PROCESSING OF SEISMIC REFLECTION DATA USING MATLAB PDF

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PDF | 30 minutes read | This short book is for students, professors and professionals interested in signal processing of seismic data using MATLAB. PDF | Due to the global demand for more energy while the less number of students Processing of seismic reflection data using MATLAB": A promising book. Abstract. This short book is for students, professors and professionals interested in signal processing of seismic data using MATLAB™. The step-by-step demo of .


Processing Of Seismic Reflection Data Using Matlab Pdf

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This short book is for students, professors and professionals interested in signal processing of seismic data using MATLAB™. The step-by-step demo of the f. In what case do you like reading so much? What about the type of the processing of seismic reflection data using matlab book? The needs to read? Well. Processing of Seismic Reflection Data Using MATLAB™: A Promising Book Opening the Gateway between Geophysics & Electrical Engineering Abdullatif A.

The strength of the reflected signal depends on the impedance contrast between adjacent layers. Each spike has an amplitude related to the reflection coefficient at a layer boundary and a traveltime1 equivalent to the two-way reflection time from the surface to that boundary. At the surface.

A geologist can then make an informed interpretation by understanding how the reflection method is used and seismic sections are created.

The analysis of seismic data is performed for many applications such as petroleum exploration. Part of its success lies in the fact that the raw seismic data is processed to produce seismic sections which are images of the subsurface structure.

It can be a one-way time such as for direct waves or two-way time such as for reflected waves. A seismic trace represents a combined response of a layered ground and a recording system to a seismic source wavelet.

In summary. Traces reflected from. The last factor is the most critical [1]. A typical example for a noisy seismic section is shown in Figure 1. Before processing. Processing of seismic data includes. Due to many factors.

As a consequence. This unwanted energy includes random or incoherent noise such as instrument signals and coherent noise like the ground roll noise. Convolution seismic data model. A seismic pulse is convolved with the reflection coefficient log reflectivity function to get a seismic trace. The purpose of processing such data can. Figure 1. Each seismic trace has three primary geometrical factors which determine its nature: This point is referred to as common mid-point or common depth-point CDP.

The reflection coefficient log is related to the geological section of the subsurface through the reflection coefficient of each geological boundary and the two-way travel time. Once the velocity is known. This process is known as stacking. In general.

This implies that all traces will have the same reflected pulses at the same time.

A typical example for a seismic section http: The CMP gather is important for seismic data processing because the subsurface velocity can be derived using it. The horizontal axis represents the offset of each seismic receiver recorder from the source where each records a trace with respect to the two-way travel time vertical axis. In their usual order of application. These objectives are achieved through three primary stages. A conventional seismic data processing flow after [2].

In addition to these primary stages. In the following section. This process involves a series of steps to condition the data and prepare it for further quality control and processing including: It corrects for lateral variations in the velocity and thickness of the weathering layer. It is an important step in order to proceed further with the other seismic data processing steps that will help geophysicists to better analyze and interpret the acquired data.

NMO correction and muting: Dipping reflections are moved to their true subsurface positions and diffractions are collapsed by migrating the stacked section. Note that the above steps are general and depending on the data type. Poststack processing includes time-variant band-pass filtering.

Deconvolution is performed along the time axis to increase vertical resolution by compressing the source wavelet to approximately a spike and attenuating noise and unwanted coherent energy such as multi-path signals. CMP sorting transforms the data from shot-receiver shot gather to midpoint-offset CMP gather coordinates using the field geometry information.

Filtering is used to attenuate components of the seismic signals based on some measurable property. Velocity analysis is performed on selected CMP gathers to estimate the stacking. Velocities are interpolated between the analyzed CMPs. Residual static correction is usually needed for most land data. In the remaining parts of this book.

The following are some important parameters about the data: The aim of this chapter is.

This will assist us in further analysis and processing of this real reflection seismic data in the remaining chapters of the book. To access any one of these fields. The wiggle display: These are different geometrical variables obtained from the seismic header structure H. The most commonly used displays are the following. We then can use such variables in the following code: The reader may enjoy more plots when running the m-files written for this chapter.

A final example is plotting the so-called seismic stacking chart see Figure 2. It may be a reflection. The variable area display: Various geometrical information plots: The variable density display: This may require editing by muting this trace since it will affect the subsequent processing steps.

