Coherence - Science topic

In my work applying negative dialectics to understand how we might better realise an emergent future, I see coherence as the essential link between the present, the adjacent (future) possibilities and the emergent future. Here, leadership has a key role in developing and maintaining that essential coherence. It is leadership that are the custodians of the adjacent possibilities and who must guide our emergence to best effect.

Well, that’s not really possible. The problem is that for any laser the phase wanders randomly over time compared to an absolutely perfect frequency reference. The more stable the frequency and the narrower the line width the slower the wander, but the phase will wander. For two sources to wander around together they have to be tied to the same random walk. That can mean splitting a single source, or it can mean seeding two sources with the same seed laser. It can also mean actively driving the phase of different sources to match that of some reference source. However, in all cases one way or another all outputs are tied back to one reference source.

Now, if you make two independent sources, their phase difference will stay constant for something like whatever the shorter coherence time of the two. However, while you can expect the phase difference to not drift much on time scales short compared to the coherence time, at any given moment you have no idea what that phase difference is and it will wander through all possible values on a time scale long compared to the shorter coherence time. So that lack of knowledge or control of the relative phase prevents you from doing all the things we do with coherent sources. Yes they are coherent for useful lengths of time, but with an unknown and varying phase difference.

The Aidos-x system is an excellent intelligent system for diagnosing and classifying human diseases. The methods of using the Aidos-x system for diagnosing human diseases are disclosed in lectures with sound "Using automated system-cognitive analysis for the classification of human organ tumors", "Intelligent system for diagnosing early stages of chronic kidney disease", which can be downloaded right now from the website https ://www.patreon.com/user?u=87599532 Creator's title: «Lectures on Electronic Medicine». After subscribing to this site, you will receive databases for medical research to identify the diseases that you will read about in lectures. The acquired skills of working in the Aidos-x system will allow you to apply for grants to carry out scientific research in the field of medicine.

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Thank you.

Actually, there are many tools that you can develop the code. But when we talk about the CNN, it about the long journey of learning. It cannot get on the spot. Maybe you can read some intro what is machine learning, what is deep learning.

Thank You for an opinion!

i am musician and played some instrument like (piano, guitar and flute) and i think about your great idea. if you want to find any relationship between player and listener you must use Pearson correlation analysis but before that you need to convert music elements (like tempo, interval, loudness) to one element with a algorithm then analyze that element with RR intervals of both player and listener. i think if you use Italian classic range of tempo as a score which with rise of tempo score increase (e.g. allegro, andante or something else) and for intervals first utilize major and minor scales as a reference and any passage between scales give a score based on interval changes for easy finding that you can utilize chord changes intervals.

also for data reduction i think you must converts RR intervals to means of RR in a fixed time like 10 sec then calculate music score on that 10 sec and then analyzed correlation of them with two tailed Pearson correlation analysis.

Dear Asieh Daneshi ,

The Fourier Transform of an image can be decomposed in two components, its power spectrum (the square modulus of each Fourier coefficient), and the phase (the argument of each Fourier coefficient).

Regards,

Shafagat

Building team work is a separate subject irrespective of nature of business or office. Education is no exception. In institutes normally subject experts are there. They are in departments .So at departmental level there is need for team work. To be able to work in a team, members have to share common vision or goals of organization , the supervisor or coordinator has to be friendly and cooperative and fair to all . Most often due to different cultural backgrounds and jealousy and rivalry teams cant perform. Ego is a big obstacle. Unless members of tam have clear goals communicated and their is enabling environment created by organization /boss, teams cant't function

I have the same feelings with you that coherence can be a better criterion for SSVEP decoding. Do you have any progress on the coherence bbased SSVEP decoding? Thanks.

You Welcome, Karim Bouhali

请你看看中国语言之中的修辞，回环、顶针……接龙，那些都是

I would suggest that you are probably using a poor method of fitting. Please look at the entries on the attached Google search. Good luck David Booth PS I note that R has procedures for doing this fit.

Thanks a lot Béatrice.

Dear Virendra Nath Rai

Thanks you

Arman Arefi a coherent, continous white light beam is pretty much a self-contradicting statement.

If light is coherent and white, it comes in pulses.

If light is coherent, but does not come in pulses, it is not white.

If light is white and does not come in pulses, it is not coherent.

Pick one.

