This is demonstrat ed for two seismic wave pairs used to probe deep mantle and coremantle boundary structure. This book is very comprehensive, and introduces various topics in seismic data analysis in depth. Reflections on the deconvolution of land seismic data. Convolution gives the overlapp of one function with another function reversed in time. The use of l1 penalty in similar problems promotes sparsity of the solution rm 1 see chen et al. Robinson and sven treitcl geophysical references series no. The book is a must have for anybody working in geophysics. Where the recorded data can be modeled as a pure signal that is distorted by a.
We herein apply a seismic interferometric method to extract the shear waves propagating in the building, identical to the. Convolution can be applied to any two functions of time or space or other variables to yield a third function, the output of the convolution. Deconvolution is a filtering process which removes a wavelet from the recorded seismic trace by reversing the process of convolution. Deconvolution techniques used in conventional processing are based on optimum wiener filtering. This book discusses the latest advances in singular spectrumbased algorithms for seismic data processing, providing an update on recent developments. This short book is for students, professors and professionals interested in signal processing of seismic data using matlab.
Cambridge university press, 496 pages, isbn 9780521199100. The present state of deconvolution of land seismic data. Acquisition and processing of marine seismic data 1st edition. Additionally, surface conditions have a significant impact on the quality of data collected in the field. The abcs of seismic exploration and processing enders a. Deconvolution is the reverse process of convolution.
They include spiking deconvolution and predictive deconvolution pioneered by the author and his colleague, sven treitel. The commonest way to perform deconvolution is to design a wiener filter to transform one wavelet into another wavelet in a leastsquares sense. Similar to the conventional kalman filter model for seismic deconvolution, our new kalman model is also based on the common viewpoint that a reflected seismic record can be regarded as a. Also note that the reflections are more regular and continuous across the seismic section after wavelet processing so the deconvolution appears to have produced the desired similarity in the. Through various synthetic and field examples, the book highlights the benefit of combining different sets of data. Convolution two important cases of interest digital signal filtering earths response is also a filter. A step in seismic signal processing to recover high frequencies, attenuate multiples, equalize amplitudes, produce a zerophase wavelet or for other purposes that generally affect the. Pdf processing of seismic reflection data using matlab. Several different deconvolution techniques have been developed for different purposes. The deconvolution of dual sensor data is becoming more popular. One of the most important stages in seismic data processing is deconvolution process.
As for convolution, the reflection coefficient rt is convolved with seismic wavelet wt to obtain the seismic trace st. Pdf seismic data deconvolution using kalman filter based. Seismic data processing strategies and results are strongly affected by field acquisition parameters. The discrete kalman filtering approach for seismic signals. The common practice in seismic deconvolution is to assume that the reflection sequence is uncorrelated. Onland acquisition tests using geophone and hydrophone sensors buried in the near surface at shallow depths where also utilized for permanent seismic monitoring by bakulin et al. An investigation of the tools of seismic data processing. The practical significance of fourier deconvolution in signal processing is that it can be used as a computational way to reverse the result of a convolution occurring in the physical domain, for.
Note the continuous reflections between 2 and 3 s outside the limestonecovered zone. Modern introduction to seismic data processing demonstrating exploration and global geophysics applications through real data and tutorial. Doherty society of exploration geophysicists, 2001. Note that in this case, the impulse response is unknown and is of primary interest hence. Processing of seismic reflection data using matlab book. The quality of modern seismic data owes a great deal to the. Seismic signals are a convolution of reflectivity and seismic wavelet.
Seismic deconvolution using sparse spike inversion vs. Time seriesdata processing and analysis math 587geop. Since the introduction of digital recording, a routine sequence in seismic data processing has evolved. Deconvolution definition of deconvolution by merriamwebster. During hydrocarbon exploration, seismic receivers measure a noisy version of the earths. This is not always the casesometimes it actually comes out right, but phasetying seismic to synthetics, and seismic to seismic, is an unfortunate part of our everyday geophysical lives that. The basic models in seismic processing assume that reflection data only consist of primaries hill, dragoset, and weglein, 1999. Compressive sensing in seismic signal processing is a construction of the unknown reflectivity sequence from the incoherent measurements of the seismic records. Deconvolution is an important, wellstudied problem that is commonly encountered in seismology 14. Modern singular spectralbased denoising and filtering. Deconvolution is basically the opposite of convolution.
A step in seismic signal processing to recover high frequencies. Machine learning applications to geophysical data analysis. We are always looking for ways to improve customer experience on. Although deconvolution usually is applied to prestack data trace by trace, it is not uncommon to design a single deconvolution operator and apply it to all the traces on a shot record. An investigation of the tools of seismic data processing monika wadhawan, priyanka midha, inderjeet kaur and savita department of geophysics kurukshetra university kurukshetra, india. Highfrequency enhancement of seismic data by reconvolution kevin gerlitz this method comes from a recent paper at the petex 2004 conference called cosmetic enhancement of seismic. Part of the seismic section shown in figure i4 between midpoints a and b is over an area covered with karstic limestone. Deconvolution has produced a section with a much higher temporal resolution. This article is based on crains seismic petrophysics by e. Convolution and filtering university of saskatchewan. This basic sequence now is described to gain an overall understanding of each step. Processing of seismic reflection data using matlab. Introduction a software developed by wolfgang stefan in cooperation with rosie renaut and ed garnero to deblur seismograms using total variation. Many of the deconvolution methods are well established and used commonly in seismic data processing.
Book description this short book is for students, professors and professionals interested in signal processing of seismic data using matlab. Time seriesdata processing and analysis math 587geop 505 brian borchers and rick aster november 8, 20 notes on deconvolution we have seen how to perform convolution of. A seismic blind deconvolution algorithm based on bayesian. Processing of seismic reflection data using matlab guide. Deconvolution definition is simplification of a complex signal as instrumental data usually by removal of instrument noise. We would like to ask you for a moment of your time to fill in a. The geophysical corner is a regular column in the explorer and is produced by the aapg geophysical committee. New publications are now available from the seg book mart. Hence, it increases resolution and yields a more interpretable seismic section. Careful surgical muting in fk space which severely attenuates coherent noise and a little of the random noise is now. In mathematics, deconvolution is an algorithmbased process used to enhance signals from recorded data. The abcs of seismic exploration and processing, by enders a. Fajar abdurrofi nawawi 12309054predictive deconvolution in seismic data processing 2.
Marine processing part 6 predictive deconvolution this sequence of blog posts will build up into a complete description of a 2d marine processing sequence and how it is. Seismic data processing geos 469569 spring 2006 geos 469569 is a mix of digital filtering theory and practical applications of digital techniques to assemble and enhance images of. So far, multiples are considered as noise in seismic data. The present state of deconvolution of land seismic data peter cary, sensor geophysical ltd.
The sedimentary layers of the earth are a complex amorphous material formed from chaotic, turbulent, and random natural processes. Robinson and sven treitel, covers the basic ideas and methods used in seismic processing. It relates the different models of seismic propagation, reflection, and imaging providing links to reflectivitybased imaging and to nonlinear seismic inversion. As others say, this is really a bible of seismic processing.
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