1,720,985 research outputs found
Simulated annealing velocity analysis: Automating the picking process
No full textWe have developed an automated method for velocity picking that allows us to estimate appropriate velocity functions for the normal moveout correction of common-depth-point (CDP) gathers, valid for either hyperbolic or nonhyperbolic trajectories. In the hyperbolic velocity analysis case, the process involves the simultaneous search (picking) of a certain number of time-velocity pairs in which the semblance, or any other coherence measure, is high. In the nonhyperbolic velocity analysis case, a third parameter, usually associated with the layering and/or the anisotropy, is added to the searching process. Our technique relies on a simple but effective search of a piecewise linear curve defined by a certain number of nodes in a 2D or 3D space that follows the semblance maxima. The search is carried out efficiently using a constrained very fast simulated annealing algorithm. The constraints consist of static and dynamic bounding restrictions, which are viewed as a means to incorporate prior information about the picking process. This allows us to avoid those maxima that correspond to multiples, spurious events, and other meaningless events. Results using synthetic and field data indicate that our technique permits automatically obtaining accurate and consistent velocity picks that lead to flattened events, in agreement with the manual picks. As an algorithm, the method is very flexible for accommodating additional constraints (e.g., preselected events) and depends on a limited number of parameters. These parameters are easily tuned according to data requirements, available prior information, and the user's needs. The computational costs are relatively low, ranging from a fraction of a second to, at most, 1-2 s per CDP gather, using a standard PC with a single processor.Fil: Velis, Danilo Ruben. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentin
A simple method inspired by empirical mode decomposition for denoising seismic data
Get PDFWe developed a new and simple method for denoising seismic data, which was inspired by data-driven empirical mode decomposition (EMD) algorithms. The method, which can be applied either as a trace-by-trace process or in the f-x domain, replaces the use of the cubic interpolation scheme, which is required to calculate the mean envelopes of the signal and the residues, by window averaging. The resulting strategy is not viewed as an EMD per se, but a userfriendly version of EMD-based algorithms that permits us to attain, in a fraction of the time, the same level of noise cancellation as standard EMD implementations. Furthermore, the proposed method requires less user intervention and easily processes millions of traces in minutes rather than in hours as required by conventional EMD-based techniques on a standard PC. We compared the performance of the new method against standard EMD methods in terms of computational cost and signal preservation and applied them to denoise synthetic and field (microseismic and poststack) data containing random, erratic, and coherent noise. The corresponding f-x EMDs implementations for lateral continuity enhancement were analyzed and compared against the classical f-x deconvolution to test the method.Fil: Gómez, Julián Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; ArgentinaFil: Velis, Danilo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin
Edge-preserving frequency-offset denoising of seismic data
Get PDFWe have developed new algorithms for denoising 2D or 3D poststack seismic-amplitude data that use simple edge-preserving smoothing operators in the frequency-offset domain. The algorithms are aimed to attenuate random and coherent noise, to enhance the signal energy and lateral continuity, and to preserve structural discontinuities such as faults. The methods consist of fitting the frequency slices of the data in the spatial dimension by means of low-order polynomials. We use an overlapping window operator to select the fitting parameters for each point of the slice from the neighborhood with minimum fitting error to provide edge preservation. Various synthetic examples and a field data set are used to demonstrate the strengths and limitations of the algorithms. The denoised outputs indicate enhanced edge preservation of seismic features, which reflects clearer details of semblance attributes.Fil: Gómez, Julián Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; ArgentinaFil: Velis, Danilo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentin
Simple and fast gradient-based impedance inversion using total variation regularization