For example. Figure 2. The stacking chart plot of the seismic data. In chapter 3. Then this shot gather is displayed using. The provided m-files illustrates more examples. Ground roll removal via bandpass filtering 3.

NMO correction and muting. Velocity analysis on several CMPs using the velocity spectrum method 6. Residual static correction using the surface-consistent method 7. The color bars in b and c refers to the amplitude dynamic range of the data. CMP sorting 5.

Various displays for seismic data shot gather number 8: Preprocessing involving only the gain application using various methods 2. Spiking deconvolution 4. Stacking 9. Comment on the shot gather amplitudes. Migration using Stolt F-K post-stack time migration 2. Examine the seismic data header information and obtain the following information: Various displays for seismic data similar to those in Figure 2.

Display shot gathers 12—15 as the case in Figure 2. What do you notice from a trace to another? Variable area display of seismic shot gathers Application of field statics: In land surveys. SEG-Y to another format that is convenient to the processing software and used throughout the processing flow e.. Gain application: Amplitude corrections are applied to account for amplitude losses due to spherical divergence and absorption.

Trace editing: During this step. This step can be delayed until the static correction process where better near-surface velocities might be available. This is usually done in the field. Steps have already been taken care of for our data set while we are going to work on step 6 later on in Chapter 7. These steps include: In the rest of this chapter. The data is transposed from the recording mode. The QC process involves a series of steps to condition the data and prepare it for further quality control and processing.

The data is converted from one seismic digital format e. Setup of field geometry: The geometry of the field is written into the data trace headers in order to associate each trace with its respective shot. Seismic data shot gather number Such a recorded trace must be muted or.

It includes. This is due to the use of a bad geophone. Another commonly used function is the exponential gain function correction: We can clearly see the amplitudes enhancements gained on this shot gather in Figure 3. The data-independent scheme corrects the amplitudes using a common scaling function to all the traces such as the gain through multiplication by a power of time using: This occurs at each geological reflector where part of the propagating seismic incident waves will be reflected.

As the seismic wave spreads out from its source. At the preprocessing stage. We can further analyze the gain increase after the corrections by plotting the average trace amplitudes envelope in dB for the both shot gathers of Figure 3.

Figure 3. There exist data independent and dependent amplitude correction schemes. There is no loss here in terms of the mechanical energy since the lost energy merely travels somewhere else.

This loss is proportional to the exponential of the distance from the source. This occurs where the seismic energy is converted into heat by friction. Seismic data shot gather number 8: Interpolate between these gate centers and multiply the result by the amplitude of samples corresponding in time. Calculate the RMS value in each gate. Divide the desired RMS scaler by the RMS value of step 1 and multiply it by the amplitude of the sample at each gate center.

Such a technique is known as automatic gain control AGC. Some of the famous AGC techniques include: This method requires segmenting each trace into fixed time gates and then: The amplitude envelope gain in dB for a the average trace and b trace 33 both of Figure 3. Divide the desired RMS scaler by the mean value of step 1 and multiply it by the amplitudes of all the samples in the gate. The answer is that these high-amplitude triangles are the result of noise that fills the whole AGC window 0.

Calculate the absolute mean value for in a given gate of length w. The amplitude gaps are due to the fact that the. Slide the gate down by one sample and repeat steps until we have calculated all the amplitudes of all trace samples that have been corrected.

AGC window at these time shifts contains both low-amplitude noise and high-amplitude seismic signal and dividing by their RMS value or absolute mean amplifies the signal and attenuates the noise.

A final word of caution is that we must be careful when applying amplitude corrections techniques since they may destroy the signals character. At this preprocessing stage, Equation 3. Display and compare your re- sults with the data before applying the required amplitude corrections.

In your opinion, which method results in the best amplitude correction? Envelope dB trace number: Similarly, the amplitude envelope gain in dB for a the average trace and b trace 33 both of Figure 3.

Mute the bad traces of shot gather 16 as in Section 3. A more proper definition of noise contaminating seismic signals can be stated by defining the type of signals we are interested in. Seismic data processing. In land seismic records.

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Spatially coherent noise is the most troublesome noise and can be highly correlated and sometimes aliased with the signal [8] and [9]. Poor seismic records usually have SNR ratios less than one. Unlike coherent noise energy.

