in Climate Models
Get PDFIntensive computational methods have been used by Earth scientists in a wide range of problems in data inversion and uncertainty quantification such as earthquake epicenter location and climate projections. To quantify the uncertainties resulting from a range of plausible model configurations it is necessary to estimate a multidimensional probability distribution. The computational cost of estimating these distributions for geoscience applications is impractical using traditional methods such as Metropolis/Gibbs algorithms as simulation costs limit the number of experiments that can be obtained reasonably. Several alternate sampling strategies have been proposed that could improve on the sampling efficiency including Multiple Very Fast Simulated Annealing (MVFSA) and Adaptive Metropolis algorithms. The performance of these proposed sampling strategies are evaluated with a surrogate climate model that is able to approximate the noise and response behavior of a realistic atmospheric general circulation model (AGCM). The surrogate model is fast enough that its evaluation can be embedded in these Monte Carlo algorithms. We show that adaptive methods can be superior to MVFSA to approximate the known posterior distribution with fewer forward evaluations. However the adaptive methods can also be limited by inadequate sample mixing. The Single Component and Delayed Rejection Adaptive Metropolis algorithms were found to resolve these limitations, although challenges remain to approximating multi-modal distributions. The results show that these advanced methods of statistical inference can provide practical solutions to the climate model calibration problem and challenges in quantifying climate projection uncertainties. The computational methods would also be useful to problems outside climate prediction, particularly those where sampling is limited by availability of computational resources.National Science Foundation OCE-0415251CONACyT-Mexico 159764Institute for Geophysic
Gravity inversion by the Multi-HOmogeneity Depth Estimation method for investigating salt domes and complex sources
No full textScalar wave equation modeling with timespace domain dispersion-relation-based staggered-grid finite-difference schemes
Get PDFAbstract The staggered-grid finite-difference (SFD) method is widely used in numerical modeling of wave equations. Conventional SFD stencils for spatial deriva-tives are usually designed in the space domain. However, when they are used to solve wave equations, it becomes difficult to satisfy the dispersion relations exactly. Liu and Sen (2009c) proposed anewSFDscheme for one-dimensional (1D) scalarwaveequation based on the time–space domain dispersion relation and plane wave theory, which is made to satisfy the exact dispersion relation. This new SFD scheme has greater accuracy and better stability than a conventional scheme under the same discretizations. In this paper, we develop this new SFD scheme further for numerical solution of 2D and 3D scalarwave equations.Wedemonstrate that themodeling accuracy is secondorderwhen the conventional 2M-th-order space-domain SFD and the second order time-domain finite-difference stencils are directly used to solve the scalar wave equation. However, under the same discretization, our 1D scheme can reach 2M-th-order accuracy and is always stable; 2D and 3D schemes can reach 2M-th-order accuracy along 8 and 48 di-rections, respectively, and have better stability. The advantages of the new schemes are also demonstrated with dispersion analysis, stability analysis, and numerical modeling
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Markov Chain Monte Carlo algorithm, integrated 4D seismic reservoir characterization and uncertainty analysis in a Bayesian framework
Get PDFtextOne of the important goals in petroleum exploration and production is to make quantitative estimates of a reservoir’s properties from all available but indirectly related surface data, which constitutes an inverse problem. Due to the inherent non-uniqueness of most inverse procedures, a deterministic solution may be impossible, and it makes more sense to formulate the inverse problem in a statistical Bayesian framework and to fully solve it by constructing the Posterior Probability Density (PPD) function using Markov Chain Monte Carlo (MCMC) algorithms. The derived PPD is the complete solution of an inverse problem and describes all the consistent models for the given data. Therefore, the estimated PPD not only leads to the most likely model or solution but also provides a theoretically correct way to quantify corresponding uncertainty. However, for many realistic applications, MCMC can be computationally expensive due to the strong nonlinearity and high dimensionality of the problem. In this research, to address the fundamental issues of efficiency and accuracy in parameter estimation and uncertainty quantification, I have incorporated some new developments and designed a new multiscale MCMC algorithm. The new algorithm is justified using an analytical example, and its performance is evaluated using a nonlinear pre-stack seismic waveform inversion application. I also find that the new technique of multi-scaling is particularly attractive in addressing model parameterization issues especially for the seismic waveform inversion. To derive an accurate reservoir model and therefore to obtain a reliable reservoir performance prediction with as little uncertainty as possible, I propose a workflow to integrate 4D seismic and well production data in a Bayesian framework. This challenging 4D seismic history matching problem is solved using the new multi-scale MCMC algorithm for reasonably accurate reservoir characterization and uncertainty analysis within an acceptable time period. To take advantage of the benefits from both the fine scale and the coarse scale, a 3D reservoir model is parameterized into two different scales. It is demonstrated that the coarse-scale model works like a regularization operator to make the derived fine-scale reservoir model smooth and more realistic. The derived best-fitting static petrophysical model is further used to image the evolution of a reservoir’s dynamic features such as pore pressure and fluid saturation, which provide a direct indication of the internal dynamic fluid flow.Earth and Planetary Science