Note that femtosecond lasers can often have high repetition rates - 70-80MHz, or in special cases even multi-GHz. For some applications, a source with this high repetition rate may work as a quasi-continous source. For others, of course, this may not be the case.

Throughout the entire path of the interferometer is light is a collimated plane wave. We can arbitrarily pick two basis polarizations. Let’s choose the two linear polarizations that are S and P at the mirrors of the interferometer. Any other arbitrary state can be described as a supposition of these two. Provided the sample itself is neither a retarder nor a polarizer, the two polarizations do not interfere with each other. Each polarization is effectively a completely independent MZ interferometer experiment. Either polarization produces the same interference pattern. Therefore any superposition of the two polarizations produces the same pattern.

A randomly polarized laser wanders in time over all possible linear combinations of the two polarizations. However, at any instant in time the polarization is well defined. The two paths arriving at the screen are from two slightly different moments in the lasers history depending on the difference in length of the two arms and the path length difference introduced by the sample. For the interferometer to work this path length difference must be less than the coherence length of the laser. It must also be small compared to how quickly the random polarization changes. However those two things are not independent. Since orthogonal polarizations do not interfere, the rate at which the polarization changes is part of what determines the coherence length.

So, basically, if the coherence length is long enough for the interferometer to work, then the polarization state is changing slowly enough to think of the interferometer as being in some particular polarization state at any moment. All superpositions of the eigenpolarizations produce the same interference pattern, so the interferometer works fine with randomly polarized light.

Now, if the sample is birefringent or polarizing, things get more interesting.

The incandescent light source is not considered a coherent source.

This source has a very short coherence time, and you cannot consider it a thermal light though.

another how it could HeNe laser and randomly polarized at the same time!

honestly, I cannot see how could please explain how it is a laser system and randomly polarized.

if you are going to use a thermal light the fringes will be washed-out.

You have to be carful with modelling coherent waves.. you need to begin with certain spectrum width at the start. It will ensure coherence parameters. starting with two or three coherent waves is not correct. there is always a spectrum of few contributing waves. the right way of modelling is spectrum and it’s width.

For the coherence time the laser’s frequency and phase are stable(ish). Interfering two snippets of the laser from different times, they may have different frequency and phase, but for a coherence time the frequency and phase of each is well defined and they will interfere producing a beat frequency. After a coherence time their phases or frequencies will have drifted enough that the beat frequency and phase drift. However, if the coherence time is longer than your detector bandwidth, you have already recorded the beats that happened during that coherence time. The beat frequency and phase will drift through all possible relations presented by the bandwidth of the laser, but it will all be recorded in the time record.

Many thanks, Abhinav Nag . I will try SPRKKR code

I would suggest using isce2 stack processing (https://github.com/isce-framework/isce2).

you may find the tutorials at https://github.com/isce-framework/isce2-docs.

بالتوفيق

Use the ' conv(A,B) ' in MATLAB to calculate the Cross-correlation. A and B are two different signals. Use the ' Y=fft(A,n) ' command to calculate the FFT of a signal. The A is the signal in the time-domain and the Y is the same signal in the frequency-domain. The n is the number of points we want to calculate the FFT.

Despite having the same roots of origin, namely quantum superposition, coherence and entanglement are conceptually different.

Dear Mohammad Razib Hossain

Wavelet Toolbox™ provides apps and functions for analyzing and synthesizing signals and images. You can detect events like anomalies, change points, and transients, and denoise and compress data. Wavelet and other multiscale techniques can be used to analyze data at different time and frequency resolutions and to decompose signals and images into their various components. You can use wavelet techniques to reduce dimensionality and extract discriminating features from signals and images to train machine and deep learning models

Regards,

Shafagat

Dear Raj Sunil Kandregula

Thank you very much for this interesting question. This indeed is a research question that can only be answered after thorough research. While I was searching for some answers, I came across the attached publication. I hope it provides some insights.

Thanks and regards

Gowhar Meraj

see

Qamar Ul Islam

Thank you for sharing the videos. However, I do not get my answer yet. Do you look at the papers I mentioned in the question?

The Alaska Satellite Facility (ASF) offers a free SAR processing service - Hybrid Pluggable Processing Pipeline (HyP3). HyP3 provides two types of data:

The quota is 250 tasks per month per user.

See: https://hyp3-docs.asf.alaska.edu/guides/insar_product_guide/

I agree with Pooja Singh.

