scholarly journals Interferometric velocity analysis using physical and nonphysical energy

Geophysics ◽  
2011 ◽  
Vol 76 (1) ◽  
pp. SA35-SA49 ◽  
Author(s):  
Simon King ◽  
Andrew Curtis ◽  
Travis L. Poole
Keyword(s):  
Green’S Function ◽  
Layer Thickness ◽  
Green's Function ◽  
Velocity Analysis ◽  
Seismic Reflection Data ◽  
Reflected Waves ◽  
Interval Velocity ◽  
Reflection Data ◽  
Receiver Array ◽  
Marine Seismic

In controlled-source seismic interferometry, waves from a surrounding boundary of sources recorded at two receivers are crosscorrelated and summed to synthesize the interreceiver Green’s function. Deviations of physically realistic source and receiver geometries from those required by theory result in errors in the Green’s function estimate. These errors are manifested as apparent energy that could not have propagated between receiver locations — so-called nonphysical energy. We have developed a novel method of velocity analysis that uses both the physical and nonphysical wavefield energy in the crosscorrelated data generated between receiver pairs. This method is used to constrain the root-mean-square (rms) velocity and layer thickness of a locally 1D medium. These estimates are used to compute the piece-wise constant interval velocity. Instead of suppressing multiple energy as in conventional common midpoint velocity analysis, the method uses the multiply reflected wavefield to further constrain the rms velocity and layer-thickness estimates. In particular, we determined that the nonphysical energy contains useful physical information. By using the nonphysical energy associated with the truncation of the source boundary and the crosscorrelation of reflected waves, a better-defined estimate of the rms velocity and layer thickness is achieved. Because this energy is excited far from the receiver pair, the technique may be ideally suited to long-offset seismic reflection data. We found that interferometric velocity analysis works best to characterize the first few layers beneath a receiver array. We have considered an acquisition configuration that can be used in a marine seismic setting.

scholarly journals Depth characterization of shallow aquifers with seismic reflection, Part I—The failure of NMO velocity analysis and quantitative error prediction

Geophysics ◽  
2002 ◽  
Vol 67 (1) ◽  
pp. 89-97 ◽  
Author(s):  
John H. Bradford
Keyword(s):  
Layer Thickness ◽  
Water Table ◽  
Seismic Reflection ◽  
Negative Impact ◽  
Compressional Wave ◽  
Unconsolidated Sediments ◽  
Velocity Analysis ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Saturated Layer

As seismic reflection data become more prevalent as input for quantitative environmental and engineering studies, there is a growing need to assess and improve the accuracy of reflection processing methodologies. It is common for compressional‐wave velocities to increase by a factor of four or more where shallow, unconsolidated sediments change from a dry or partially water‐saturated regime to full saturation. While this degree of velocity contrast is rare in conventional seismology, it is a common scenario in shallow environments and leads to significant problems when trying to record and interpret reflections within about the first 30 m below the water table. The problem is compounded in shallow reflection studies where problems primarily associated with surface‐related noise limit the range of offsets we can use to record reflected energy. For offset‐to‐depth ratios typically required to record reflections originating in this zone, the assumptions of NMO velocity analysis are violated, leading to very large errors in depth and layer thickness estimates if the Dix equation is assumed valid. For a broad range of velocity profiles, saturated layer thickness will be overestimated by a minimum of 10% if the boundary of interest is <30 m below the water table. The error increases rapidly as the boundary shallows and can be very large (>100%) if the saturated layer is <10 m thick. This degree of error has a significant and negative impact if quantitative interpretations of aquifer geometry are used in aquifer evaluation such as predictive groundwater flow modeling or total resource estimates.

scholarly journals Velocity analysis using both reflections and refractions in seismic interferometry

Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. SA83-SA96 ◽  
Author(s):  
Simon King ◽  
Andrew Curtis
Keyword(s):  
Green’S Function ◽  
Half Space ◽  
Green's Function ◽  
Green's Functions ◽  
Seismic Velocity ◽  
Green’S Functions ◽  
Oil Field ◽  
Velocity Analysis ◽  
Space Model ◽  
Receiver Array

The Green’s function between two receiver locations can be estimated by crosscorrelating and summing the recorded Green’s functions from sources on a boundary that surrounds the receiver pair. We demonstrate that when two receivers are positioned far from the source boundary in a marine-type acquisition geometry, the crosscorrelations (the Green’s functions before summation over the source boundary) are dominated by reflected energy which can be used in a semblance analysis to determine the seismic velocity and thickness of the first layer. When these crosscorrelations are summed over the boundary of sources, the resulting Green’s function estimates along a receiver array contain nonphysical or spurious refracted energy. We illustrate that by using a further semblance analysis, the most prominent nonphysical refracted energy occurs prior to the direct arrival and determines the remaining refraction velocities of deeper layers (or interval velocities in the case of a subsurface with homogeneous layers). We demonstrate the velocity analysis procedure on a single layer over half-space model, a three layer over a half-space model, and a more realistic model based on a North Sea oil field.

scholarly journals Data-driven wavefield focusing and imaging with multidimensional deconvolution: Numerical examples for reflection data with internal multiples

Geophysics ◽  
2014 ◽  
Vol 79 (3) ◽  
pp. WA107-WA115 ◽  
Author(s):  
Filippo Broggini ◽  
Roel Snieder ◽  
Kees Wapenaar
Keyword(s):  
Green’S Function ◽  
Green's Function ◽  
Imaging Techniques ◽  
Velocity Model ◽  
Single Scattering ◽  
Data Driven ◽  
Multiple Reflections ◽  
Prestack Migration ◽  
Reflection Data ◽  
Internal Multiples

