Scholte wave velocity inversion for a near surface S‐velocity model and PS‐statics

2000 ◽  
Author(s):  
Everhard Muyzert
Keyword(s):  
Wave Velocity ◽  
Velocity Model ◽  
Near Surface ◽  
Velocity Inversion ◽  
Scholte Wave

scholarly journals The potential of seismic cross-hole tomography for geotechnical site investigation

2019 ◽  
Vol 92 ◽  
pp. 18006
Author(s):  
Yannick Choy Hing Ng ◽  
William Danovan ◽  
Taeseo Ku
Keyword(s):  
Wave Velocity ◽  
Site Investigation ◽  
Velocity Model ◽  
P Wave ◽  
Reconstruction Technique ◽  
Sufficient Information ◽  
Eastern Region ◽  
S Wave ◽  
Near Surface ◽  
Soil Layers

Seismic cross-hole tomography has been commonly used in oil and gas exploration and the mining industry for the detection of precious resources. For near-surface geotechnical site investigation, this geophysical method is relatively new and can be used to supplement traditional methods such as the standard penetration test, coring and sampling, thus improving the effectiveness of site characterization. This paper presents a case study which was carried out on a reclaimed land in the Eastern region of Singapore. A seismic cross-hole test was performed by generating both compressional waves and shear waves into the ground. The signals were interpreted by using first-arrival travel time wave tomography and the arrival times were subsequently inverted using Simultaneous Iterative Reconstruction Technique (SIRT). A comparison with the borehole logging data indicated that P-wave velocity model cannot provide sufficient information about the soil layers, especially when the ground water table is near the surface. The S-wave velocity model seemed to agree quite well with the variation in the SPT-N value and could identify to a certain extent the interface between the different soil layers. Finally, P-wave and S-wave velocities are used to compute the Poisson's ratio distribution which gave a good indication of the degree of saturation of the soil.

Passive seismic imaging using microearthquakes

Geophysics ◽  
1995 ◽  
Vol 60 (4) ◽  
pp. 1178-1186 ◽  
Author(s):  
M. Reza Daneshvar ◽  
Clarence S. Clay ◽  
Martha K. Savage
Keyword(s):  
Wave Velocity ◽  
Signal To Noise Ratio ◽  
Velocity Model ◽  
P Wave ◽  
Seismic Signals ◽  
Subsurface Structure ◽  
Near Surface ◽  
P Wave Velocity ◽  
Recording Station ◽  
Passive Seismic

We have developed a method of processing seismic signals generated by microearthquakes to image local subsurface structure beneath a recording station. This technique uses the autocorrelation of the vertically traveling earthquake signals to generate pseudoreflection seismograms that can be interpreted for subsurface structure. Processed pseudoreflection data, from microearthquakes recorded in the island of Hawaii, show consistent reflectivity patterns that are interpreted as near‐surface horizontal features. Forward modeling of the pseudoreflection data results in a P‐wave velocity model that shows reasonable agreement with the velocity model derived from a refraction study in the region. Usable signal‐to‐noise ratio is obtained down to 2 s. A shear‐wave velocity model was also generated by applying this technique to horizontal component data.

scholarly journals A P-wave velocity model of the upper crust of the Sannio region (Southern Apennines, Italy)

1998 ◽  
Vol 41 (4) ◽  
Author(s):  
G. Iannaccone ◽  
L. Improta ◽  
P. Capuano ◽  
A. Zollo ◽  
G. Biella ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Seismic Velocity ◽  
Carbonate Platform ◽  
Velocity Model ◽  
P Wave ◽  
Seismic Velocities ◽  
Upper Crust ◽  
Near Surface ◽  
P Wave Velocity ◽  
Apulia Platform