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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Acoustic full-waveform to elastic pre-stack seismic inversion of the Yakutat Terrane, Gulf of Alaska
Get PDFThe Yakutat-North American collision in the Gulf of Alaska has developed a complex subduction zone followed by major deformations such as the Chugach-St Elias mountain range creation, intensified exhumation, fold and thrust-fault formation. I generate a compressional velocity model of the Yakutat microplate using two-dimensional acoustic and isotropic time-domain full waveform inversion (FWI) of marine seismic reflection and refraction data from the STEEP project (ST. Elias Erosion/tectonics Project). FWI is a non-linear data-fitting algorithm that aims to recover subsurface parameters from the recorded seismic wavefield. Seismic wave propagation along the Yakutat terrane is simulated using a staggered-grid finite difference modeling scheme. Drawbacks associated with FWI is cycle skipping during the minimization process, which results in converging to the wrong velocity model. Starting with a good initial model that contains the low-frequency information can help mitigate this issue. The starting velocity model input to FWI in this case is generated by a traveltime tomographic inversion of ocean-bottom seismometer and streamer seismic data. Data preconditioning includes muting, filtering, noise removal and amplitude rescaling of the field seismic data to match the corresponding amplitudes of the synthetic traces. The forward model is able to produce a good match between the observed and the modeled wavefield within half the propagated wavelength. I use the FWI result, which shows good correlation with the industry well, as an input to two additional seismic inversion methods: acoustic post-stack and elastic pre-stack seismic inversion in order to recover shear impedance and density models along the seismic line. Extending the problem to the elastic medium is important to support more advanced seismic interpretation. Both techniques were able to produce higher-resolution images of the Yakutat terrane that are well correlated with the well response. Structural complexities identified in the generated models include the northwest-dipping Pamplona fault system, the offshore folding zone, thickening of the Yakutat basement and, lastly, significantly lower velocities in the Poul Creek formation compared to the younger Yakataga formation, which may be attributed to high-fluid pressure within that formation.Earth and Planetary Science
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Time-lapse seismic AVAz in the Stocker Field
No full textOrganic rich Murdock reservoirs require hydraulic stimulation to be economically productive. Zones of high fracture density are desired due to the increase in permeability they would have. Thus, fracture characterization is an essential part of production planning in unconventional reservoirs. Amplitude Variation with Offset and Azimuth (AVAz) methodologies can help us determine parameters related to fractures. Here we employ two different methodologies, namely, Rüger’s method and Fourier Coefficients decomposition method, to address this problem using seismic data targeting the Barnett formation. Rüger’s method uses a linearized version of the full reflection coefficient for HTI media. Fourier Coefficients decomposition relies upon the fact that P-wave reflectivity can be described by a Fourier series and the components of the series can be linked to different properties related to fracture parameters. Both methodologies solve the inverse problem through Iterative Reweighted Least Squares (IRLS) and Cauchy-Gauss method for the calculation of the weights. Using 4D seismic data from the Stocker field, we compute the variation of the AVAz response before and after the hydraulic fracturing treatment and production from the area of interest. The results clearly demonstrate how the seismic response changes after these processes are performed and that can be interpreted in terms of fracture attributes. Anisotropic gradient B[subscript ani], and second Fourier coefficient r2, both proxies for fracture density, are used with fracture azimuth φ[subscript sym] to estimate fracture orientation. With the absence of core data and FMI logs, regional stress information is used to constrain the estimates of fracture orientation. These estimates yield as result the primary orientation of the fractures in the area is NE-SW. Correctly estimating fracture parameters through AVAz analysis can be helpful for future exploitation of shale fields. Correctly planning a well trajectory and completion can help increase hydrocarbon production in shale reservoirs, hence, directly improving the economic value of the project.Earth and Planetary Science
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