Gérard C. Herman

Imaging and suppressing near-receiver scattered surface waves

When traveling through a complex overburden, upcoming seismic body waves can be disturbed by scattering from local heterogeneities. Currently, surface-consistent static and amplitude corrections correct for rapid variations in arrival times and amplitudes of a reflector, but these methods impose strong assumptions on the near-surface model. Observations on synthetic and laboratory experiments of near-surface scattering with densely sampled data suggest that removing noise from near-receiver scattering requires multichannel approaches rather than single-channel, near-surface corrections. In this paper we develop a wavefield-based imaging method to suppress surface waves scattered directly beneath the receivers. Using an integral-equation formulation, we account for near-surface heterogeneities by a surface impedance function. This impedance function is used to model scattered surface waves, excited by upcoming wavefronts. The final step in our algorithm is to subtract the scattered surface waves. We succes...

Removal of scattered guided waves from seismic data

Near-surface scattered waves form a major source of coherent noise in seismic land data. Most current methods for removing these waves do not attenuate them adequately if they come from other than the inline direction. We present a wave-theory-based method for removing (scattered) guided waves by a prediction-and-removal algorithm. We assume that the near surface consists of a laterally varying medium, in which heterogeneities are embedded that act as scatterers. We first estimate the dispersive and laterally varying phase slowness field by applying a phase-based tomography algorithm on the direct groundroll wave. Subsequently, the near-surface heterogeneities are imaged using a least-squares criterium. Finally, the scattered guided waves are modeled and subtracted adaptively from the seismic data. We have applied this method to seismic land data and found that near-surface scattering effects are attenuated.

Inverse scattering of surface waves : a new look at surface consistency

A method is presented for eliminating near‐surface scattered noise from seismic data. Starting from an appropriately chosen background model, a surface‐consistent scattering model is determined using linearized elastodynamic inverse scattering theory. This scattering model does not necessarily equal the actual scatterer distribution, but it enables one to calculate, approximately, the near‐surface scattered part of the data. The method honors at least some of the complexity of the near‐surface scattering process and can be applied in cases where traditional methods, like wavenumber‐frequency filtering techniques and methods for static corrections, are ineffective. From a number of tests on synthetic data, we conclude that the method is rather robust; its main sensitivity is because of errors in the determination of the background Rayleigh‐wave velocity.

An elastodynamic inverse scattering method for removing scattered surface waves from field data

In an earlier paper, we introduced a 3-D inverse scattering method for removing scattered surface waves from seismic data that was based on a tomographic imaging of the scattered surface waves by a data-fitting procedure that used as much of the seismic data as possible. After this imaging step, the scattered surface waves can be computed and removed for each separate source-receiver pair. We now apply the method to two field-data sets. The method requires a knowledge of the source waveform and shallow propagation characteristics, and these input requirements are estimated from the direct surface wave. We conclude that the method effectively attenuates crossline scattered surface waves without affecting deeper reflections.

Predictive removal of scattered noise

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.

Imaging scattered seismic surface waves

Surface-wave analysis is a key tool for seismologists, ranging from near-surface characterization in geotechnical applications to global seismology. Even in exploration seismology, where surface waves are regarded as a kind of noise, the fact that they typically represent the bulk of the recorded energy makes an understanding of surface-wave propagation important. On the other hand, the heterogeneity of the near surface can make such analyses difficult since the heterogeneity is responsible for scattering and mode conversion. Here, we show how multichannel seismic records of scattered surface waves can be used to obtain spatial images of the heterogeneity. We discuss both data processing and imaging and illustrate our method on laboratory-scale data. Further, synthetic examples show that we can locate individual scatterers accurately, even when many scatterers produce interfering surface waves. Our laboratory results show that the method has the potential to locate near-surface heterogeneities in the field.

Imaging shallow objects and heterogeneities with scattered guided waves

Current surface seismic reflection techniques based on the common‐midpoint (CMP) reflection stacking method cannot be readily used to image small objects in the first few meters of a weathered layer. We discuss a seismic imaging method to detect such objects; it uses the first‐arrival (guided) wave, scattered by shallow heterogeneities and converted into scattered Rayleigh waves. These guided waves and Rayleigh waves are dominant in the shallow weathered layer and therefore might be suitable for shallow object imaging. We applied this method to a field data set and found that we could certainly image meter‐size objects up to about 3 m off to the side of a survey line consisting of vertical geophones. There are indications that cross‐line horizontal geophone data could be used to identify shallow objects up to 10 m offline in the same region.

Analysis and removal of multiply scattered tube waves

Because of irregularities in or near the borehole, vertical seismic profiling (VSP) or crosswell data can be contaminated with scattered tube waves. These can have a large amplitude and can interfere with weaker upcoming reflections, destroying their continuity. This type of organized noise cannot always be removed with filtering methods currently in use. We propose a method based on modeling the scattered tube‐wave field and then subtracting it from the total data set. We assume that the scattering occurs close to the borehole axis and therefore use a 1-D impedance function to characterize borehole irregularities. Estimation of this impedance function is one of the first steps. Our method also accounts for multiply scattered tube waves. We apply the method to an actual VSP data set and conclude that the continuity of reflected, upcoming events improves significantly in a washout zone.

Fast iterative solution of sparsely sampled seismic inverse problems

One of the problems in linearized seismic inverse scattering, which has received little attention so far, is the existence of large gaps in the acquisition geometry due to the use of a limited number of sources and receivers. Frequently used Born inversion methods do not take this kind of sampling effect into account. Therefore, especially for three-dimensional problems, the results may suffer from serious artefacts. These problems are partially overcome by using iterative methods, based on the minimization of an error norm. For two-dimensional test problems, the authors have found that iterative methods give significantly more accurate results for sparsely sampled data. For large-scale seismic inverse problems, the rate of convergence of any iterative method is extremely important. The authors have found that fast convergence rates can be achieved with the aid of methods that are preconditioned with the Born inverse scattering operator. In particular, the rate of convergence of the preconditioned successive overrelaxation method and the preconditioned Krylov subspace method have been found to be much faster than the widely used conjugate gradient method. With these new methods, the authors have obtained acceptable results for problems containing as many as 90000 unknowns, after only four iterations.

Removal of scattered surface waves using multicomponent seismic data

In many exploration areas, the shallow subsurface is strongly heterogeneous. The heterogeneities can give rise to scattering of surface waves. These scattered waves can depreciate the quality of land seismic data when they mask the body-wave reflections from the deeper part of the subsurface. Surface waves scattered near a line of receivers (inline-scattered waves) can be removed by well-known filtering techniques (see e.g., Yilmaz, 1987, section 1.6.2). However, surface waves scattered far from the receiver line (crossline-scattered waves) are left intact partially by filtering because these waves can resemble body-wave reflections. In previous papers, we have discussed an inverse scattering method for removing scattered surface waves from simulated data (Blonk and Herman, 1994), as well as from field data (Blonk et al., 1995). So far, we have limited our attention to the vertical components of the particle velocity which implies that surface waves and body-wave reflections can be distinguished on the basis of their respective differences in phase velocity.