Random noise. The authors in [8] define the signal of interest as the energy that is most coherent and desirable for geophysical interpretation of primarily reflected arrivals signals. Anything other than that is considered to be unwanted energy. This unwanted energy can be classified into two main categories [2]: One can define the signal of interest coherent energy as the energy which is coherent from trace to trace. Examples of such a type in land seismic records are [2]: There might be several modes of ground rolls in the record because of their dispersive nature i.

Figure 4. They are attenuated using source and receiver arrays in the field and various processing methods e. The reader can examine references [2] and [4] for more information about seismic data filtering and noise suppression techniques. It is an undesirable energy that is added to the primary signals.

Since our real seismic data contains mainly ground roll coherent noise. Such energy shows consistent phase from trace to trace. As discussed before in Chapter 1. Improper removal of coherent noise can affect nearly all the processing techniques and complicates interpretation of geological structures see [2].

In particular. Since our land seismic data set contains ground roll noise. The color bars in b and c indicate the magnitude values in dB. There exist different means for such useful analysis: These plots and their corresponding analysis and interpretations are very useful. A final word of caution is that although frequency filtering like BFP has improved the data SNR by attenuating the ground roll noise. This is common in seismic data processing and will be taken care of by applying deconvolution as we will explore in the next chapter.

Processing of seismic reflection data using MATLAB

Comment on both traces by considering their time and frequency domain representations. Write down your own observations. For the same shot gather. In your opinion.

Extract and display shot gather 2 using the wiggle plotting with a scale of your choice. Seismic data shot gather number 8 containing ground roll noise a before and b after BPF filtering. Design the IIR filter using the bilinear transformation method. Use both linear and dB magnitudes. Comment on your results.

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Select any trace and apply your designed filter to it and display both the original trace and its LPF version. What can you notice from your results? Display the original shot gather and the processed shot gathers. This is known as spiking deconvolution. One of the main aims of seismic deconvolution is to increase the vertical resolution of the data by compressing the source wavelet to a zero-phase spike. In the seismic convolutional model: That is. The deconvolution inverse seismic convolutional model is used to: Since our land seismic data set requires at this stage enhancement of its vertical resolution.

Processing of Seismic Reflection Data Using MATLAB

A linear system is one whose output o t is given by the convolution of its input i t with its response r t. The seismic convolutional model approximates the earth by a linear system. Seismic deconvolution.

From Equation 5. The aim of spiking deconvolution is to compress the source wavelet w t into a zero-phase spike of zero width. The source wavelet is stationary. The source generates only a primary wave. The seismic convolutional model is widely accepted because it agrees well with the observed seismic traces. If the source wavelet is known.

SEISGAMA: A Free C# Based Seismic Data Processing Software Platform

The earth is made up of horizontal layers of constant velocity. We can achieve this by convolving the seismic trace by the inverse filter. The seismic convolutional model commonly assumes the following [2].

The seismic wavelet is a minimum-phase wavelet. This is used if a seismic trace is recorded near a borehole.

This is the most common objective of deconvolution. In practice. For practical reasons. The truncated filter hN n is calculated by taking the inverse z-transform of HN z. Because of truncation. Truncation generates less error if w n is a minimum-phase wavelet and we include more terms of HN z.

The actual output y n is given by: Taking the inverse Fourier transform of the H f. The Wiener optimum filter model. H z is an infinite polynomial of z that is convergent only if w n is a minimum-phase wavelet.

The actual output y n is given as: Applying Equation 5. Substituting equation 5. This is a typical least-squares problem. In matrix format.

Equations 5. Acoustic impedance is denoted by the product of rock density and seismic wave speed within a certain medium. Seismic reflection is very popular, especially in the oil and gas industry, because this method yields high resolution subsurface images of geological structures [ 4 ]. The seismic reflection method is also useful in engineering, environmental, geohazard, and ground-water exploration, as discussed by Steeples and Miller [ 5 ].

Recently, a large number of open-source software programs have emerged that can be used to process seismic reflection data; however, each program has its own disadvantages.

For example, Seismic Un x [ 6 ] has a really good ability to process seismic reflection data, but it has no intuitive user interface and depends on a command-line interface. Templeton and Gough [ 7 ] developed a web-based seismic data processing tool based on the Seismic Un x platform. Through this web service, users can fill-out the form provided by the server, and the input data will be queued on a parallel processing scheduler application.

