Traditional Seismic Processing Research Articles

Study on reflection spherical wave at the near surface

In real seismic surveys, seismic wave propagates in the form of spherical wave and plane wave is the approximations of spherical wave in the far-field and for high frequencies. The traditional seismic processing and interpretation methods based on the plane wave theory are not exact. Furthermore the difference between the plane wave and spherical wave is strong, especially in the near surface. Studying the characteristics of reflection spherical wave near the surface can develop more accurate wave theory. In this paper, a two-layer model with a high-velocity upper layer is built to analyze the spherical reflection wave characteristics. Tests show when the incident angle reaches a certain level, there will be an irregular wave, the pseudo-spherical wave, following the main reflection spherical wave, which has a great influence on the main reflection wave. This discovery has enriched our understanding of wave propagation theory and it has important guiding significance for accurate study on wave phenomena.

The WARR Machine: System Design, Implementation and Data

In this paper, we describe a ground penetrating radar (GPR) system called the wide angle reflection and refraction (WARR) machine, outline the design and discuss the implementation challenges. WARR and the closely related common-mid-point (CMP) GPR soundings have been standard survey methods to measure velocity since GPR first existed. Earliest efforts demonstrated the variation in ice sheet velocity versus depth. Although GPR multi-offset soundings are valuable survey methods, they have seen little adoption since many systems are not bistatic. In addition, surveys most often use a single transmitter with a single receiver deployed sequentially at varying antenna separations, making data acquisition slow. Modern instrumentation with recent advances in GPR timing and control technology has enabled deployment of systems with multiple concurrent sampling receivers. This development has resulted in the ability to continuously acquire multi-offset WARR data at the same rate as two dimensional (2D) common offset reflection surveys in the past. The concomitant issues of survey design plus organizing the WARR data storage, documentation and analysis present numerous challenges. The extraction of velocity information from the large volumes of GPR WARR/CMP data demands automated analysis techniques. We have explored the use of normal move out (NMO) stacking at creating enhanced zero offset section from multi-offset data. Furthermore, we investigated the use of semblance analysis at estimating move-out velocities in order to apply in the NMO stack. These traditional seismic processing steps have proven to be less effective with GPR. These conclusions point to the differences in data character between seismic and GPR. Results of in-field deployment are used to illustrate advances to date and point the way to further advancements.

Integrated VTI model building with seismic data, geologic information, and rock-physics modeling — Part 2: Field data test

Velocity model building is the first step of seismic inversion and the foundation of the subsequent processing and interpretation workflow. Velocity model building from surface seismic data only becomes severely underdetermined and nonunique when more than one parameter is needed to characterize the velocity anisotropy. The traditional seismic processing workflow sequentially performs seismic velocity model building, structural imaging/interpretation, and lithologic inversion, modifying the subsurface model in each step without verifications against the previously used data. We have developed an integrated model building scheme that uses all available information: seismic data, geologic structural information, well logs, and rock-physics knowledge. We have evaluated the accuracy of the anisotropic model in the image space, in which structural information is estimated. The lithologic inversion results from well logs and the dynamic seismic information (amplitude versus angle) are also fed back to the kinematic seismic inversion via a cross-parameter covariance matrix, which is a multivariate Gaussian approximation to the numerical distribution modeled from stochastic rock-physics modeling. The procedure of building the rock-physics prior information and the improvements using these extra constraints were tested on a Gulf of Mexico data set. The inverted vertical transverse isotropic model not only better focused the seismic image, but it also satisfied the geologic and rock-physics principles.

First application of Green’s theorem-derived source and receiver deghosting on deep-water Gulf of Mexico synthetic (SEAM) and field data

Deghosting benefits traditional seismic processing and is a prerequisite to all inverse-scattering-series based processing. The freedom of choosing a convenient reference medium (and associated Green’s function) means Green’s theorem offers a flexible framework for deriving useful algorithms including deghosting. Among advantages over traditional deghosting methods are: (1) no need for Fourier transforms over receivers and sources, and (2) can accommodate a horizontal or non-horizontal measurement surface, the latter of particular interest for ocean bottom and onshore applications. The theory of Green’s theorem-derived deghosting is presented, and its first application on deep-water Gulf of Mexico synthetic (SEAM) and field data is reported. The source and receiver deghosting algorithms work with positive and encouraging results.

Very low frequency marine seismic noise

Some ocean bottom seismic records from Western Australia show very low frequency noise amplitudes that are larger than peak-to-peak amplitudes of seismic reflections on hydrophone records. The noise period is longer than a 5000 ms record length. As a result, it is hard to suppress this noise using traditional seismic processing filters without generating large truncation edge effects. Spectra from 100 trace windows were computed using a Burg multiple segment algorithm. Low frequency peaks near 0.12 Hz dominate the spectra from either hydrophone or vertical accelerometer sensors with or without air gun array shots. The low frequency peaks correspond with wavelength peaks near 500 m. Therefore, the apparent speed of propagation of this low frequency noise is about 60 m/s. This is close to the speed of gravity water waves for the OBC sensor depths. Since this marine seismic noise is at a very low frequency and not generated by the seismic source, a best practice would be additional, analogue, low cut, filtering in the field to suppress its effects on seismic records and processing. Alternatively, digital recursive filters can be designed to be applied with initial condition constraints derived for each seismic trace. The very low frequency noise can then be suppressed in the computer without truncation edge effects.