This paper describes the results of a seismic refraction profile conducted in October 1992 in the Sannio region, Southern Italy, to obtain a detailed P-wave velocity model of the upper crust. The profile, 75 km long, extended parallel to the Apenninic chain in a region frequently damaged in historical time by strong earthquakes. Six shots were fired at five sites and recorded by a number of seismic stations ranging from 41 to 71 with a spacing of 1-2 km along the recording line. We used a two-dimensional raytracing technique to model travel times and amplitudes of first and second arrivals. The obtained P-wave velocity model has a shallow structure with strong lateral variations in the southern portion of the profile. Near surface sediments of the Tertiary age are characterized by seismic velocities in the 3.0-4.1 km/s range. In the northern part of the profile these deposits overlie a layer with a velocity of 4.8 km/s that has been interpreted as a Mesozoic sedimentary succession. A high velocity body, corresponding to the limestones of the Western Carbonate Platform with a velocity of 6 km/s, characterizes the southernmost part of the profile at shallow depths. At a depth of about 4 km the model becomes laterally homogeneous showing a continuous layer with a thickness in the 3-4 km range and a velocity of 6 km/s corresponding to the Meso-Cenozoic limestone succession of the Apulia Carbonate Platform. This platform appears to be layered, as indicated by an increase in seismic velocity from 6 to 6.7 km/s at depths in the 6-8 km range, that has been interpreted as a lithological transition from limestones to Triassic dolomites and anhydrites of the Burano formation. A lower P-wave velocity of about 5.0-5.5 km/s is hypothesized at the bottom of the Apulia Platform at depths ranging from 10 km down to 12.5 km; these low velocities could be related to Permo-Triassic siliciclastic deposits of the Verrucano sequence drilled at the bottom of the Apulia Platform in the Apulia Foreland.

Seabed property estimation from ambient-noise recordings: Part 2 — Scholte-wave spectral-ratio inversion

Geophysics ◽  
2007 ◽  
Vol 72 (4) ◽  
pp. U47-U53 ◽  
Author(s):  
Everhard Muyzert
Keyword(s):  
Ambient Noise ◽  
Spectral Ratio ◽  
Shear Velocity ◽  
Velocity Model ◽  
Surface Shear ◽  
Data Set ◽  
Recording Time ◽  
Near Surface ◽  
Scholte Waves ◽  
Scholte Wave

Having knowledge of the near-surface shear-velocity model is useful for various seismic processing methods such as shear-wave static estimation, wavefield separation, and geohazard prediction. I present a new method to derive a 2D near-surface shear-velocity model from ambient-noise recordings made at the seafloor. The method relies on inverting horizontal- and vertical-amplitude spectra of Scholte waves propagating in the seafloor. I compare the commonly used horizontal-over-vertical spectral ratio with three alternative spectral-ratio definitions through modeling. The modeling shows that the vertical-over-total spectral ratio has some favorable properties for inversion. I describe a nonlinear inversion method for the vertical-to-total spectral ratio of the Scholte waves and apply it to an ambient-noise data set recorded by an ocean-bottom-cable (OBC) system. A 1D near-surface shear-velocity model is derived through a joint inversion of the spectral-ratio and phase-velocity data. A 2D shear-velocity model is obtained through a local inversion of the spectral ratios averaged over small groups of receivers and shows evidence for lateral heterogeneity. The newly developed method for deriving near-surface shear-velocity distribution by inverting the Scholte-wave spectral ratio measured from seabed noise provides great opportunities for estimating the shallow-seabed shear velocity in deep water. Another benefit of the method is that, with the OBC system, no additional hardware is needed; only additional recording time is required. In this case, half an hour is sufficient.

Method of building three-dimensional near-surface S-wave velocity model via down-hole surveys

2018 ◽  
Author(s):  
Min Li ◽  
Miaoyu Chen ◽  
Xiaoyang Wang ◽  
Zhichao Yang ◽  
Jiangli Chen ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Three Dimensional ◽  
Velocity Model ◽  
S Wave ◽  
Near Surface

scholarly journals Time-average velocity estimation through surface-wave analysis: Part 1 — S-wave velocity