Also can be called as entanglement sudden death and birth (when system is entangled) occurs when the system is linked with a channel, cavity, medium or we call it environment.

This channel has some disorders like random motion, thermal instabilities etc which complels the system to loss energy. And we call it the collapse or death. But different environments have different properties, like some show Markovian and some show non-Markovian properties. In the first case, there is a sibgle collapse, because the environment does not has the ability to return the energy or entanglement. In the latter case, the environment releases the energy, entanglement or coherence again to the system and we call it birth of the entanglement or coherence.

The repeatedly exchange between the system and environments causes repeated deaths and births of quantum phenomena, and we call it revival character.

It can be seen readily in quantum cavities or in few of classical environments with static or colored noises

check these articles:

The short answer is, all kinds of quantum correlations satisfy the conditions: quantum coherence ⊇ quantum discord ⊇ quantum entanglement ⊇ quantum steering ⊇ Bell non-locality. Therefore, coherence is considered to be the most general measure.

hi,

The electrical parameter Resistance is the measurable quantity which undergoes changes as the ferrous particles of the coherer are exposed to the impinging electromagnetic field. Even though google may not show any particular details, one can just measure the DC resistance (DC circuit with an antenna) to detect RF signals using coherer. If you use metallic parabolic reflectors and place the coherer at its focal point, the signals will be highly concentrated on it. This will to see the changes in the resistance value relatively with an ease.

Regards

Dear Julius,

Thank you for your answer and for asking me to clarify my question.

First of all, my question refers to the work of family doctors. I am collecting arguments and literature references for updating a recommendation for action by the German Society for General and Family Medicine on the initial interview. The previous version contains the recommendation that in addition to the exploration of the general physical condition, any current complaints, existing risk factors and already manifested chronic diseases as well as the diseases that have already occurred in the family, the current life situation (work, family, housing, etc.) and central aspects of the biography should also be found out. This should be done in order to exclude foreseeable dangerous courses and to develop an overall understanding of the patient, which can guide the development of the relationship and the joint treatment. On the part of the group of authors, we argue that it is worth investing in the beginning - the initial interview - whereas many practitioners argue that in the course of long-term treatment, the relevant life-world-related and biographical data can gradually emerge (" lived anamnesis") or gradually be recovered. In Germany, a maximum of about 20 minutes is available for initial interviews, so the question is when it is "enough". In my experience, information is often collected whose meaning remains unclear from the patient's point of view - does "living in family" mean having social support? Or, in the given case, does it mean quite the opposite, that there is constant tension? Therefore, we thought that "the practitioner's feeling of having understood the patient as a whole person" could be a central criterion. It is clear that this feeling depends on experience and the like, but we assume that it also becomes action-guiding when the practitioner is not aware of it.

Apart from the breadth of possible problem situations, the time frame available for the conversation and the possible long-term relationship as special features of general practice, I think the question can be transferred to other situations.

What do you mean?

All the best

Ottomar

It is not that simple. The boundary is not always incoherent. If the microstructure contains Widmannstatten ferrite, then there is a coherent, or semicoherent boundary between this ferrite and austenite.

The nature of a boundary between phases, of which one is the result of a precipitation process, depends on several factors, primarily on the difference in the free formation energy of both phases, on the grain boundary energies and on the rate of movement of the respective grain boundaries of the precipitated phase.

These physical factors, in case of steel, depend on the composition (carbon content) and the cooling rate.

For a more comprehensive reply and understanding of the physical principles, I suggest to read the book: Phase Transformations in Metals and Alloys, third edition, by D. Porter, K, Easterling and M. Sharif, CRC Press, especially page 299 onward.

Thanks Iftikhar Yasin for sharing the sample file. TI learned that some data may cause this problem. The wtc function uses another function arima which checks with both coefficients to be between -1 and 1 so that the process would be stationary. If not, it throws the error you are experiencing. This can be solved by using the argument method = "ML" in the arima function itself. The ML stands for maximum likelihood and it forces R to perform the task. For your convenience, I have modified the script and pushed it to my GitHub account. The following link provides a script that you can copy and paste into R for execution. Afterward, instead of wtc, use wtc_Modfd. You can leave the rest as it is. Please let me know if there is any difficulty. Thanks

# Use it as

wt1 = wtc_Modfd(t1, t2, nrands = nrands)

Ominous sign in Marfan and Loeys–Dietz syndromes (Eur Heart J Cardiovasc Imaging 2021 Aug 14;22(9):1035-1044)

Thanks for your help!