EMERALD: A Web Application for Seismic Event Data Processing

Seismologists studying earthquake sources and the structure of the Earth’s interior commonly use event‐based seismic data processing, with typical workflow including the acquisition, preprocessing, and analysis of data associated with one or more discrete seismic events. Until recently, most seismic event datasets were relatively small, including at most a few thousands or tens of thousands of seismic waveforms used in a given study (e.g., Dziewonski and Woodhouse, 1987; Grand, 1987, 1994; Woodward and Masters, 1991; Engdahl et al. , 1998). More recently, however, the size of datasets has increased significantly, not only from larger‐scale investigator‐driven temporary field experiments such as the Kaapvaal Project (Carlson et al. , 1996), La RISTRA (Wilson et al. , 2005), the High Lava Plains Project (Carlson et al. , 2005), NorthEast China Extended SeiSmic Array (NECESSArray; Grand et al. , 2006), the Carpathians Basin Project (Hetenyi et al. , 2009), and Project INDEPTH (e.g., Langin et al. , 2003; Karplus et al. , 2011) but also from large regional networks (i.e., from several dozens to 100+ broadband stations) such as the Advanced National Seismic System (earthquake.usgs.gov/monitoring/anss), Pacific Northwest Seismic Network (www.pnsn.org), Hi‐net (http://www.hinet.bosai.go.jp), ORFEUS (http://www.orfeus-eu.org), and AfricaArray (www.africaarray.psu.edu). Furthermore, new projects such as the EarthScope’s USArray (www.usarray.org) and SinoProbe (www.sinoprobe.org) are generating unprecedented volumes of data from hundreds to thousands of broadband stations. The advent of datasets potentially containing millions of seismic waveforms has exposed limitations of traditional seismic processing methods. Preprocessing and processing methods for seismic data vary widely by investigator, but they have some common attributes. They frequently consist of some combinations of standardized command‐line programs such as Seismic Analysis Code (SAC; Goldstein et al. , 2003 …

A Method for The Removal of Long Period Surface Related Multiples

Traditional seismic processing methods such as CMP stacking and predictive deconvolution often fail in the attenuation of long period multiples. Stacking often fails due to the lack of velocity differentiation between the multiple and surrounding events and because of the amplitude of the multiple reflections. Predictive deconvolution and Event Prediction (DePledge and Uren, 1992) fail because of the non-periodic nature of the multiples and to a lesser extent because of the variation of the wavelet with angle of incidence. A four step procedure to precondition and attenuate surface related long period multiples has been devised. The first two steps of this procedure solve both the periodicity problem and the variation of wavelet with angle of incidence. The third step, therefore, is the attenuation of the surface related multiples using the standard techniques mentioned above. The remaining step is to reverse the transforms applied in the first two steps. An example is given to demonstrate the performance of this method on field data. The procedure is simple, fast to apply and gives results superior to other existing techniques.

TO TACKLE THE DECONVOLUTION PROBLEM — A POWERFUL METHOD BASED ON MORE GEOLOGICAL HYPOTHESES

The new rules of the game in hydrocarbon exploration demand an exact positioning of the seismic markers in order to define the geometry of the targets more than ever before. However, the degree of success will depend to a great extent on how accurately the amplitude of reflection coefficients can be estimated.These new requirements mean that all stages of traditional seismic processing have to be critically evaluated. It can be seen, in particular, when assessing existing deconvolution methods for seismic processing, that they are often ill-conditioned to problems posed by the targets of stratigraphic exploration or by reservoir seismic prospecting. The amplitude of the reflectivity function is often estimated inaccurately.The approach described in this paper abandons the usual hypothesis (white reflectivity spectra) made by deconvolution methods and employs as alternative information the lateral redundancies which are always present on a seismic section. Our method first estimates the location of high amplitude reflectors with good lateral continuity, by means of an elegant automatic picking program. Based on these locations, a generalized inversion can be used to yield the wavelet emitted by the source, and the amplitude of the main reflection coefficients simultaneously for each trace. All the reflection coefficients are then estimated using the amplitudes and the wavelets computed previously.The various stages of this method which is called Deconvolution-Inversion, developed by Total Compagnie Française des Pétroles, are illustrated in the paper by means of both synthetic and real examples. The ability of the method to preserve the amplitudes makes it a powerful tool for stratigraphic and reservoir seismic prospecting purposes.