Surface-consistent matching filters for time-lapse seismic processing

We evaluated the concept of surface-consistent matching filters for processing time-lapse seismic data, in which matching filters are convolutional filters that minimize the sum-squared error between two signals. Because in the Fourier domain a matching filter is the spectral ratio of the two signals, we extended the well-known surface-consistent hypothesis such that the data term is a trace-by-trace spectral ratio of two data sets instead of only one (i.e., surface-consistent deconvolution). To avoid unstable division of spectra, we computed the spectral ratios in the time domain by first designing trace-sequential, least-squares matching filters, then Fourier transforming them. A subsequent least-squares solution then factored the trace-sequential matching filters into four operators: two surface-consistent (source and receiver) and two subsurface-consistent (offset and midpoint). We evaluated a time-lapse synthetic data set with nonrepeatable acquisition parameters, complex near-surface geology, and a variable subsurface reservoir layer. We computed the four-operator surface-consistent matching filters from two surveys, baseline and monitor, then applied these matching filters to the monitor survey to match it to the baseline survey over a temporal window where changes were not expected. This algorithm significantly reduced the effect of most of the nonrepeatable parameters, such as differences in source strength, receiver coupling, wavelet bandwidth and phase, and static shifts. We computed the normalized root mean square difference on raw stacked data (baseline and monitor) and obtained a mean value of 70%. This value was significantly reduced after applying the 4C surface-consistent matching filters to about 13.6% computed from final stacks.

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Simultaneous multivintage time-shift estimation

Geophysics ◽

10.1190/1.3177002 ◽

2009 ◽

Vol 74 (5) ◽

pp. V109-V121 ◽

Cited By ~ 6

Author(s):

Ehsan Zabihi Naeini ◽

Henning Hoeber ◽

Gordon Poole ◽

Hamid R. Siahkoohi

Keyword(s):

Continental Shelf ◽

Least Squares ◽

Least Squares Method ◽

Synthetic Data ◽

Time Lapse ◽

Time Shift ◽

Data Sets ◽

Data Set ◽

Time Alignment ◽

Multiple Repeat

Time-shift estimation is a key step in seismic time-lapse processing as well as in many other signal-processing applications. We consider the time-shift problem in the setting of multiple repeat surveys that must be aligned consistently. We introduce an optimized least-squares method based on the Taylor expansion for estimating two-vintage time shifts and compare it to crosscorrelation. The superiority of the proposed algorithm is demonstrated with synthetic data and residual time-lapse matching on a U. K. continental shelf data set. We then discuss the shortcomings of cascaded time alignment in multiple repeat monitor surveys and propose an approach to estimate simultaneous multivintage time shifts that uses a constrained least-squares technique combined with elements of network theory. The resulting time shifts are consistent across all vintages in a least-squares sense, improving overall alignment when compared to the classical flow of alignment in a cascaded manner. The method surpasses the cascaded approach, as noted with sample synthetic and three-vintage U. K. continental shelf time-lapse data sets.

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Regularization and datuming of seismic data by weighted, damped least squares

Geophysics ◽

10.1190/1.2235616 ◽

2006 ◽

Vol 71 (5) ◽

pp. U67-U76 ◽

Cited By ~ 7

Author(s):

Robert J. Ferguson

Keyword(s):

Least Squares ◽

Seismic Data ◽

Synthetic Data ◽

Real Data ◽

Structural Data ◽

Irregular Sampling ◽

Data Set ◽

Near Surface ◽

Damped Least Squares ◽

Wavefield Extrapolation

The possibility of improving regularization/datuming of seismic data is investigated by treating wavefield extrapolation as an inversion problem. Weighted, damped least squares is then used to produce the regularized/datumed wavefield. Regularization/datuming is extremely costly because of computing the Hessian, so an efficient approximation is introduced. Approximation is achieved by computing a limited number of diagonals in the operators involved. Real and synthetic data examples demonstrate the utility of this approach. For synthetic data, regularization/datuming is demonstrated for large extrapolation distances using a highly irregular recording array. Without approximation, regularization/datuming returns a regularized wavefield with reduced operator artifacts when compared to a nonregularizing method such as generalized phase shift plus interpolation (PSPI). Approximate regularization/datuming returns a regularized wavefield for approximately two orders of magnitude less in cost; but it is dip limited, though in a controllable way, compared to the full method. The Foothills structural data set, a freely available data set from the Rocky Mountains of Canada, demonstrates application to real data. The data have highly irregular sampling along the shot coordinate, and they suffer from significant near-surface effects. Approximate regularization/datuming returns common receiver data that are superior in appearance compared to conventional datuming.

