Deconvolution of seismic data an overview of the book

Reflections on the deconvolution of land seismic data. This book is devoted to application of the reflection seismic method to the exploration and development of oil and gas fields. Processing of seismic reflection data using matlab book. We have extended the method of stationary spiking deconvolution of seismic data to the context of nonstationary signals in which the nonstationarity is due to attenuation processes. The process of applying fxdecon starts by partitioning the data into windows small enough so events of interest appear linear. Advanced digital signal processing of seismic data by wail a. Data conditioning encompasses a wide range of technologies designed to address numerous challenges in the processing sequencefrom data calibration and regularization.

The main objective of this course is to give the audience an overview of the techniques in seismic multiple removal, starting with the deconvolutionbased methods from the 1960s, via the moveout discrimination techniques of the 1980s and ending up with waveequation based methods from the 1990s and their 3d extensions as developed in the 2000s. Seismic inversion deconvolution download ebook pdf, epub. Deconvolution compresses the basic wavelet in the recorded seismogram and attenuates reverberations and shortperiod multiples. The technique ensures that traces from the same surface source and. Featured within this volume are various geophysical convolution models and a treatment of deconvolution for a timevarying signal. We will describe four techniques which have been and are being used to accomplish this objective. This book provides an advanced overview of digital signal processing dsp and its applications to exploration seismology using realworld examples. The data within each window are then fourier transformed. Deconvolution is a mathematical process that allows the adjustment of the amplitude and frequency contents of a seismic ground motion applied at the base of the foundation in order to get the desired output at the damfoundation interface. Advanced digital signal processing of seismic data by wail. In the case of deconvolution of seismic data, the original unknown signal is made of spikes hence is possible to characterize with sparsity constraints or regularizations such as l 1 norml 2 norm norm ratios, suggested by w. Book description this short book is for students, professors and professionals interested in signal processing of seismic data using matlab. In mathematics, deconvolution is an algorithmbased process used to enhance signals from recorded data. In seismic processing the purpose of deconvolution is to remove or collapse the seismic wavelet.

An interactive java application demonstrates the method and can be used to explore the impact of different parameter choices. The present state of deconvolution of land seismic data. The objective of deconvolution is to extract the reflectivity function from the seismic trace. In spiking deconvolution, it is assumed that the autocorrelation of the seismic trace is the same as the autocorrelation of the source wavelet, and that the reflectivity is random and its autocorrelation is a spike in other words, the desired output is. The other two show how surfaceconsistent deconvolution can degrade as seismic data depart from the surfaceconsistent model. So far, there has been a limited number of studies on the processing of real 3d data sets using opensource software packages. A new iterative procedure for deconvolution of seismic. Predictive deconvolution in seismic data processing in atala prospect of rivers state, nigeria egbai, j.

The goal of sparse spike deconvolution is to recover an approximation of a given noisy measurement t w. Gabor deconvolution of seismic data for source waveform. Figure i1 shows a seismic section from the gulf of mexico, nearly 40 km in. The abcs of seismic exploration and processing, by enders a. Dualsensor technology is to provide the fundaments of dualsensor technology. Basis pursuit inversion conclusion the results presented in this paper reveal several interesting aspects of the sparse channel inversion methods. The stepbystep demo of the full reflection seismic data processing workflow using a complete real seismic data set places itself as a very useful feature of the book. Seismic deconvolution used for improving the bandwidth of data is inherently nonstationary, mixed phase, and blind. Processing, inversion, and interpretation of seismic data. 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. A new iterative procedure for deconvolution of seismic ground. Deconvolution and inverse theory, volume 29 1st edition.

Robinson and sven treitel, covers the basic ideas and methods used in seismic processing, concentrating on the fundamentals of seismic imaging and deconvolution. This landmark volume was first published to provide both an overview of the field, and practical methods and results. Another goal of deconvolution is to produce a wavelet with a simple phase character, ideally a zerophase wavelet, which is the same for every trace in the seismic dataset. Hence, it increases resolution and yields a more interpretable seismic section. I illustrate the method and its limitations with three instructive field examples. Enwenode onajite, in seismic data analysis techniques in hydrocarbon exploration, 2014.

The first underlines the issues of uncertainty and reliability. This is the first study to present simultaneously both deconvolution and inversion, two powerful tools of data analysis. Click download or read online button to get maximum likelihood deconvolution book now. Deconvolution is basically the opposite of convolution. The unprocessed data got from the field operations are fed into automatic computer whose programme is written in line with the theory.

An overview of the current techniques used in the inversion of seismic data is provided. Seismic deconvolution using sparse spike inversion vs. Click download or read online button to get seismic inversion deconvolution book now. Advanced digital signal processing of seismic datanook book. The stepbystep demo of the full reflection seismic data processing. Processing of seismic reflection data using matlab guide books. Conventional processing of reflection seismic data yields an earth image represented by a seismic section which usually is displayed in time. Processing of seismic reflection data using matlab guide.

Multidimensional seismic data processing forms a major component of seismic profiling, a technique used in geophysical exploration. Where the recorded data can be modeled as a pure signal that is distorted by a filter a process known as convolution, deconvolution can be used to restore the original signal. Blind deconvolution can be performed iteratively, whereby each iteration improves the estimation of the psf and the scene, or noniteratively, where one application of the algorithm, based on exterior information, extracts the psf. For example, a convolution can be used to model the filtering of seismic energy by the various rock layers in the earth. Sparse spikes deconvolution is one of the oldest inverse problems, which is a stylized version of recovery in seismic imaging. In seismology we create an impulse seismic energysource and this signal after interacting with the. The seismic convolutional model states that a seismic record is the convolution of the earths reflectivity with the seismic wavelet. One of the most common deconvolution techniques is spiking deconvolution, which tries to remove source wavelets from the recorded data by converting them into a spike. This short book is for students, professors and professionals interested in signal processing of seismic data using matlab. Maximum likelihood deconvolution download ebook pdf. The deconvolution of seismic data as a fluctuation analysis.