Some of my intramuscular EMG data was 'not good' because the fine-wire slightly moves during muscle contraction. For subjects with more fat at their back, getting a nice recording will also be challenging.

Muhammad Usman thank you so much , files have been opened

Michel's last sentence is exactly what I'm referring to in my gas-tube HeNe example. Over the time duration of a single pulse, the light is temporally coherent; however, correlated pulse to pulse (depending on the pulse repetition frequency compared to the coherence time), the light is temporally incoherent.

What do you mean by reactor?

Coherent elastic scattering of what?

Neutrons and X-Rays undergo Bragg scattering in polycrystalline metals.

As you mention the behaviour is similar to a powder, because of low interaction of the radiation with the material.

Nicolò

Dear Prof. Saini!

You pointed to an important topic. I think that now during the COVID-19 pandemic online interactive online handouts with video short movies (cases) are the best way to adapt to circumstances (remote learning). Still this problem is a case - and context dependent one:

1) Lorico DS. Lapitan Jr et al. (2021). An effective blended online teaching and learning strategy during the COVID-19 pandemic, Education for Chemical Engineers, Volume 35, (2021), Free access: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7847201/pdf/main.pdf

2) A case-study: William Flynn et al. (2021).Delivering online alternatives to the anatomy laboratory: Early experience during the COVID-19 pandemic, Clinical Anatomy, Volume 34, Issue 5, January 2021, Free access: https://onlinelibrary.wiley.com/doi/10.1002/ca.23722?af=R

3) Darius, P.S.H., Gundabattini, E. & Solomon, D.G. A Survey on the Effectiveness of Online Teaching–Learning Methods for University and College Students. J. Inst. Eng. India Ser. B (2021). https://doi.org/10.1007/s40031-021-00581-x Free access:

4) Kumar, P., Saxena, C. & Baber, H. Learner-content interaction in e-learning- the moderating role of perceived harm of COVID-19 in assessing the satisfaction of learners. Smart Learn. Environ. 8, 5 (2021). https://doi.org/10.1186/s40561-021-00149-8 Free access:

Yours sincerely, Bulcsu Szekely

You can consider with discourse can increase the student motivation and activity to write their idea.

Well, I never heard of OCT until this minute, but a fiber rotator is generally for selecting the axis of polarization entering (exiting) a polarization maintaining (PM) fiber. A couple minutes with Google and I see polarization sensitive OCT is a thing, so I think that is the idea. If the light source is polarized, the scattering tissue can change the polarization. Different tissues give different polarization effects, so being able to change what polarization you are observing provides extra tissue differentiation and contrast.

Hopefully the guidance as per the following publications could help:

Piyush Kumar For generating coherency matrix and further performing target decomposition, Single Look Complex SAR data is required which is Slant Range Data Product. So, in case of Chandrayaan-2 DFSAR data, you need to explore SLI (Single Look Image) data. I think that these basic information are available at ISSDC web portal where you might be downloading data sets.

One option could be just using windowing or proper scaling filters and performing wavelet decomposition separately if your ultimate goal is to detect the frequency elements in a particular time period. For this option, you might use any windowing algorithm with or without overlap. Another option could be using different forms of wavelet transform. For instance, wavelet package decomposition might give you more flexibility in terms of time-frequency scaling. You might also wanna look at : Maximal Overlap Discrete Wavelet Package Transform (MODWPT) and Double Density Discrete Wavelet Transform (DWT) for better decomposition options.

For more details:

https://www.mathworks.com/help/wavelet/ref/dddtree.html#:~:text=The%20double-density%20DWT%20is%20a%20three-channel%20perfect%20reconstruction,%28wavelet%29%20filters%20in%20the%20second%20and%20third%20columns.

Agree, GriMech is one of the best libs to calculate such a problem!

For a prefect pulsed laser, the repetition rate will not affect the first-order coherence. Happenly, I have also worked on this noise model of SC generation several month ago. In fact, I have tried to model several kinds of noise such as the quantum noise, technical noise, as well as the amplified spontaneous emission noise. The last one makes us publish some papers. see " Spontaneous emission noise in mode-locked lasers and frequency combs" and " Tailoring the waveguide dispersion of nonlinear fibers for supercontinuum generation with superior intrapulse coherence ". I also wonder what mathematical models you will adopted for noises?