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Time-lapse difference static correction using prestack crosscorrelations: 4D seismic image enhancement case from Ketzin

Geophysics ◽

10.1190/geo2013-0422.1 ◽

2014 ◽

Vol 79 (6) ◽

pp. B243-B252 ◽

Cited By ~ 8

Author(s):

Peter Bergmann ◽

Artem Kashubin ◽

Monika Ivandic ◽

Stefan Lüth ◽

Christopher Juhlin

Keyword(s):

Seismic Data ◽

Time Lapse ◽

Data Sets ◽

Storage Site ◽

Data Set ◽

Near Surface ◽

Static Correction ◽

Seismic Image ◽

Static Corrections ◽

4D Seismic

A method for static correction of time-lapse differences in reflection arrival times of time-lapse prestack seismic data is presented. These arrival-time differences are typically caused by changes in the near-surface velocities between the acquisitions and had a detrimental impact on time-lapse seismic imaging. Trace-to-trace time shifts of the data sets from different vintages are determined by crosscorrelations. The time shifts are decomposed in a surface-consistent manner, which yields static corrections that tie the repeat data to the baseline data. Hence, this approach implies that new refraction static corrections for the repeat data sets are unnecessary. The approach is demonstrated on a 4D seismic data set from the Ketzin [Formula: see text] pilot storage site, Germany, and is compared with the result of an initial processing that was based on separate refraction static corrections. It is shown that the time-lapse difference static correction approach reduces 4D noise more effectively than separate refraction static corrections and is significantly less labor intensive.

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Least-squares Gaussian beam migration

Geophysics ◽

10.1190/geo2015-0328.1 ◽

2016 ◽

Vol 81 (3) ◽

pp. S87-S100 ◽

Cited By ~ 22

Author(s):

Hao Hu ◽

Yike Liu ◽

Yingcai Zheng ◽

Xuejian Liu ◽

Huiyi Lu

Keyword(s):

Least Squares ◽

Gaussian Beam ◽

Spatial Resolution ◽

Field Data ◽

Synthetic Data ◽

Data Sets ◽

Data Set ◽

Seismic Acquisition ◽

Gaussian Beam Migration ◽

Least Squares Migration

Least-squares migration (LSM) can be effective to mitigate the limitation of finite-seismic acquisition, balance the subsurface illumination, and improve the spatial resolution of the image, but it requires iterations of migration and demigration to obtain the desired subsurface reflectivity model. The computational efficiency and accuracy of migration and demigration operators are crucial for applying the algorithm. We have developed a test of the feasibility of using the Gaussian beam as the wavefield extrapolating operator for the LSM, denoted as least-squares Gaussian beam migration. Our method combines the advantages of the LSM and the efficiency of the Gaussian beam propagator. Our numerical evaluations, including two synthetic data sets and one marine field data set, illustrate that the proposed approach could be used to obtain amplitude-balanced images and to broaden the bandwidth of the migrated images in particular for the low-wavenumber components.

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Asteroid mass estimation with the robust adaptive Metropolis algorithm

Astronomy and Astrophysics ◽

10.1051/0004-6361/201935608 ◽

2020 ◽

Vol 633 ◽

pp. A46

Author(s):

L. Siltala ◽

M. Granvik

Keyword(s):

