scholarly journals Near‐surface seismic properties for elastic wavefield decomposition: Estimates based on multicomponent land and seabed recordings

Geophysics ◽  
2003 ◽  
Vol 68 (6) ◽  
pp. 2073-2081 ◽  
Author(s):  
Remco Muijs ◽  
O. A. Johan Robertsson ◽  
Andrew Curtis ◽  
Klaus Holliger
Keyword(s):  
Material Properties ◽  
S Wave ◽  
Data Set ◽  
Near Surface ◽  
Seismic Properties ◽  
Wave Velocities ◽  
The North ◽  
Orthogonal Components ◽  
Multicomponent Data ◽  
Wavefield Decomposition

Accurate knowledge of the seismic material properties in the immediate vicinity of the receivers represents a prerequisite for elastic wavefield decomposition. We present strategies for estimating the elastic material properties for both land and seabed multicomponent seismic data. The proposed scheme for land data requires dense multicomponent geophone configurations, which allow spatial wavefield derivatives to be explicitly calculated. The required information can be obtained with four three‐component surface geophones positioned at the corners of a square, and a fifth geophone buried at a shallow depth below the center of the square. The technique yields local estimates of the near‐surface P‐ and S‐wave velocities, but the density cannot be constrained. Using a similar approach for four‐component (three orthogonal components of particle velocity plus pressure) seabed recordings allows the P‐ and S‐wave velocities as well as the density of the seafloor to be estimated. In this case, the proposed scheme does not require buried geophones, and it is applicable to multicomponent data recorded in routine seabed surveys. Compared to existing techniques, the new method allows the elastic sea‐floor properties to be more accurately determined, and it does not rely critically on the inclusion of large‐offset data. Numerical tests indicate that the proposed schemes are robust and yield accurate results, provided that the signal used for the inversion contains sufficient horizontal energy and can be clearly identified and separated from other signals. Although the schemes are designed for application on the first arrivals, they are, in principle, applicable to any data window containing isolated P‐ or S‐arrivals. The proposed scheme is successfully applied to a seabed data set acquired in the North Sea. In contrast, the application on a multicomponent land data set was unsuccessful, because of strong receiver‐to‐receiver variations in amplitude and phase, probably caused by differences in coupling and instrument response.

Shear‐wave velocity and density estimation from PS-wave AVO analysis: Application to an OBS dataset from the North Sea

Geophysics ◽  
2000 ◽  
Vol 65 (5) ◽  
pp. 1446-1454 ◽  
Author(s):  
Side Jin ◽  
G. Cambois ◽  
C. Vuillermoz
Keyword(s):  
Wave Velocity ◽  
North Sea ◽  
Random Noise ◽  
P Wave ◽  
S Wave ◽  
Data Set ◽  
Avo Analysis ◽  
The North ◽  
Avo Inversion ◽  
The North Sea

S-wave velocity and density information is crucial for hydrocarbon detection, because they help in the discrimination of pore filling fluids. Unfortunately, these two parameters cannot be accurately resolved from conventional P-wave marine data. Recent developments in ocean‐bottom seismic (OBS) technology make it possible to acquire high quality S-wave data in marine environments. The use of (S)-waves for amplitude variation with offset (AVO) analysis can give better estimates of S-wave velocity and density contrasts. Like P-wave AVO, S-wave AVO is sensitive to various types of noise. We investigate numerically and analytically the sensitivity of AVO inversion to random noise and errors in angles of incidence. Synthetic examples show that random noise and angle errors can strongly bias the parameter estimation. The use of singular value decomposition offers a simple stabilization scheme to solve for the elastic parameters. The AVO inversion is applied to an OBS data set from the North Sea. Special prestack processing techniques are required for the success of S-wave AVO inversion. The derived S-wave velocity and density contrasts help in detecting the fluid contacts and delineating the extent of the reservoir sand.

