Exploiting surface consistency for surface-wave characterization and mitigation — Part 2: Application to 3D data

Geophysics ◽  
2017 ◽  
Vol 82 (1) ◽  
pp. V39-V50 ◽  
Author(s):  
Christine E. Krohn ◽  
Partha S. Routh
Keyword(s):  
Surface Wave ◽  
Low Frequency ◽  
Single Mode ◽  
Complex Surface ◽  
Surface Elevation ◽  
Inversion Method ◽  
Model Parameters ◽  
Nonlinear Inversion ◽  
Data Set ◽  
Near Surface

We present a case history demonstrating the 3D implementation of the surface-wave impulse estimation and removal (SWIPER) method. SWIPER is a tomographic inversion method that is able to predict and remove complex surface waves, which are multimodal and heterogeneous. The inversion generates surface-consistent model parameters, which correlate with near-surface elevation. These parameters include a surface map of the propagation velocity and attenuation values for each surface-wave mode as a function of frequency. The method also determines variations in source coupling as a function of frequency, which also correlate with the near-surface elevation changes. We show that the method works equally well with a fully sampled and decimated 3D dynamite-sourced data set. We start with a linear single-mode inversion and use the results to generate the starting model for a subsequent three-mode nonlinear inversion. The resulting velocity-dispersion grid has greater lateral resolution and extends to higher frequencies than that generated by a conventional array beam forming method. The propagation and source coupling parameters can be used together to predict the surface-wave waveforms, which are then adaptively subtracted from the data on a trace-to-trace basis. We demonstrate with decimated data that low-frequency reflections can be preserved, even when the data are highly aliased and would be removed by traditional multichannel filters.

Exploiting surface consistency for surface-wave characterization and mitigation — Part 1: Theory and 2D examples

Geophysics ◽  
2017 ◽  
Vol 82 (1) ◽  
pp. V21-V37 ◽  
Author(s):  
Christine E. Krohn ◽  
Partha S. Routh
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Single Mode ◽  
Complex Surface ◽  
Companion Paper ◽  
Inversion Method ◽  
Earth Model ◽  
Model Parameters ◽  
Quality Factors ◽  
Near Surface

We have developed a new tomographic inversion method that is able to determine the properties of complex surface waves, which are multimodal and heterogeneous. These properties can be used to generate a detailed near-surface earth model or to predict and remove the surface waves, while protecting reflection signals even with aliased data. The inversion assumes plane-wave physics and generates surface-consistent model parameters as a function of frequency. In this paper, we validate our method with 2D models and data. In a companion paper, we demonstrate its application to 3D data. Inversion for a single mode is linear, but the linearity does not hold at higher frequencies, where multiple modes interfere. However, single-mode inversion results can be used to create a starting model for the subsequent nonlinear multimode tomography. The resulting velocity-frequency grid has greater resolution compared with a beam-forming method. The dispersion curves can be used as input to a subsequent standard 1D surface-wave inversion to generate a velocity-depth model. The tomographic method also determines a grid of attenuation quality factors and variations in the source amplitude and bandwidth, which correlate with the near-surface elevation changes. The amplitude and phase properties can be used together to predict the surface-wave waveforms, which can then be adaptively subtracted from the data on a trace-to-trace basis.

scholarly journals Fast probabilistic petrophysical mapping of reservoirs from 3D seismic data

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. O1-O19 ◽  
Author(s):  
Mohammad S. Shahraeeni ◽  
Andrew Curtis ◽  
Gabriel Chao
Keyword(s):  
Seismic Data ◽  
Water Saturation ◽  
Random Noise ◽  
Clay Content ◽  
Inversion Method ◽  
Model Parameters ◽  
Nonlinear Inversion ◽  
Desktop Computer ◽  
Data Set ◽  
Mixture Density