In this sense, the online service is a good concept, but the performance is limited to user interaction via a form-based input with limited resources on the server side. DSISoft, which was developed by Beaty et al. MATLAB is a popular as programming language because it has a library of common mathematical and engineering functions.

Unfortunately, it has a large computational cost as data size increases and is thus inefficient compared to a lower level programming language e. This software is mainly developed for interpretation purposes.

The recent popularity of Python, another high-level programming language that is free and reliable, has many features to handle numerical problems and has high quality graphics. It is increasingly being used to develop geophysical data processing software [ 12 , 13 ].

One of the advantages of Python is its ability to be installed on a server so that the application can be accessed remotely e. Schwehr [ 15 ] has developed an application called seismic-py, which provides an infrastructure for creating and managing a Python library for each seismic data format. Each spike has an amplitude value related to the reflection coefficient at a layer boundary and a traveltime equivalent to the two-way reflection time from the surface to that boundary.

Furthermore, the reflection time series represents the impulse response of the layered ground, which is basically the output for a spike input. Since the source wavelet has a finite length, individual reflections from closelyspaced boundaries may overlap in time on the resultant seismogram seismic section. Figure 1 represents a typical seismic convolution model. Figure 1: Convolution seismic data model. A seismic pulse is convolved with the reflection coefficient log reflectivity function to get a seismic trace.

The reflection coefficient log is related to the geological section of the subsurface through the reflection coefficient of each geological boundary and the two-way travel time.

Due to many factors, unwanted waves such as surface waves, corrupt the seismic records with noise unwanted energy. This unwanted energy includes random or incoherent noise such as instrument signals and coherent noise like the ground roll noise [3,4,5].

A typical example for a noisy seismic section is shown in Figure 2. As a consequence, of the above effects, seismic traces generally have a complex appearance and reflection events are often not recognized without the application of suitable processing techniques.

The purpose of processing such data can, in general, be viewed as the process of attenuating noise and then determining the input pulse and removing that to give the reflectivity function, which ultimately allows the determination of the acoustic impedances or related properties of the subsurface layers.

A typical example for a seismic section http: The horizontal axis represents the offset of each seismic receiver recorder from the source where each records a trace with respect to the two-way travel time vertical axis. Clearly, this section contains various noise types. Processing of seismic data includes, but is not limited to: Each seismic trace has three primary geometrical factors which determine its nature: The last factor is the most critical.

Before processing, this position is unknown but a good approximation can be made by assuming that this reflection point lies vertically under the position on the surface midway between the shot and the receiver for that particular trace. This point is referred to as common mid-point or common depth-point CDP. The CMP gather is important for seismic data processing because the subsurface velocity can be derived using it. In general, the reflection seismic energy is very weak and it is essential to increase the signal-to-noise ratio SNR of most data.

Once the velocity is known, the traces in CMP gathers can be corrected for NMO, which is basically a way of correcting for time differences which occur due to offset in a CMP gather, i.

This implies that all traces will have the same reflected pulses at the same time, but with different random and coherent noise. This process is known as stacking. In general, the main objectives of seismic data processing are to improve the seismic resolution and increase the SNR of the data [4,5]. These objectives are achieved through three primary stages.

In their usual order of application, they are: Deconvolution, which increases the vertical resolution. Stacking, which increases the SNR. Migration, which increases the horizontal resolution. Figure 3 shows a conventional seismic data processing flow. In the following section, we elaborate on the processing steps in Figure 3. These steps are: This process involves a series of steps to condition the data and prepare it for further quality control and processing including: Filtering is used to attenuate components of the seismic signals based on some measurable property.

It is an important step in order to proceed further with the other seismic data processing steps that will help geophysicists to better analyze and interpret the acquired data.This occurs where the seismic energy is converted into heat by friction. Let us assume that a storm barrier exists at some distance z3 from the beach and that there is a gap in the barrier. Chapter 4 explains what the main signals are that we are looking for and how to analyze the data in the spectrum domain.

Note that the line is not horizontal, i. Cited on page s 24 [19] A.

CMP sorting transforms the data from shot-receiver shot gather to midpoint-offset CMP gather coordinates using the field geometry information.