The present state of deconvolution of land seismic data peter cary, sensor geophysical ltd. Pages in category deconvolution the following 33 pages are in this category, out of 33 total. Current practices for industrial pwave processing are described, discussed and exemplified using synthetic and real data examples. Jan 01, 2016 deconvolution is basically the opposite of convolution. Predictive deconvolution in seismic data processing in. This site is like a library, use search box in the widget to get ebook that you want. For each frequency, a prediction filter is calculated and applied twice. It is common practice to model a seismic trace as a convolution of the reflectivity function of the earth and an energy waveform referred to as the seismic wavelet. The deconvolution procedure here was adopted from 1 but di erent minimizing criteria and a di erent generation. Since the convolution destroys many low and high frequencies, this requires some prior information to regularize the inverse problem. Pdf processing of seismic reflection data using matlab. 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 derived. Convolution gives the overlapp of one function with another function reversed in time.

In addition, some kind of trace balancing gain applications often is applied after deconvolution to bring the data to a common rootmeansquared rms level figure 1. Early in our careers as geophysicists, most of us took at least one course on seismic signal analysis where we were taught that standard wiener deconvolution converts the minimumphase source wavelet in our seismic data to a wavelet with a phase spectrum that is zero and an amplitude spectrum that. The deconvolution of seismic data as a fluctuation. Up up, down down sourceside updown 2 and higher order. Part 6 predictive deconvolution seismic reflections. Predictive deconvolution in seismic data processing in atala. Maximum likelihood deconvolution download ebook pdf, epub. Purchase deconvolution and inverse theory, volume 29 1st edition. From pz summation to wavefield separation, mirror imaging. This book provides an advanced overview of digital signal processing dsp and its applications to exploration. Since geophysical data obtained in such techniques is a function of both space and time. Seismic data must be interpreted using digital signal processing techniques in order to create accurate representations of petroleum reservoirs and the interior structure of the earth.

The concept of deconvolution is widely used in the techniques of signal processing and image processing. In 1996, deconvolution of images and spectra was published academic press as a second edition of janssons 1984 book, deconvolution with applications in spectroscopy. Aug, 20 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 derived. The transition of these applications to standard practice is still ongoing but progressing rapidly, backed by theoretical correctness and the quality of practical results on field data. Deconvolution can also be performed on other types of data, such as gravity, magnetic or well log data. Deconvolution, or inverse filtering, can improve seismic data that were adversely affected by filtering, or convolution that occurs naturally as seismic energy is filtered by the earth. While d may be built with a longer shift, a shift of one sample is typically used the process of applying fxdecon starts by partitioning the data into windows small enough so events of interest appear linear. In this way, the deconvolved seismic record provides an estimate of the reflectivity. Deconvolution techniques used in conventional processing are based on optimum wiener filtering.

Processing of seismic reflection data using matlab morgan. The mathematical form of the convolution of two functions, a filter ft and a timeseries xt, is. In the limit, it can be used to spike the seismic wavelet and obtain an estimate for reflectivity. Reflections on the deconvolution of land seismic data cseg.

Early in our careers as geophysicists, most of us took at least one course on seismic signal analysis where we were taught that standard wiener deconvolution converts the minimumphase source wavelet in our seismic data to a wavelet with a phase spectrum. Because both low and highfrequency noise and signal are boosted, the data often need filtering with a wide bandpass filter after deconvolution. A comparison of gabor and wiener deconvolution on real data. Jan 10, 2018 we present an overview of reproducible 3d seismic data processing and imaging using the madagascar opensource software package. Blind deconvolution is used in astronomical imaging and medical imaging. Deconvolution from oz yilmazs seismic data analysis book, published in 2001. The present dover edition is a corrected reprinting of the. From the field to the final volume, seismic data goes through many processes and workflows. Due to some restricting assumptions imposed by conventional deconvolution. We present an overview of reproducible 3d seismic data processing and imaging using the madagascar opensource software package. Processing of seismic reflection data using matlab. The technique itself has various applications, including mapping ocean floors, determining the structure of sediments, mapping subsurface currents and hydrocarbon exploration. Introduction a software developed by wolfgang stefan in cooperation with rosie renaut and ed garnero to deblur seismograms using total variation regularized deconvolution. Grossman, and victor iliescu, the university of calgary summary we present a novel approach to nonstationary seismic deconvolution using the gabor transform.

Highresolution seismic data deconvolution by a0 algorithm. Inversion is defined as mapping the physical structure and properties of the subsurface of the earth using measurements made on the surface, creating a model of the earth using seismic data as input. 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 waveshape. In particular, predictive deconvolution is an integral part of seismic data processing that is aimed at compressing the seismic wavelet, thereby increasing temporal resolution. Applicationspecific seismic data conditioning and processing for confident imaging. Featured within this volume are various geophysical convolution models and. Deconvolution attempts to compress the wavelet, thereby increasing the resolution of the seismic data. Previous work together with new results are brought together into a self contained whole. Several different deconvolution techniques have been developed for different purposes. Deconvolution is a process universally applied to seismic data, but is one that is mysterious to many geoscientists.