Have a look at

this pdf>>>>>>

@Andrew McCulloch

Yes, I agree with the arguments of Hassan as they are fundamentally the bases for policy sciences.

However, I think my interest is in the advancement of the field and the recognition that the structural logic of a policy may have a big impact on the implementation outcomes. Some even suggest that in evaluation one could blame failure more to policy design (noun) than on implementation.

For me, the collaborative approach as suggested by you in concert with mapping as suggested by current research could help objectively or more rigorously analyse the content.

Additionally, for me I think another problem is the "tacit information" or policy elements that we have in policy. They are difficult to trace or rigorously evaluate, and makes policy feedback very difficult. Policy feedback and learning is important to the advancement of policy to include new information to contextualize the policy more to its environment. "Approximating it well against its environment" as suggested by John Peter.

On your starting point suggestion. Yes, that would be a good start. Having identified the data underlying the policy, and relevance (meaning)of the policy. As policy pinching often in 3rd world states tilt toward failure because of the possible mismatch between the policy and its environment. The missing link however, will be the "structural /causal logic"of the policy in Popper's tradition.

That is where my interest to the question comes from, as current scholarship hints at this but doesn't clearly provide methods to evaluate the structural logic.

Thanks,

Dear Alberto,

the interferogram measured by FTIR-based OSA provides exactly the same information as the usual spectrum. It is impossible to retrieve the pulse shape in time domain using only FTIR interferogram. Very clear and comprehensive discussion of this issue you can find in the book:

R. Trebino, "Frequency-Resolved Optical Gating: The Measurement of Ultrashort Laser Pulses" (Kluwer Academic Publishers 2000).

Particularly relevant chapter of this book you can download from:

Dear Md Masud-Ul-Alam ,

if you want to measure the interrelation between two signals , it is suggested to use WTC, which is the normalized version of the XWT. Not being normalized, XWT is not altogether a satisfactory method for coherency analysis.

Check out this paper of Maraun and Kurths (2004) on the pitfalls of XWT

For this better tool is ANP!

Dario Ambrosini Thanks for this. However, I am not trying to add speckle to an image. I am trying to simulate the speckle pattern itself (based on the coherence properties of the laser in use). For example, a laser with increased linewidth (due to optical feedback) will have decreased temporal coherence, which directly affects speckle intensity distribution and contrast. The algorithms you mentioned are probably needed (based on statistical copula), however I could not find practical advice on how to actually implement the algorithms. And typically, that is exactly what I need to proceed with simulations!

Basic text books explain mathematically, using the boundary conditions, that light is transmitted through a glass plate without any change in phase at the boundaries. But the reflection from the denser boundary surface to the less dense medium (called external reflection) suffers a Pi phase shift.This is also true for the mechancal oscillation on bound stretched string.

When the surface varies randomly, the reflected or the transmitted wavefront emerges while picking up this random random phase variation. However, the physics of "external reflection" remains the same. The "backward scateering" is "external reflection".

The size of the random surface variations compared to the wavelength determines the detailed quantittative scaterring phenomenon. They could also be isolated particles. But, the basic properties of light transmission and reflection remain the smae.

I guess Yes, because the Wavelets can be considered as a frequency separation tool...

I also find that medical issues are being answered by people who have credentials in chemistry, Physics, arts, computer sciences.

Sometimes, the people are discussing matters that are not related the the questions asked and being recommended for such answers.

Please look at the Thought Technology equipment.

Regards,

Rafal

"BFSK is done by selecting between two frequencies for each symbol (Binary Frequency Shift Keying). What the OP is showing is samples from M-PSK (Phase Shift Keying); selecting between M phases for each symbol (which would result in two distinct dots on the IQ constellation). Here the two symbols selected are phases 0° and 90°, so these could be 2 of the 4 symbols for QPSK.

To do any form of FSK, the phase must transition over the course of the symbol, given that frequency is by definition the derivative of phase. Thus if one symbol is at a fixed frequency, the phase would vary linearly with time over the duration of that symbol. On an IQ constellation, we would see the phasor rotate at a constant rate from one dot to the next (rotating counter-clockwise to represent a positive frequency and clock-wise to represent a negative frequency).