Least Squares ◽

Bulk Density ◽

Recent Literature ◽

Estimation Method ◽

Synthetic Data ◽

Metropolis Algorithm ◽

Data Sets ◽

Data Set ◽

Mass Estimation ◽

Robust Adaptive

Context. The bulk density of an asteroid informs us about its interior structure and composition. To constrain the bulk density, one needs an estimated mass of the asteroid. The mass is estimated by analyzing an asteroid’s gravitational interaction with another object, such as another asteroid during a close encounter. An estimate for the mass has typically been obtained with linearized least-squares methods, despite the fact that this family of methods is not able to properly describe non-Gaussian parameter distributions. In addition, the uncertainties reported for asteroid masses in the literature are sometimes inconsistent with each other and are suspected to be unrealistically low. Aims. We aim to present a Markov-chain Monte Carlo (MCMC) algorithm for the asteroid mass estimation problem based on asteroid-asteroid close encounters. We verify that our algorithm works correctly by applying it to synthetic data sets. We use astrometry available through the Minor Planet Center to estimate masses for a select few example cases and compare our results with results reported in the literature. Methods. Our mass-estimation method is based on the robust adaptive Metropolis algorithm that has been implemented into the OpenOrb asteroid orbit computation software. Our method has the built-in capability to analyze multiple perturbing asteroids and test asteroids simultaneously. Results. We find that our mass estimates for the synthetic data sets are fully consistent with the ground truth. The nominal masses for real example cases typically agree with the literature but tend to have greater uncertainties than what is reported in recent literature. Possible reasons for this include different astrometric data sets and weights, different test asteroids, different force models or different algorithms. For (16) Psyche, the target of NASA’s Psyche mission, our maximum likelihood mass is approximately 55% of what is reported in the literature. Such a low mass would imply that the bulk density is significantly lower than previously expected and thus disagrees with the theory of (16) Psyche being the metallic core of a protoplanet. We do, however, note that masses reported in recent literature remain within our 3-sigma limits. Results. The new MCMC mass-estimation algorithm performs as expected, but a rigorous comparison with results from a least-squares algorithm with the exact same data set remains to be done. The matters of uncertainties in comparison with other algorithms and correlations of observations also warrant further investigation.

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Imaging near-surface heterogeneities by natural migration of backscattered surface waves: Field data test

Geophysics ◽

10.1190/geo2016-0253.1 ◽

2017 ◽

Vol 82 (3) ◽

pp. S197-S205 ◽

Cited By ~ 8

Author(s):

Zhaolun Liu ◽

Abdullah AlTheyab ◽

Sherif M. Hanafy ◽

Gerard Schuster

Keyword(s):

Surface Waves ◽

Field Data ◽

Synthetic Data ◽

Data Sets ◽

Data Set ◽

Near Surface ◽

3D Data ◽

Strong Surface ◽

Migration Method ◽

Recorded Data

We have developed a methodology for detecting the presence of near-surface heterogeneities by naturally migrating backscattered surface waves in controlled-source data. The near-surface heterogeneities must be located within a depth of approximately one-third the dominant wavelength [Formula: see text] of the strong surface-wave arrivals. This natural migration method does not require knowledge of the near-surface phase-velocity distribution because it uses the recorded data to approximate the Green’s functions for migration. Prior to migration, the backscattered data are separated from the original records, and the band-passed filtered data are migrated to give an estimate of the migration image at a depth of approximately one-third [Formula: see text]. Each band-passed data set gives a migration image at a different depth. Results with synthetic data and field data recorded over known faults validate the effectiveness of this method. Migrating the surface waves in recorded 2D and 3D data sets accurately reveals the locations of known faults. The limitation of this method is that it requires a dense array of receivers with a geophone interval less than approximately one-half [Formula: see text].

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Discrimination between phase and amplitude attributes in time-lapse seismic streamer data

Geophysics ◽

10.1190/1.2208942 ◽

2006 ◽

Vol 71 (4) ◽

pp. O9-O19 ◽

Cited By ~ 7

Author(s):

Jesper Spetzler ◽

Øyvind Kvam

Keyword(s):

Spectral Ratio ◽

Time Lapse ◽

Ratio Method ◽

Data Sets ◽

Data Set ◽

The North ◽

Seismic Experiment ◽

Streamer Data ◽

Time Invariant ◽

Relative Change

Time-lapse seismic experiments aim to obtain information about production-related effects in hydrocarbon reservoirs to increase the recovery percentage. However, nonrepeatability problems such as acquisition differences, overburden effects, and noise are often significantly stronger than the imprint of production changes in time-lapse seismic data sets. Consequently, it is very difficult to appraise the changes in petrophysical reservoir parameters over time. We introduce a 4D monitoring approach based on the spectral ratio method. This method produces two time-lapse attributes: the relative change in reflection coefficient and the traveltime shift at reflecting interfaces. These attributes can be used for appraising production-related changes in the subsurface. The approach corrects for time-invariant nonrepeatability effects in the overburden and source-receiver coupling problems in time-lapse surveys. The validity of the method is limited to structurally simple overburden and reservoirs with weak lateral variations. First, we validate the methodology using a synthetic time-lapse seismic experiment. Next, we apply the method to a real time-lapse data set from the Troll West gas province in the North Sea. In the real example, we could not detect movement in the fluid contact of [Formula: see text]. The expected change in amplitude is less than 10%, which is probably below the background noise level for this data set.