A fast probabilistic inversion method for 3D petrophysical property prediction from inverted prestack seismic data has been developed and tested on a real data set. The inversion objective is to estimate the joint probability density function (PDF) of model vectors consisting of porosity, clay content, and water saturation components at each point in the reservoir, from data vectors with compressional- and shear-wave-impedance components that are obtained from the inversion of seismic data. The proposed inversion method is based on mixture density network (MDN), which is trained by a given set of training samples, and provides an estimate of the joint posterior PDF’s of the model parameters for any given data point. This method is much more time and memory efficient than conventional nonlinear inversion methods. The training data set is constructed using nonlinear petrophysical forward relations and includes different sources of uncertainty in the inverse problem such as variations in effective pressure, bulk modulus and density of hydrocarbon, and random noise in recorded data. Results showed that the standard deviations of all model parameters were reduced after inversion, which shows that the inversion process provides information about all parameters. The reduction of uncertainty in water saturation was smaller than that for porosity or clay content; nevertheless the maximum of the a posteriori (MAP) of model PDF clearly showed the boundary between brine saturated and oil saturated rocks at wellbores. The MAP estimates of different model parameters show the lateral and vertical continuity of these boundaries. Errors in the MAP estimate of different model parameters can be reduced using more accurate petrophysical forward relations. This fast, probabilistic, nonlinear inversion method can be applied to invert large seismic cubes for petrophysical parameters on a standard desktop computer.

scholarly journals Sensitivities of surface wave velocities to the medium parameters in a radially anisotropic spherical Earth and inversion strategies

2015 ◽  
Vol 58 (5) ◽  
Author(s):  
Sankar N. Bhattacharya
Keyword(s):  
Surface Wave ◽  
Phase Velocity ◽  
Group Velocity ◽  
Wave Velocity ◽  
Love Waves ◽  
P Wave ◽  
Model Parameters ◽  
Nonlinear Inversion ◽  
Wave Velocities ◽  
Spherical Earth

<p>Sensitivity kernels or partial derivatives of phase velocity (<em>c</em>) and group velocity (<em>U</em>) with respect to medium parameters are useful to interpret a given set of observed surface wave velocity data. In addition to phase velocities, group velocities are also being observed to find the radial anisotropy of the crust and mantle. However, sensitivities of group velocity for a radially anisotropic Earth have rarely been studied. Here we show sensitivities of group velocity along with those of phase velocity to the medium parameters <em>V<sub>SV</sub>, V<sub>SH </sub>, V<sub>PV</sub>, V<sub>PH , </sub></em><em>h</em><em> </em>and density in a radially anisotropic spherical Earth. The peak sensitivities for <em>U</em> are generally twice of those for <em>c</em>; thus <em>U</em> is more efficient than <em>c</em> to explore anisotropic nature of the medium. Love waves mainly depends on <em>V<sub>SH</sub></em> while Rayleigh waves is nearly independent of <em>V<sub>SH</sub></em> . The sensitivities show that there are trade-offs among these parameters during inversion and there is a need to reduce the number of parameters to be evaluated independently. It is suggested to use a nonlinear inversion jointly for Rayleigh and Love waves; in such a nonlinear inversion best solutions are obtained among the model parameters within prescribed limits for each parameter. We first choose <em>V<sub>SH</sub></em>, <em>V<sub>SV </sub></em>and <em>V<sub>PH</sub></em> within their corresponding limits; <em>V<sub>PV</sub></em> and <em>h</em> can be evaluated from empirical relations among the parameters. The density has small effect on surface wave velocities and it can be considered from other studies or from empirical relation of density to average P-wave velocity.</p>

Nonlinear velocity inversion by a two‐step Monte Carlo method.