Standard imaging techniques rely on the single scattering assumption. This requires that the recorded data do not include internal multiples, i.e., waves that have bounced multiple times between reflectors before reaching the receivers at the acquisition surface. When multiple reflections are present in the data, standard imaging algorithms incorrectly image them as ghost reflectors. These artifacts can mislead interpreters in locating potential hydrocarbon reservoirs. Recently, we introduced a new approach for retrieving the Green’s function recorded at the acquisition surface due to a virtual source located at depth. We refer to this approach as data-driven wavefield focusing. Additionally, after applying source-receiver reciprocity, this approach allowed us to decompose the Green’s function at a virtual receiver at depth in its downgoing and upgoing components. These wavefields were then used to create a ghost-free image of the medium with either crosscorrelation or multidimensional deconvolution, presenting an advantage over standard prestack migration. We tested the robustness of our approach when an erroneous background velocity model is used to estimate the first-arriving waves, which are a required input for the data-driven wavefield focusing process. We tested the new method with a numerical example based on a modification of the Amoco model.

scholarly journals On the Marchenko equation for multicomponent single-sided reflection data

2014 ◽  
Vol 199 (3) ◽  
pp. 1367-1371 ◽  
Author(s):  
Kees Wapenaar ◽  
Evert Slob
Keyword(s):  
Green’S Function ◽  
Recent Work ◽  
Green's Function ◽  
Scattered Field ◽  
Iterative Solution ◽  
Virtual Source ◽  
Function Representation ◽  
Reflection Data ◽  
Marchenko Equation ◽  
Multicomponent Data

Abstract Recent work on the Marchenko equation has shown that the scalar 3-D Green's function for a virtual source in the subsurface can be retrieved from the single-sided reflection response at the surface and an estimate of the direct arrival. Here, we discuss the first steps towards extending this result to multicomponent data. After introducing a unified multicomponent 3-D Green's function representation, we analyse its 1-D version for elastodynamic waves in more detail. It follows that the main additional requirement is that the multicomponent direct arrival, needed to initiate the iterative solution of the Marchenko equation, includes the forward-scattered field. Under this and other conditions, the multicomponent Green's function can be retrieved from single-sided reflection data, and this is demonstrated with a 1-D numerical example.

scholarly journals Monitoring of induced distributed double-couple sources using Marchenko-based virtual receivers

Solid Earth ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 1301-1319 ◽  
Author(s):  
Joeri Brackenhoff ◽  
Jan Thorbecke ◽  
Kees Wapenaar
Keyword(s):  
Green’S Function ◽  
Green's Function ◽  
Synthetic Data ◽  
Real Data ◽  
Point Sources ◽  
Reflection Data ◽  
Double Couple ◽  
Source Mechanisms ◽  
Source Signal ◽  
Time Part

Abstract. We aim to monitor and characterize signals in the subsurface by combining these passive signals with recorded reflection data at the surface of the Earth. To achieve this, we propose a method to create virtual receivers from reflection data using the Marchenko method. By applying homogeneous Green’s function retrieval, these virtual receivers are then used to monitor the responses from subsurface sources. We consider monopole point sources with a symmetric source signal, for which the full wave field without artifacts in the subsurface can be obtained. Responses from more complex source mechanisms, such as double-couple sources, can also be used and provide results with comparable quality to the monopole responses. If the source signal is not symmetric in time, our technique based on homogeneous Green’s function retrieval provides an incomplete signal, with additional artifacts. The duration of these artifacts is limited and they are only present when the source of the signal is located above the virtual receiver. For sources along a fault rupture, this limitation is also present and more severe due to the source activating over a longer period of time. Part of the correct signal is still retrieved, as is the source location of the signal. These artifacts do not occur in another method that creates virtual sources as well as receivers from reflection data at the surface. This second method can be used to forecast responses to possible future induced seismicity sources (monopoles, double-couple sources and fault ruptures). This method is applied to field data, and similar results to the ones on synthetic data are achieved, which shows the potential for application on real data signals.

scholarly journals Wiener estimation of the Green’s function

Geophysics ◽  
2013 ◽  
Vol 78 (5) ◽  
pp. W31-W44 ◽  
Author(s):  
Anton Ziolkowski
Keyword(s):  
Green’S Function ◽  
Impulse Response ◽  
Green's Function ◽  
Seismic Data ◽  
Wiener Filter ◽  
Electromagnetic Data ◽  
Transient Electromagnetic ◽  
Marine Seismic ◽  
Measured Response

I consider the problem of finding the impulse response, or Green’s function, from a measured response including noise, given an estimate of the source time function. This process is usually known as signature deconvolution. Classical signature deconvolution provides no measure of the quality of the result and does not separate signal from noise. Recovery of the earth impulse response is here formulated as the calculation of a Wiener filter in which the estimated source signature is the input and the measured response is the desired output. Convolution of this filter with the estimated source signature is the part of the measured response that is correlated with the estimated signature. Subtraction of the correlated part from the measured response yields the estimated noise, or the uncorrelated part. The fraction of energy not contained in this uncorrelated component is defined as the quality of the filter. If the estimated source signature contains errors, the estimated earth impulse response is incomplete, and the estimated noise contains signal, recognizable as trace-to-trace correlation. The method can be applied to many types of geophysical data, including earthquake seismic data, exploration seismic data, and controlled source electromagnetic data; it is illustrated here with examples of marine seismic and marine transient electromagnetic data.

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