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Methods for estimating uncertainty in factor analytic solutions

Atmospheric Measurement Techniques ◽

10.5194/amt-7-781-2014 ◽

2014 ◽

Vol 7 (3) ◽

pp. 781-797 ◽

Cited By ~ 174

Author(s):

P. Paatero ◽

S. Eberly ◽

S. G. Brown ◽

G. A. Norris

Keyword(s):

Environmental Protection Agency ◽

Synthetic Data ◽

Analytic Solutions ◽

Data Sets ◽

Random Errors ◽

Data Set ◽

Factor Analytic ◽

Uncertainty Estimates ◽

Multilinear Engine ◽

Analytic Models

Abstract. The EPA PMF (Environmental Protection Agency positive matrix factorization) version 5.0 and the underlying multilinear engine-executable ME-2 contain three methods for estimating uncertainty in factor analytic models: classical bootstrap (BS), displacement of factor elements (DISP), and bootstrap enhanced by displacement of factor elements (BS-DISP). The goal of these methods is to capture the uncertainty of PMF analyses due to random errors and rotational ambiguity. It is shown that the three methods complement each other: depending on characteristics of the data set, one method may provide better results than the other two. Results are presented using synthetic data sets, including interpretation of diagnostics, and recommendations are given for parameters to report when documenting uncertainty estimates from EPA PMF or ME-2 applications.

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Stochastic inversion of pressure and saturation changes from time-lapse AVO data

Geophysics ◽

10.1190/1.2235858 ◽

2006 ◽

Vol 71 (5) ◽

pp. C81-C92 ◽

Cited By ~ 17

Author(s):

Helene Hafslund Veire ◽

Hilde Grude Borgos ◽

Martin Landrø

Keyword(s):

Stochastic Model ◽

Seismic Data ◽

Synthetic Data ◽

Time Lapse ◽

Bayesian Framework ◽

Reservoir Modeling ◽

Data Set ◽

The North ◽

Fluid Saturation ◽

Likelihood Model

Effects of pressure and fluid saturation can have the same degree of impact on seismic amplitudes and differential traveltimes in the reservoir interval; thus, they are often inseparable by analysis of a single stacked seismic data set. In such cases, time-lapse AVO analysis offers an opportunity to discriminate between the two effects. We quantify the uncertainty in estimations to utilize information about pressure- and saturation-related changes in reservoir modeling and simulation. One way of analyzing uncertainties is to formulate the problem in a Bayesian framework. Here, the solution of the problem will be represented by a probability density function (PDF), providing estimations of uncertainties as well as direct estimations of the properties. A stochastic model for estimation of pressure and saturation changes from time-lapse seismic AVO data is investigated within a Bayesian framework. Well-known rock physical relationships are used to set up a prior stochastic model. PP reflection coefficient differences are used to establish a likelihood model for linking reservoir variables and time-lapse seismic data. The methodology incorporates correlation between different variables of the model as well as spatial dependencies for each of the variables. In addition, information about possible bottlenecks causing large uncertainties in the estimations can be identified through sensitivity analysis of the system. The method has been tested on 1D synthetic data and on field time-lapse seismic AVO data from the Gullfaks Field in the North Sea.

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Migration‐based traveltime waveform inversion of 2-D simple structures: A synthetic example

Geophysics ◽

10.1190/1.1444974 ◽

2001 ◽

Vol 66 (3) ◽

pp. 845-860 ◽

Cited By ~ 77

Author(s):

François Clément ◽

Guy Chavent ◽

Susana Gómez

Keyword(s):

Least Squares ◽

Waveform Inversion ◽

Domain Of Attraction ◽

Optimization Methods ◽

Data Sets ◽

Seismic Reflection Data ◽

Prestack Migration ◽

Reflection Data ◽

Full Wave ◽

The Time Domain

Migration‐based traveltime (MBTT) formulation provides algorithms for automatically determining background velocities from full‐waveform surface seismic reflection data using local optimization methods. In particular, it addresses the difficulty of the nonconvexity of the least‐squares data misfit function. The method consists of parameterizing the reflectivity in the time domain through a migration step and providing a multiscale representation for the smooth background velocity. We present an implementation of the MBTT approach for a 2-D finite‐difference (FD) full‐wave acoustic model. Numerical analysis on a 2-D synthetic example shows the ability of the method to find much more reliable estimates of both long and short wavelengths of the velocity than the classical least‐squares approach, even when starting from very poor initial guesses. This enlargement of the domain of attraction for the global minima of the least‐squares misfit has a price: each evaluation of the new objective function requires, besides the usual FD full‐wave forward modeling, an additional full‐wave prestack migration. Hence, the FD implementation of the MBTT approach presented in this paper is expected to provide a useful tool for the inversion of data sets of moderate size.

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