Shallow velocity structure and poisson's ratio at the Tarzana, California, strong-motion accelerometer site

1996 ◽  
Vol 86 (6) ◽  
pp. 1704-1713 ◽  
Author(s):  
R. D. Catchings ◽  
W. H. K. Lee
Keyword(s):  
Urban Areas ◽  
Velocity Structure ◽  
Strong Motion ◽  
P Wave ◽  
S Wave ◽  
Near Surface ◽  
Velocity Models ◽  
Wave Velocities ◽  
Poisson's Ratios ◽  
Poisson’S Ratios

Abstract The 17 January 1994, Northridge, California, earthquake produced strong ground shaking at the Cedar Hills Nursery (referred to here as the Tarzana site) within the city of Tarzana, California, approximately 6 km from the epicenter of the mainshock. Although the Tarzana site is on a hill and is a rock site, accelerations of approximately 1.78 g horizontally and 1.2 g vertically at the Tarzana site are among the highest ever instrumentally recorded for an earthquake. To investigate possible site effects at the Tarzana site, we used explosive-source seismic refraction data to determine the shallow (<70 m) P-and S-wave velocity structure. Our seismic velocity models for the Tarzana site indicate that the local velocity structure may have contributed significantly to the observed shaking. P-wave velocities range from 0.9 to 1.65 km/sec, and S-wave velocities range from 0.20 and 0.6 km/sec for the upper 70 m. We also found evidence for a local S-wave low-velocity zone (LVZ) beneath the top of the hill. The LVZ underlies a CDMG strong-motion recording site at depths between 25 and 60 m below ground surface (BGS). Our velocity model is consistent with the near-surface (<30 m) P- and S-wave velocities and Poisson's ratios measured in a nearby (<30 m) borehole. High Poisson's ratios (0.477 to 0.494) and S-wave attenuation within the LVZ suggest that the LVZ may be composed of highly saturated shales of the Modelo Formation. Because the lateral dimensions of the LVZ approximately correspond to the areas of strongest shaking, we suggest that the highly saturated zone may have contributed to localized strong shaking. Rock sites are generally considered to be ideal locations for site response in urban areas; however, localized, highly saturated rock sites may be a hazard in urban areas that requires further investigation.

Data Sets

2013 ◽  
Author(s):  
James B. Elsner ◽  
Thomas H. Jagger
Keyword(s):  
Model Building ◽  
Careful Analysis ◽  
Logical Structure ◽  
Fortran Program ◽  
Data Sets ◽  
Data Set ◽  
Near Surface ◽  
Wind Speeds ◽  
The North ◽  
The One

Hurricane data originate from careful analysis of past storms by operational meteorologists. The data include estimates of the hurricane position and intensity at 6-hourly intervals. Information related to landfall time, local wind speeds, damages, and deaths, as well as cyclone size, are included. The data are archived by season. Some effort is needed to make the data useful for hurricane climate studies. In this chapter, we describe the data sets used throughout this book. We show you a work flow that includes importing, interpolating, smoothing, and adding attributes. We also show you how to create subsets of the data. Code in this chapter is more complicated and it can take longer to run. You can skip this material on first reading and continue with model building in Chapter 7. You can return here when you have an updated version of the data that includes the most recent years. Most statistical models in this book use the best-track data. Here we describe these data and provide original source material. We also explain how to smooth and interpolate them. Interpolations are needed for regional hurricane analyses. The best-track data set contains the 6-hourly center locations and intensities of all known tropical cyclones across the North Atlantic basin, including the Gulf of Mexico and Caribbean Sea. The data set is called HURDAT for HURricane DATa. It is maintained by the U.S. National Oceanic and Atmospheric Administration (NOAA) at the National Hurricane Center (NHC). Center locations are given in geographic coordinates (in tenths of degrees) and the intensities, representing the one-minute near-surface (∼10 m) wind speeds, are given in knots (1 kt = .5144 m s−1) and the minimum central pressures are given in millibars (1 mb = 1 hPa). The data are provided in 6-hourly intervals starting at 00 UTC (Universal Time Coordinate). The version of HURDAT file used here contains cyclones over the period 1851 through 2010 inclusive. Information on the history and origin of these data is found in Jarvinen et al (1984). The file has a logical structure that makes it easy to read with a FORTRAN program. Each cyclone contains a header record, a series of data records, and a trailer record.

