One‐Dimensional Reference Velocity Model and Precise Locations of Earthquake Hypocenters in the Kumaon–Garhwal Himalaya

2013 ◽  
Vol 103 (1) ◽  
pp. 328-339 ◽  
Author(s):  
P. Mahesh ◽  
S. S. Rai ◽  
K. Sivaram ◽  
Ajay Paul ◽  
Sandeep Gupta ◽  
...  
Keyword(s):  
Velocity Model ◽  
Garhwal Himalaya ◽  
One Dimensional ◽  
Reference Velocity

Time-migration velocity analysis by image-wave propagation of common-image gathers

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. VE161-VE171 ◽  
Author(s):  
J. Schleicher ◽  
J. C. Costa ◽  
A. Novais
Keyword(s):  
Wave Propagation ◽  
Seismic Event ◽  
Velocity Model ◽  
Migration Velocity ◽  
Velocity Analysis ◽  
Reference Velocity ◽  
Time Migration ◽  
Migration Velocity Analysis ◽  
Velocity Image ◽  
The Common

Image-wave propagation or velocity continuation describes the variation of the migrated position of a seismic event as a function of migration velocity. Image-wave propagation in the common-image gather (CIG) domain can be combined with residual-moveout analysis for iterative migration velocity analysis (MVA). Velocity continuation of CIGs leads to a detection of those velocities in which events flatten. Although image-wave continuation is based on the assumption of a constant migration velocity, the procedure can be applied in inhomogeneous media. For this purpose, CIGs obtained by migration with an inhomogeneous macrovelocity model are continued starting from a constant reference velocity. The interpretation of continued CIGs, as if they were obtained from residual migrations, leads to a correction formula that translates residual flattening velocities into absolute time-migration velocities. In this way, the migration velocity model can be improved iteratively until a satisfactory result is reached. With a numerical example, we found that MVA with iterative image continuation applied exclusively to selected CIGs can construct a reasonable migration velocity model from scratch, without the need to build an initial model from a previous conventional normal-moveout/dip-moveout velocity analysis.

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.

scholarly journals Reference P-wave velocity in coal seams at great depths in Jastrzebie coal mine

2019 ◽  
Vol 133 ◽  
pp. 01011
Author(s):  
Jakub Kokowski ◽  
Zbigniew Szreder ◽  
Elżbieta Pilecka
Keyword(s):  
Wave Velocity ◽  
Coal Mine ◽  
Velocity Model ◽  
P Wave ◽  
Coal Seams ◽  
Data Set ◽  
Seismic Profiling ◽  
Reference Velocity ◽  
P Wave Velocity ◽  
Geological Conditions

In the study, the determining of the reference velocity of the P-wave in coal seams used in seismic profiling to assess increases and decreases in relative stresses at large depths has been presented. The seismic profiling method proposed by Dubinski in 1989 covers a range of depth up to 970 m. At present, coal seams exploitation in Polish coal mines is conducted at greater depths, even exceeding 1200 m, which creates the necessity for a new reference velocity model. The study presents an empirical mathematical model of the change of the P-wave velocity in coal seams in the geological conditions of the Jastrzebie coal mine. A power model analogous to the Dubinski’s one was elaborated with new constants. The calculations included the results from 35 measurements of seismic profiling carried out in various coal seams of the Jastrzebie mine at depths from 640 to 1200 m. The results obtained cause changes in the result of calculations of seismic anomalies. Future validation of the proposed model with larger data set will be required.

The sensitivity of Born inversion to the choice of reference velocity: A simple example

Geophysics ◽  
1983 ◽  
Vol 48 (1) ◽  
pp. 36-38 ◽  
Author(s):  
A. B. Weglein ◽  
S. H. Gray
Keyword(s):  
Trade Off ◽  
One Dimensional ◽  
Surface Reflection ◽  
Born Model ◽  
Reflection Data ◽  
Reference Velocity ◽  
Perturbative Methods ◽  
The Difference

We examine the sensitivity of the Born model to the input background velocity. We use a one‐dimensional analytic example to point out the difference between a corrective procedure and merely a perturbative one. We examine various aspects of the sensitivity issue, including the trade‐off between velocity determination and mapping of reflector location. Although this problem is discussed within the context of the Born model, it is an issue common to all perturbative methods (e.g., migration methods) which transform surface reflection data into a map of subsurface reflectors.