Get PDFWe present an algorithm to estimate blocky images of the subsurface acoustic impedance (AI) from poststack seismic data. We regularize the resulting inverse problem, which is inherently ill-posed and non-unique, by means of the total variation semi-norm (TV). This allows us promote stable and blocky solutions which are, by virtue of the capability of TV to handle edges properly, adequate to model layered earth models with sharp contrasts. The use of the TV leads to a convex objective function easily minimized using a gradient-based algorithm that requires, in contrast to other AI inversion methods based on TV regularization, simple matrix-vector multiplications and no direct matrix inversion. The latter makes the algorithm numerically stable, easy to apply, and economic in terms of computational cost. Tests on synthetic and field data show that the proposed method, contrarily to conventional l2- or l1-norm regularized solutions, is able to provide blocky AI images that preserve the subsurface layered structure with good lateral continuity from noisy observations.Fil: Perez, Daniel Omar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. YPF - Tecnología; ArgentinaFil: Velis, Danilo Ruben. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentin
A simple energy-based strategy for sensor orientation in borehole microseismic monitoring
Get PDFAccurately determining the orientation of borehole sensors is of paramount importance for microseismic monitoring applications. We calculate the relative bearing angle that allows the orientation of borehole receivers for microseismic monitoring by means of an energy-based strategy that considers the recorded horizontal amplitudes of a perforation shot of known position. This process also allows the appropriate separation of P and S waves, enhancing the accuracy of further processing steps (e.g. time arrival picking). By taking into account the inclination and azimuth of the well, this approach searches for the angle that, after the proper rotations are applied, leads to maximum energy in the source–receiver direction (and minimum energy in the transverse direction). We test the method on synthetic records and two field datasets from Vaca Muerta Formation (Neuquina Basin, Argentina) and statistically evaluate its sensitivity to noise and picking errors. The results show that, in spite of its simplicity, the proposed method is a robust approach that leads to reliable bearing angle estimates with minimum user supervision.Fil: Lagos, Soledad Rocio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; ArgentinaFil: Velis, Danilo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentin
A robust method for microseismic event detection based on automatic phase pickers
Get PDFWe present a robust method for the automatic detection and picking of microseismic events that consists of two steps. The first step provides accurate single-trace picks using three automatic phase pickers adapted from earthquake seismology. In the second step, a multi-channel strategy is implemented to associate (or not) the previous picks with actual microseismic signals by taking into account their expected alignment in all the available channels, thus reducing the false positive rate. As a result, the method provides the number of declared microseismic events, a confidence indicator associated with each of them, and the corresponding traveltime picks. Results using two field noisy data records demonstrate that the automatic detection and picking of microseismic events can be carried out with a relatively high confidence level and accuracy.Fil: Sabbione, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de la Plata. Facultad de Ciencias Astronómicas y Geofísicas; ArgentinaFil: Velis, Danilo Ruben. Universidad Nacional de la Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin
A hybrid simulated annealing and gradient-based algorithm for the estimation of unsaturated soil parameters
Get PDFSimulation of water flow in the unsaturated zone requires knowledge of hydraulic conductivity and water content functions. In most applied studies these functions are described by the well-known van Genuchten constitutive model, which has five independent parameters. Model parameters are usually determined from laboratory experiments, although often these estimates are non-representative of field conditions. In recent years, the use of inverse methods in conjunction with field experiments has become a promising alternative for the accurate estimation of unsaturated soil parameters. Essentially, this procedure involves the minimization of a cost or objective function that measures the discrepancy between observed and simulated data. In the present work we estimate the van Genuchten model parameters from hypothetical drainage experiments using a hybrid optimization strategy based on simulated annealing and a quasi-Newton method. Drainage experiments are modeled by solving Richards equation with appropriate initial and boundary conditions. To obtain approximate solutions of Richards equation we use a Galerkin finite element method. The algorithm behavior and the consequences on the estimated van Genuchten model parameters using different objective functions are explored. Objective functions are constructed from two sets of data which are usually obtained on field experiments: pressure head p versus time measured at different depths and water content θ versus depth measured at different times. The proposed estimation procedure is tested using synthetically generated data. Numerical examples show that the inverse modeling of drainage experiments using a hybrid simulated annealing and gradient-based algorithm provides an excellent methodology for an efficient and accurate estimation of unsaturated soil parameters.Fil: Guarracino, Luis. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Departamento de Geofísica Aplicada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Velis, Danilo Ruben. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentin
Footprint removal from seismic data with residual dictionary learning
No full textDictionary learning (DL) is a machine learning technique that can be used to find a sparse representation of a given data set by means of a relatively small set of atoms, which are learned from the input data. DL allows for the removal of random noise from seismic data very effectively. However, when seismic data are contaminated with footprint noise, the atoms of the learned dictionary are often a mixture of data and coherent noise patterns. In this scenario, DL requires carrying out a morphological attribute classification of the atoms to separate the noisy atoms from the dictionary. Instead, we have developed a novel DL strategy for the removal of footprint patterns in 3D seismic data that is based on an augmented dictionary built upon appropriately filtering the learned atoms. The resulting augmented dictionary, which contains the filtered atoms and their residuals, has a high discriminative power in separating signal and footprint atoms, thus precluding the use of any statistical classification strategy to segregate the atoms of the learned dictionary. We filter the atoms using a domain transform filtering approach, a very efficient edge-preserving smoothing algorithm. As in the so-called coherence-constrained DL method, the proposed DL strategy does not require the user to know or adjust the noise level or the sparsity of the solution for each data set. Furthermore, it only requires one pass of DL and is shown to produce successful transfer learning. This increases the speed of the denoising processing because the augmented dictionary does not need to be calculated for each time slice of the input data volume. Results on synthetic and 3D public-domain poststack field data demonstrate effective footprint removal with accurate edge preservation.Fil: Gómez, Julián Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. YPF - Tecnología; ArgentinaFil: Velis, Danilo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentin
Poldw: A Python code to denoise 3C seismic data with a new threshold-free polarization technique
No full textWe present a Python code that implements a novel threshold-free polarization strategy for removing random noise from three-component (3C) linearly polarized seismic data. The code, which we refer to as poldw (polarization denoising through windowing), uses closed-form formulas along sliding windows that span the data to determine the optimal rotation angles that allow the transfer of most of the signal energy to a given component. The denoised 3C data is obtained after canceling out the other two components, which are assumed to contain predominantly noise, and rotating back. The method is simple and efficient because it only requires setting the sliding window length. Synthetic and microseismic field data examples show the method’s effectiveness, which significantly improves the signal-to-noise ratio without the need for threshold-based polarization filters. Even so, these filters can be pipelined in the rotation-based strategy for additional noise removal if necessary. When the dataset contains non-linearly polarized data or significant non-random noise, the method is likely to fail. For robustness against non-Gaussian noise and outliers, poldw allows for the use of alternative norms like the L1- or Lp-norms instead of the energy. In addition to the code, we provide a Jupyter notebook to illustrate the method step by step and reproduce the results of the field data example.Fil: Velis, Danilo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; ArgentinaFil: Gómez, Julián Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. YPF - Tecnología; Argentin
Inversion of prestack seismic data using FISTA
Get PDFIn this work we present a new inversion method to obtain AVA high-resolution attributes from prestack seismic data. The method aims to find a series of sparse reflectors that, when convolved with the source wavelet, fit the observed data. To perform the inversion, we propose the use of the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). FISTA, which can be viewed as an extension of the classical gradient algorithm, provides sparse solutions minimizing both the misfit between the modeled and the observed data, and the l1-norm of the solution. The advantage of FISTA over other methods is that no inversion over any matrix is needed, making it numerically stable, easy to apply, economic in computational terms, and adequate for solving large-scale problems even with dense matrix data. Results on synthetic and field data show that the proposed method is capable to provide high-resolution AVA attributes that honor the observed data under noisy conditions, making it an interesting alternative to other known methods.Fil: Perez, Daniel Omar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; ArgentinaFil: Velis, Danilo Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; ArgentinaFil: Sacchi, Mauricio Dino. University Of Alberta. Faculty Of Sciences; Canad
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