Geophysics ◽  
1994 ◽  
Vol 59 (4) ◽  
pp. 577-590 ◽  
Author(s):  
Side Jin ◽  
Raul Madariaga
Keyword(s):  
Monte Carlo ◽  
Velocity Model ◽  
Small Scale ◽  
Inversion Method ◽  
Nonlinear Inversion ◽  
Ray Theory ◽  
Data Set ◽  
Seismic Reflection Data ◽  
Velocity Models ◽  
Reference Velocity

Seismic reflection data contain information on small‐scale impedance variations and a smooth reference velocity model. Given a reference velocity model, the reflectors can be obtained by linearized migration‐inversion. If the reference velocity is incorrect, the reflectors obtained by inverting different subsets of the data will be incoherent. We propose to use the coherency of these images to invert for the background velocity distribution. We have developed a two‐step iterative inversion method in which we separate the retrieval of small‐scale variations of the seismic velocity from the longer‐period reference velocity model. Given an initial background velocity model, we use a waveform misfit‐functional for the inversion of small‐scale velocity variations. For this linear step we use the linearized migration‐inversion method based on ray theory that we have recently developed with Lambaré and Virieux. The reference velocity model is then updated by a Monte Carlo inversion method. For the nonlinear inversion of the velocity background, we introduce an objective functional that measures the coherency of the short wavelength components obtained by inverting different common shot gathers at the same locations. The nonlinear functional is calculated directly in migrated data space to avoid expensive numerical forward modeling by finite differences or ray theory. Our method is somewhat similar to an iterative migration velocity analysis, but we do an automatic search for relatively large‐scale 1-D reference velocity models. We apply the nonlinear inversion method to a marine data set from the North Sea and also show that nonlinear inversion can be applied to realistic scale data sets to obtain a laterally heterogeneous velocity model with a reasonable amount of computer time.

Predictive removal of scattered noise

Geophysics ◽  
2006 ◽  
Vol 71 (2) ◽  
pp. V41-V49 ◽  
Author(s):  
Gérard C. Herman ◽  
Colin Perkins
Keyword(s):  
Mathematical Model ◽  
Wave Propagation ◽  
Surface Wave ◽  
Seismic Data ◽  
Coherent Noise ◽  
Data Set ◽  
Near Surface ◽  
Unique Data ◽  
Surface Wave Propagation ◽  
Petroleum Development

Land seismic data can be severely contaminated with coherent noise. We discuss a deterministic technique to predict and remove scattered coherent noise from land seismic data based on a mathematical model of near-surface wave propagation. We test the method on a unique data set recorded by Petroleum Development of Oman in the Qarn Alam area (with shots and receivers on the same grid), and we conclude that it effectively reduces scattered noise without smearing reflection energy.

scholarly journals Geostatistical FDEM inversion: a three-dimensional real case application

2021 ◽  
Author(s):  
Leonardo Azevedo ◽  
João Narciso ◽  
Ellen Van De Vijver
Keyword(s):  
Spatial Distribution ◽  
Physical Properties ◽  
Three Dimensional ◽  
Real Case ◽  
Small Scale ◽  
Inversion Method ◽  
Two Dimensional ◽  
Data Set ◽  
Near Surface ◽  
Direct Observations