MSK (Minimum Shift Keying) is the case of the phase rotating ±90° over the symbol duration, as this would be the minimum frequency offset between symbols where the symbols would still be orthogonal. Out of band emissions can be reduced by smoothing the phase from one symbol to the next (generally abrupt transitions in phase or magnitude have high spectral content), which is the intention for GMSK (Gaussian Minimum Shift Keying) where instead of linear phase ramps going up and down according to the symbol used, the phase trajectory is filtered with a Gaussian filter such that the transitions are continuous in the first derivative, meaning the frequency transitions smoothly from one symbol to the next. " Dan Boschen Marcus Müller

Code --

%This program simulates BER of BFSK in AWGN channel%

clear all; close all; clc;

num_bit=100000; %Signal length

max_run=20; %Maximum number of iterations for a single SNR

Eb=1; %Bit energy

SNRdB=0:1:50; %Signal to Noise Ratio (in dB)

SNR=10.^(SNRdB/10);

hand=waitbar(0,'Please Wait....');

for count=1:length(SNR) %Beginning of loop for different SNR

avgError=0;

avgError_awgn=0;

No=Eb/SNR(count); %Calculate noise power from SNR

for run_time=1:max_run %Beginning of loop for different runs

waitbar((((count-1)*max_run)+run_time-1)/(length(SNRdB)*max_run));

Error=0;

Error_awgn=0;

data= round(rand(1,num_bit)); %Generate binary data source

s=data+j*(~data); %Baseband BFSK modulation

NI=sqrt(No/2)*randn(1,num_bit);

NQ=sqrt(No/2)*randn(1,num_bit);

N=NI+j*NQ; %Generate complex AWGN

h = 1/sqrt(2).* (randn(1,num_bit) + j*randn(1,num_bit)); % Rayleigh channel coefficent

Y_with_AWGN = s +N; %Receievd signal with AWGN only

Y=s.*h +N; %Received Signal with both Rayleigh and AWGN

Y= Y./h; %Channel equalization

%---------------------------------------------------------------

%---------------------------------------------------------------

for k=1:num_bit %Decision device taking hard decision and deciding error

Z(k)=real(Y(k))-imag(Y(k)); %Y(k) is actual trasmited signal signal

Z_1(k)=real(Y_with_AWGN(k))-imag(Y_with_AWGN(k));

% if real that mean we chose 1 and if imaginary we chose zero

if ((Z(k)>0 && data(k)==0)||(Z(k)<0 && data(k)==1)) % error conditions

Error_awgn=Error_awgn+1;

end

if ((Z_1(k)>0 && data(k)==0)||(Z_1(k)<0 && data(k)==1)) % error conditions

Error=Error+1;

end

end

Error=Error/num_bit; %Calculate error/bit

avgError=avgError+Error; %Calculate error/bit for different runs

Error_awgn=Error_awgn/num_bit;

avgError_awgn=avgError_awgn+Error_awgn;

end %Termination of loop for different runs

BER_sim(count)=avgError/max_run; %Calculate BER for a particular SNR

BER_sim1(count) =avgError_awgn/max_run;

end %Termination of loop for different SNR

BER_th=(1/2)*erfc(sqrt(SNR/2)); %Calculate analytical BER

close(hand);

semilogy(SNRdB,BER_th,'k'); %Plot BER

hold on

semilogy(SNRdB,BER_sim1,'r--');

hold on

semilogy(SNRdB,BER_sim,'k*');

legend('awgn','Rayleigh');

axis([min(SNRdB) max(SNRdB) 10^(-5) 1]);

grid on;

hold off

Hi Bhavana,

After referring to the attached research article it can be inferred that.

If the precipitates have a coherent strain then they will show a coffee bean contrast in the TEM. Furthermore, in diffraction mode, if the concentric rings are continuous at the interface between the precipitate and the matrix, then it indicates that the precipitates are indeed coherent. Also in this work, in the electron diffraction patterns from (OO1), strong superlattice reflections were observed, which further confirms that the precipitates of A13Sc have an ordered L12 structure. You may refer to the publication for detailed understanding.

the internet services data bases are very powerful to obtain any information in the open arena. However, some sites are secure and permission may be obtained from editors and/or those who have published papers there. The academic community is so kind that members may even download some important works and send to you so that your work can be enriched when the publishers permit so.