Near-surface scattering phenomena and implications for tunnel detection

2015 ◽  
Vol 3 (1) ◽  
pp. SF43-SF54 ◽  
Author(s):  
Shelby L. Peterie ◽  
Richard D. Miller
Keyword(s):  
Synthetic Data ◽  
P Wave ◽  
S Wave ◽  
Seismic Line ◽  
Data Set ◽  
Near Surface ◽  
Tunnel Detection ◽  
Impedance Contrast ◽  
Diffraction Imaging ◽  
Imaging Algorithms

Tunnel locations are accurately interpreted from diffraction sections of focused mode converted P- to S-wave diffractions from a perpendicular tunnel and P-wave diffractions from a nonperpendicular (oblique) tunnel. Near-surface tunnels are ideal candidates for diffraction imaging due to their small size relative to the seismic wavelength and large acoustic impedance contrast at the tunnel interface. Diffraction imaging algorithms generally assume that the velocities of the primary wave and the diffracted wave are approximately equal, and that the diffraction apex is recorded directly above the scatterpoint. Scattering phenomena from shallow tunnels with kinematic properties that violate these assumptions were observed in one field data set and one synthetic data set. We developed the traveltime equations for mode-converted and oblique diffractions and demonstrated a diffraction imaging algorithm designed for the roll-along style of acquisition. Potential processing and interpretation pitfalls specific to these diffraction types were identified. Based on our observations, recommendations were made to recognize and image mode-converted and oblique diffractions and accurately interpret tunnel depth, horizontal location, and azimuth with respect to the seismic line.

EXTRACTING SUB-SURFACE INFORMATION FROM LONG OFFSET P-WAVE DATA—A CHEAPER ALTERNATIVE TO MULTICOMPONENT OBC FOR EXPLORATION?

2002 ◽  
Vol 42 (1) ◽  
pp. 627
Author(s):  
R.G. Williams ◽  
G. Roberts ◽  
K. Hawkins
Keyword(s):  
Seismic Energy ◽  
Higher Order ◽  
P Wave ◽  
S Wave ◽  
Additional Information ◽  
Wave Data ◽  
Wave Velocities ◽  
Avo Inversion ◽  
Long Offset ◽  
Multicomponent Data

Seismic energy that has been mode converted from pwave to s-wave in the sub-surface may be recorded by multi-component surveys to obtain information about the elastic properties of the earth. Since the energy converted to s-wave is missing from the p-wave an alternative to recording OBC multi-component data is to examine p-wave data for the missing energy. Since pwave velocities are generally faster than s-wave velocities, then for a given reflection point the converted s-wave signal reaches the surface at a shorter offset than the equivalent p-wave information. Thus, it is necessary to record longer offsets for p-wave data than for multicomponent data in order to measure the same information.A non-linear, wide-angle (including post critical) AVO inversion has been developed that allows relative changes in p-wave velocities, s-wave velocities and density to be extracted from long offset p-wave data. To extract amplitudes at long offsets for this inversion it is necessary to image the data correctly, including correcting for higher order moveout and possibly anisotropy if it is present.The higher order moveout may itself be inverted to yield additional information about the anisotropy of the sub-surface.