Flow Through Rough Microchannels: A Lubrication Perspective

2003 ◽  
Author(s):  
S. Boedo
Keyword(s):  
Poiseuille Flow ◽  
Stokes Equations ◽  
Upper And Lower Bounds ◽  
Navier Stokes ◽  
Navier Stokes Equations ◽  
Test Setup ◽  
One Dimensional ◽  
Reference Velocity ◽  
Sample Application ◽  
Flow Through

This paper provides concise specifications where idealized Poiseuille flow is applicable in representing one-dimensional flow through wide, thin, rough microchannels subjected to prescribed pressures at the channel ends. Starting with the general (compressible) form of the Navier-Stokes equations, new expressions which discuss the effect of body forces on flow through thin channels are first presented, leading to upper and lower bounds on channel reference velocity where idealized Poiseuille flow dominates. These results are combined with previously published studies related to the predicability of flow through stochastically rough surfaces. An arbitrarily chosen microchannel model based loosely on a previously published experimental test setup is used as a sample application.

scholarly journals Reconstructing the primary reflections in seismic data by Marchenko redatuming and convolutional interferometry

Geophysics ◽  
2016 ◽  
Vol 81 (2) ◽  
pp. Q15-Q26 ◽  
Author(s):  
Giovanni Angelo Meles ◽  
Kees Wapenaar ◽  
Andrew Curtis
Keyword(s):  
Velocity Model ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Data Set ◽  
Seismic Reflection Data ◽  
Surface Reflection ◽  
Reflection Data ◽  
Reference Velocity ◽  
Time Migration ◽  
Recorded Data

State-of-the-art methods to image the earth’s subsurface using active-source seismic reflection data involve reverse time migration. This and other standard seismic processing methods such as velocity analysis provide best results only when all waves in the data set are primaries (waves reflected only once). A variety of methods are therefore deployed as processing to predict and remove multiples (waves reflected several times); however, accurate removal of those predicted multiples from the recorded data using adaptive subtraction techniques proves challenging, even in cases in which they can be predicted with reasonable accuracy. We present a new, alternative strategy to construct a parallel data set consisting only of primaries, which is calculated directly from recorded data. This obviates the need for multiple prediction and removal methods. Primaries are constructed by using convolutional interferometry to combine the first-arriving events of upgoing and direct-wave downgoing Green’s functions to virtual receivers in the subsurface. The required upgoing wavefields to virtual receivers are constructed by Marchenko redatuming. Crucially, this is possible without detailed models of the earth’s subsurface reflectivity structure: Similar to the most migration techniques, the method only requires surface reflection data and estimates of direct (nonreflected) arrivals between the virtual subsurface sources and the acquisition surface. We evaluate the method on a stratified synclinal model. It is shown to be particularly robust against errors in the reference velocity model used and to improve the migrated images substantially.

Imaging Noisy Seismic Data using a One Dimensional Inverse Scattering Algorithm

2015 ◽  
Vol 12 (4) ◽  
Author(s):  
Bogdan Nita ◽  
Christopher Smith
Keyword(s):  
Inverse Scattering ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Velocity Model ◽  
Noise Condition ◽  
One Dimensional ◽  
Amplitude Correction ◽  
Threshold Filter ◽  
Velocity Changes

We test the capability of an inverse scattering algorithm for imaging noisy seismic data. The algorithm does not require a velocity model or any other a priori information about the medium under investigation. We use three different geometries which capture different types of one-dimensional media with variable velocity. We show that the algorithm can precisely locate the interfaces and discover the correct velocity changes at those interfaces under moderate noise condition. When the signal to noise ratio is too small, the data is de-noised using a threshold filter and then imaged with excellent results. KEYWORDS: Seismic Imaging, Inversion, Amplitude Correction, Scattering Theory, Noise, Threshold Filter. 2000 MATHEMATICS SUBJECT CLASSIFICATION 86A22, 35J05, 35R30.

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