Dear Satyendra Panwar:

Coherence is the measures of the similarity of the seismic waveform between traces neighboring or adjacent, and it reveals structural and stratigraphic changes in your data, such as faults, channels, edges of bodies in general. You can measurement the local waveform within a defined space and time aperture or window, by cross correlation of two adjacent traces, both, inline and crossline in 3D seismic data.

It is important to say that there are alternative measures of the similarity of the wavelet or waveform in seismic, such as: cross-correlation, semblance, variance, eigenstructure, gradient structural tensors.

In semblance estimate of coherence, we define the coherence such as the ratio between the energy of average traces in a defined window by the energy of the input traces. It means that you: 1- calculate the energy of input traces, 2- calculate the average wavelet within your window of analysis, 3- estimate coherent traces by their average, and 4- calculate energy of average traces.

In eigenstructure estimate of coherence, we define the coherence such as the ratio between the energy of coherent component traces in a defined window by the energy of the input traces. It means that you: 1- calculate the energy of input traces, 2- calculate the wavelet that best fits the data within your window of analysis, 3- estimate coherent compt of traces, and 4- calculate energy of coherent compt traces.

From coherence volume, you can spatially image and “directly” map faults and fracture systems without the tedious and extremely subjective method of interpreting faults on selected vertical sections, then connecting the interpreted segments to give a complete fault picture. You can more readily understand depositional systems, as the process highlights such depositional features as channels, onlap, turbidite sequences, etc.

It is very important to say to you that the quality of coherence volume attribute results depends on selection of optimum processing parameters specifying such as: dip constraints, vertical and spatial aperture, and processing algorithms.

In addition, before that, you must understand the magnitude of the acquisition seismic footprint, in focus when you working with onshore seismic, because if you do not remove or attenuate this influence, the coherence attribute highlights these changes: often they are the same order of magnitude as your target wavelet distortions related to real geological features, faults or stratigraphic. So, you need, before running the coherence attribute, try to reduce the acquisition footprint by filtering such as spectral whitening and spectral blending / FXY decon / dip enhancement. To better understand subtle fault changes, advanced structural filtering for coherency analysis that use fault preservation and across-fault smoothing techniques depending on the data requirements.

Also, because you use windows following your interesting-horizons, you must check carefully the QC of the input horizons. Sometimes you need to smooth the noisy ones.

Another consideration for your coherence attribute calculation is the fact that there are in most of the commercial software the chance to get almost instantaneously the volume. Also, that with the same input volume, you can modify the computational window options, e.g., direction of the calculus – the best perpendicular to the structural features -, the aperture window, the shape of the aperture window and size or radius, etc.

Try to read the paper attached and this landmark´s ones:

Taner, M. T., et al. 1976. Complex seismic trace analysis. Geophysics. Vol 44 No 6 (June 1979), pp 1041-1063.

Robertson, James D., and Fisher, David A. Complex seismic trace attributes. The Leading Edge, June 1988, pp 22-26.

Marfurt, et al. 1998. 3-D seismic attributes using a semblance-based coherency algorithm. Geophysics, Vol. 63 No 4 (July-August) pp 1150-1165.

Marfurt, et al. 1999. Coherency calculations in the presence of structural dip. Geophysics. Vol 64 No 1 (January-February) pp 104-111.

Best regards Satyendra,

Mario E. Sigismondi

Have a look at https://www.mathworks.com/matlabcentral/answers/28605-measure-the-coherence-or-incoherence-of-two-one-matrix-ces

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Hi Addam, in case you found the answer to your question in the meantime, I would be very interested,as I am asking myself the same! Tanks!

thanks for information Dr. Oleg I. Berngardt

sir,

but in this are only particular region not in the layer of the ionospheric region of E layer echoes data are received in the 5 channel receiver header file data encrypted file is decrypted then generating in the waveforms of both IQ data but the antenna positions and baseline lengths are depending on the received vector of s(t) in the paper of RDplamer spherical coordinate system in our radar station is the not advanced in the ionospheric radar only for x and y-direction of the image generation cartesian coordinate system are used to find ing in the steering vector of the s(t) and direction vector of (W) and it find weight matrix of the antenna in the 5 channel data are their but resolution of the image are very low compare to our base paper in the after generating the IQ data processing the wave it generates image directly one file shows without Fast Fourier Transform, autocorrelation function, and capon algorithm image of the that layer are shown