Petrophysical properties variation of bitumen-bearing carbonates at increasing temperatures from laboratory to model

Geophysics ◽  
2020 ◽  
Vol 85 (5) ◽  
pp. MR297-MR308
Author(s):  
Roberta Ruggieri ◽  
Fabio Trippetta
Keyword(s):  
Central Italy ◽  
Carbonate Reservoir ◽  
P Wave ◽  
Petrophysical Properties ◽  
S Wave ◽  
Reservoir Properties ◽  
Velocity Change ◽  
Seismic Properties ◽  
Wave Velocities ◽  
Velocity Changes

Variations in reservoir seismic properties can be correlated to changes in saturated-fluid properties. Thus, the determination of variation in petrophysical properties of carbonate-bearing rocks is of interest to the oil exploration industry because unconventional oils, such as bitumen (HHC), are emerging as an alternative hydrocarbon reserve. We have investigated the temperature effects on laboratory seismic wave velocities of HHC-bearing carbonate rocks belonging to the Bolognano Formation (Majella Mountain, central Italy), which can be defined as a natural laboratory to study carbonate reservoir properties. We conduct an initial characterization in terms of porosity and density for the carbonate-bearing samples and then density and viscosity measurements for the residual HHC, extracted by HCl dissolution of the hosting rock. Acoustic wave velocities are recorded from ambient temperature to 90°C. Our acoustic velocity data point out an inverse relationship with temperature, and compressional (P) and shear (S) wave velocities show a distinct trend with increasing temperature depending on the amount of HHC content. Indeed, samples with the highest HHC content show a larger gradient of velocity changes in the temperature range of approximately 50°C–60°C, suggesting that the bitumen can be in a fluid state. Conversely, below approximately 50°C, the velocity gradient is lower because, at this temperature, bitumen can change its phase in a solid state. We also propose a theoretical model to predict the P-wave velocity change at different initial porosities for HHC-saturated samples suggesting that the velocity change mainly is related to the absolute volume of HHC.

Random objective waveform inversion of surface waves

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. EN49-EN61
Author(s):  
Yudi Pan ◽  
Lingli Gao
Keyword(s):  
Objective Function ◽  
Surface Waves ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Initial Model ◽  
S Wave ◽  
Data Set ◽  
Near Surface ◽  
Synthetic Test ◽  
Ground Penetrating

Full-waveform inversion (FWI) of surface waves is becoming increasingly popular among shallow-seismic methods. Due to a huge amount of data and the high nonlinearity of the objective function, FWI usually requires heavy computational costs and may converge toward a local minimum. To mitigate these problems, we have reformulated FWI under a multiobjective framework and adopted a random objective waveform inversion (ROWI) method for surface-wave characterization. Three different measure functions were used, whereas the combination of one measure function with one shot independently provided one of the [Formula: see text] objective functions ([Formula: see text] is the total number of shots). We have randomly chose and optimized one objective function at each iteration. We performed a synthetic test to compare the performance of the ROWI and conventional FWI approaches, which showed that the convergence of ROWI is faster and more robust compared with conventional FWI approaches. We also applied ROWI to a field data set acquired in Rheinstetten, Germany. ROWI successfully reconstructed the main geologic feature, a refilled trench, in the final result. The comparison between the ROWI result and a migrated ground-penetrating radar profile further proved the effectiveness of ROWI in reconstructing the near-surface S-wave velocity model. We also ran the same field example by using a poor initial model. In this case, conventional FWI failed whereas ROWI still reconstructed the subsurface model to a fairly good level, which highlighted the relatively low dependency of ROWI on the initial model.

Velocity anisotropy in shale determined from crosshole seismic and vertical seismic profile data

Geophysics ◽  
1990 ◽  
Vol 55 (4) ◽  
pp. 470-479 ◽  
Author(s):  
D. F. Winterstein ◽  
B. N. P. Paulsson
Keyword(s):  
Seismic Profile ◽  
P Wave ◽  
Isotropic Model ◽  
S Wave ◽  
Vertical Seismic Profile ◽  
Near Surface ◽  
S Waves ◽  
Velocity Anisotropy ◽  
Wave Velocities ◽  
Late Tertiary