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. U49-U59 ◽  
Author(s):  
Laura Valentina Socco ◽  
Cesare Comina ◽  
Farbod Khosro Anjom
Keyword(s):  
Wave Velocity ◽  
Average Velocity ◽  
Field Data ◽  
Velocity Model ◽  
Velocity Estimation ◽  
S Wave ◽  
Simple Method ◽  
Near Surface ◽  
Velocity Models ◽  
Time Average

In some areas, the estimation of static corrections for land seismic data is a critical step of the processing workflow. It often requires the execution of additional surveys and data analyses. Surface waves (SWs) in seismic records can be processed to extract local dispersion curves (DCs) that can be used to estimate near-surface S-wave velocity models. Here we focus on the direct estimation of time-average S-wave velocity models from SW DCs without the need to invert the data. Time-average velocity directly provides the value of one-way time, given a datum plan depth. The method requires the knowledge of one 1D S-wave velocity model along the seismic line, together with the relevant DC, to estimate a relationship between SW wavelength and investigation depth on the time-average velocity model. This wavelength/depth relationship is then used to estimate all the other time-average S-wave velocity models along the line directly from the DCs by means of a data transformation. This approach removes the need for extensive data inversion and provides a simple method suitable for industrial workflows. We tested the method on synthetic and field data and found that it is possible to retrieve the time-average velocity models with uncertainties less than 10% in sites with laterally varying velocities. The error on one-way times at various depths of the datum plan retrieved by the time-average velocity models is mostly less than 5 ms for synthetic and field data.

Computing near-surface velocity models for S-wave static corrections using raypath interferometry

Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. U23-U34
Author(s):  
Raul Cova ◽  
David Henley ◽  
Kristopher A. Innanen
Keyword(s):  
Wave Velocity ◽  
Velocity Model ◽  
P Wave ◽  
Surface Velocity ◽  
S Wave ◽  
Converted Wave ◽  
Near Surface ◽  
Velocity Models ◽  
Consistent Solution ◽  
Static Corrections

A near-surface velocity model is one of the typical products generated when computing static corrections, particularly in the processing of PP data. Critically refracted waves are the input usually needed for this process. In addition, for the converted PS mode, S-wave near-surface corrections must be applied at the receiver locations. In this case, however, critically refracted S-waves are difficult to identify when using P-wave energy sources. We use the [Formula: see text]-[Formula: see text] representation of the converted-wave data to capture the intercept-time differences between receiver locations. These [Formula: see text]-differences are then used in the inversion of a near-surface S-wave velocity model. Our processing workflow provides not only a set of raypath-dependent S-wave static corrections, but also a velocity model that is based on those corrections. Our computed near-surface S-wave velocity model can be used for building migration velocity models or to initialize elastic full-waveform inversions. Our tests on synthetic and field data provided superior results to those obtained by using a surface-consistent solution.

VSP traveltime inversion: Near‐surface issues

Geophysics ◽  
2004 ◽  
Vol 69 (2) ◽  
pp. 345-351 ◽  
Author(s):  
Geoff J.M. Moret ◽  
William P. Clement ◽  
Michael D. Knoll ◽  
Warren Barrash
Keyword(s):  
Wave Velocity ◽  
Regularization Parameter ◽  
Velocity Model ◽  
P Wave ◽  
Seismic Profiles ◽  
Seismic Reflection Data ◽  
Near Surface ◽  
Shallow Subsurface ◽  
P Wave Velocity ◽  
Velocity Information

P‐wave velocity information obtained from vertical seismic profiles (VSPs) can be useful in imaging subsurface structure, either by directly detecting changes in the subsurface or as an aid to the interpretation of seismic reflection data. In the shallow subsurface, P‐wave velocity can change by nearly an order of magnitude over a short distance, so curved rays are needed to accurately model VSP traveltimes. We used a curved‐ray inversion to estimate the velocity profile and the discrepancy principle to estimate the data noise level and to choose the optimum regularization parameter. The curved‐ray routine performed better than a straight‐ray inversion for synthetic models containing high‐velocity contrasts. The application of the inversion to field data produced a velocity model that agreed well with prior information. These results show that curved‐ray inversion should be used to obtain velocity information from VSPs in the shallow subsurface.

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