&lt;p&gt;The near surface is a complex and often highly heterogeneous system as its current status results from interacting processes of both natural and anthropogenic origin. Effective sustainable management and land use planning, especially in urban environments, demands high-resolution subsurface property models enabling to capture small-scale processes of interest. The modelling methods based only on discrete direct observations from conventional invasive sampling techniques have limitations with respect to capturing the spatial variability of these systems. Near-surface geophysical surveys are emerging as powerful techniques to provide indirect measurements of subsurface properties. Their integration with direct observations has the potential for better predicting the spatial distribution of the subsurface physical properties of interest and capture the heterogeneities of the near-surface systems.&lt;/p&gt;&lt;p&gt;Within the most common geophysical techniques, frequency-domain electromagnetic (FDEM) induction methods have demonstrated their potential and efficiency to characterize heterogeneous deposits due to their simultaneous sensitivity to electrical conductivity (EC) and magnetic susceptibility (MS). The inverse modelling of FDEM data based on geostatistical techniques allows to go beyond conventional analyses of FDEM data. This geostatistical FDEM inversion method uses stochastic sequential simulation and co-simulation to perturbate the model parameter space and the corresponding FDEM forward model solutions, including both the synthetic FDEM responses and their sensitivity to changes on the physical properties of interest. A stochastic optimization driven by the misfit between true and synthetic FDEM data is applied to iterative towards a final subsurface model. This method not only improve the confidence of the obtained EC and MS inverted models but also allows to quantify the uncertainty related to them. Furthermore, taking into account spatial correlations enables more accurate prediction of the spatial distribution of subsurface properties and a more realistic reconstruction of small-scale spatial variations, even when considering highly heterogeneous near surface systems. Moreover, a main advantage of this iterative geostatistical FDEM inversion method is its ability to flexibly integrate data with different resolution in the same framework.&lt;/p&gt;&lt;p&gt;In this work, we apply this iterative geostatistical FDEM inversion technique, which has already been successfully demonstrated for one- and two-dimensional applications, to invert a real case FDEM data set in three dimensions. The FDEM survey data set was collected on a site located near Knowlton (Dorset, UK), which is geologically characterized by Cretaceous chalk overlain by Quaternary siliciclastic sand deposits. The subsurface at the site is known to contain several archaeological features, which produces strong local in-phase anomalies in the FDEM survey data. We discuss the particular challenges involved in the three-dimensional application of the inversion method to a real case data set and compare our results against previously obtained ones for one- and two-dimensional approximations.&lt;/p&gt;

Advances in surface-wave tomography for near-surface applications

2021 ◽  
Vol 40 (8) ◽  
pp. 567-575
Author(s):  
Myrto Papadopoulou ◽  
Farbod Khosro Anjom ◽  
Mohammad Karim Karimpour ◽  
Valentina Laura Socco
Keyword(s):  
Surface Wave ◽  
Wave Velocity ◽  
P Wave ◽  
Seismic Exploration ◽  
S Wave ◽  
Data Set ◽  
Near Surface ◽  
Processing Times ◽  
Spatial Coverage ◽  
Wave Tomography

Surface-wave (SW) tomography is a technique that has been widely used in the field of seismology. It can provide higher resolution relative to the classical multichannel SW processing and inversion schemes that are usually adopted for near-surface applications. Nevertheless, the method is rarely used in this context, mainly due to the long processing times needed to pick the dispersion curves as well as the inability of the two-station processing to discriminate between higher SW modes. To make it efficient and to retrieve pseudo-2D/3D S-wave velocity (VS) and P-wave velocity (VP) models in a fast and convenient way, we develop a fully data-driven two-station dispersion curve estimation, which achieves dense spatial coverage without the involvement of an operator. To handle higher SW modes, we apply a dedicated time-windowing algorithm to isolate and pick the different modes. A multimodal tomographic inversion is applied to estimate a VS model. The VS model is then converted to a VP model with the Poisson's ratio estimated through the wavelength-depth method. We apply the method to a 2D seismic exploration data set acquired at a mining site, where strong lateral heterogeneity is expected, and to a 3D pilot data set, recorded with state-of-the-art acquisition technology. We compare the results with the ones retrieved from classical multichannel analysis.

scholarly journals Improved seismic profiling by minimally invasive multimodal surface wave method with standard penetration test source (MMSW-SPT)

2016 ◽  
Vol 208 (3) ◽  
pp. 1308-1312 ◽  
Author(s):  
Shibin Lin ◽  
Jeramy C. Ashlock
Keyword(s):  
Surface Wave ◽  
Minimally Invasive ◽  
Single Mode ◽  
Standard Penetration Test ◽  
Wave Method ◽  
Penetration Test ◽  
Testing Methods ◽  
Surface Wave Dispersion ◽  
Near Surface ◽  
Surface Wave Method