Crosshole and vertical seismic profile (VST) data made possible accurate characterization of the elastic properties, including noticeable velocity anisotropy, of a near‐surface late Tertiary shale formation. Shear‐wave splitting was obvious in both crosshole and VSP data. In crosshole data, two orthologonally polarrized shear (S) waves arrived 19 ms in the uppermost 246 ft (75 m). Vertically traveling S waves of the VSP separated about 10 ms in the uppermost 300 ft (90 m) but remained at nearly constant separation below that level. A transversely isotropic model, which incorporates a rapid increase in S-wave velocities with depth but slow increase in P-wave velocities, closely fits the data over most of the measured interval. Elastic constants of the transvesely isotropic model show spherical P- and [Formula: see text]wave velocity surfaces but an ellipsoidal [Formula: see text]wave surface with a ratio of major to minor axes of 1.15. The magnitude of this S-wave anisotropy is consistent with and lends credence to S-wave anisotropy magnitudes deduced less directly from data of many sedimentary basins.

Bayesian closed-skew Gaussian inversion of seismic AVO data for elastic material properties

Geophysics ◽  
2010 ◽  
Vol 75 (1) ◽  
pp. R1-R11 ◽  
Author(s):  
Omid Karimi ◽  
Henning Omre ◽  
Mohsen Mohammadzadeh
Keyword(s):  
Elastic Material ◽  
Material Properties ◽  
P Wave ◽  
S Wave ◽  
High Dimensions ◽  
Seismic Amplitude ◽  
The North ◽  
Avo Inversion ◽  
Amplitude Versus Offset ◽  
The North Sea

Bayesian closed-skew Gaussian inversion is defined as a generalization of traditional Bayesian Gaussian inversion, which is used frequently in seismic amplitude-versus-offset (AVO) inversion. The new model captures skewness in the variables of interest; hence, the posterior model for log-transformed elastic material properties given seismic AVO data might be a skew probability density function. The model is analytically tractable, and this makes it applicable in high-dimensional 3D inversion problems. Assessment of the posterior models in high dimensions requires numerical approximations, however. The Bayesian closed-skew Gaussian inversion approach has been applied on real elastic material properties from a well in the Sleipner field in the North Sea. A comparison with results from traditional Bayesian Gaussian inversion shows that the mean square error of predictions of P-wave and S-wave velocities are reduced by a factor of two, although somewhat less for density predictions.

The impact of common‐offset migration on porosity estimation by AVO inversion

Geophysics ◽  
2001 ◽  
Vol 66 (3) ◽  
pp. 755-762 ◽  
Author(s):  
Arild Buland ◽  
Martin Landrø
Keyword(s):  
P Wave ◽  
High Porosity ◽  
S Wave ◽  
Seismic Line ◽  
Data Set ◽  
The North ◽  
Avo Inversion ◽  
Time Migration ◽  
The Impact ◽  
Porosity Estimation

The impact of prestack time migration on porosity estimation has been tested on a 2-D seismic line from the Valhall/Hod area in the North Sea. Porosity is estimated in the Cretaceous chalk section in a two‐step procedure. First, P-wave and S-wave velocity and density are estimated by amplitude variation with offset (AVO) inversion. These parameters are then linked to porosity through a petrophysical rock data base based on core plug analysis. The porosity is estimated both from unmigrated and prestack migrated seismic data. For the migrated data set, a standard prestack Kirchhoff time migration is used, followed by simple angle and amplitude corrections. Compared to modern high‐cost, true amplitude migration methods, this approach is faster and more practical. The test line is structurally fairly simple, with a maximum dip of 5°; but the results differ significantly, depending on whether migration is applied prior to the inversion. The maximum difference in estimated porosity is of the order of 10% (about 50% relative change). High‐porosity zones estimated from the unmigrated data were not present on the porosity section estimated from the migrated data.

Sign in / Sign up

Export Citation Format

Share Document