Abstract Surface waves propagating in layered media inherently possess multimodal dispersion characteristics. However, traditional surface wave testing methods employing measurements at the free surface usually capture only a single apparent dispersion curve, especially when using short geophone arrays common to near surface and geotechnical-scale investigations. Such single-mode or fragmented multimode apparent dispersion curves contain only a fraction of the possible dispersion information, thus limiting the accuracy of inverted profiles. To enable more robust measurement of higher Rayleigh-wave modes, a recently developed hybrid minimally invasive multimodal surface wave method is combined herein with the widely used geotechnical standard penetration test (SPT), which is employed as a practical and ubiquitous downhole source. Upon superimposing surface wave dispersion data for a range of SPT impact depths within the soil, higher modes can be measured more consistently and reliably relative to traditional non-invasive testing methods. As a result, misidentification of multiple dispersion modes can be practically eliminated, significantly improving the accuracy and certainty of inversion results.

A ray inversion method for refraction analysis

Geophysics ◽  
1985 ◽  
Vol 50 (11) ◽  
pp. 1701-1720 ◽  
Author(s):  
Glyn M. Jones ◽  
D. B. Jovanovich
Keyword(s):  
Field Data ◽  
Opposite Sign ◽  
Synthetic Data ◽  
Head Wave ◽  
Inversion Method ◽  
Data Set ◽  
Near Surface ◽  
Left And Right ◽  
A New Technique ◽  
Good Agreement

A new technique is presented for the inversion of head‐wave traveltimes to infer near‐surface structure. Traveltimes computed along intersecting pairs of refracted rays are used to reconstruct the shape of the first refracting horizon beneath the surface and variations in refractor velocity along this boundary. The information derived can be used as the basis for further processing, such as the calculation of near‐surface static delays. One advantage of the method is that the shape of the refractor is determined independently of the refractor velocity. With multifold coverage, rapid lateral changes in refractor geometry or velocity can be mapped. Two examples of the inversion technique are presented: one uses a synthetic data set; the other is drawn from field data shot over a deep graben filled with sediment. The results obtained using the synthetic data validate the method and support the conclusions of an error analysis, in which errors in the refractor velocity determined using receivers to the left and right of the shots are of opposite sign. The true refractor velocity therefore falls between the two sets of estimates. The refraction image obtained by inversion of the set of field data is in good agreement with a constant‐velocity reflection stack and illustrates that the ray inversion method can handle large lateral changes in refractor velocity or relief.

Some practical aspects of prestack waveform inversion using a genetic algorithm: An example from the east Texas Woodbine gas sand

Geophysics ◽  
1999 ◽  
Vol 64 (2) ◽  
pp. 326-336 ◽  
Author(s):  
Subhashis Mallick
Keyword(s):  
Genetic Algorithm ◽  
Biological Evolution ◽  
Synthetic Data ◽  
Posteriori Probability ◽  
Inversion Method ◽  
Model Parameters ◽  
East Texas ◽  
Data Set ◽  
A Posteriori Probability ◽  
Prestack Inversion

In this paper, a prestack inversion method using a genetic algorithm (GA) is presented, and issues relating to the implementation of prestack GA inversion in practice are discussed. GA is a Monte‐Carlo type inversion, using a natural analogy to the biological evolution process. When GA is cast into a Bayesian framework, a priori information of the model parameters and the physics of the forward problem are used to compute synthetic data. These synthetic data can then be matched with observations to obtain approximate estimates of the marginal a posteriori probability density (PPD) functions in the model space. Plots of these PPD functions allow an interpreter to choose models which best describe the specific geologic setting and lead to an accurate prediction of seismic lithology. Poststack inversion and prestack GA inversion were applied to a Woodbine gas sand data set from East Texas. A comparison of prestack inversion with poststack inversion demonstrates that prestack inversion shows detailed stratigraphic features of the subsurface which are not visible on the poststack inversion.

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