Load and process multiple traces

[mcardenass]

Dear Tim

I loaded 72 traces in sac format in S. The next step is to build a structure using R = RawData (S,cc_len, cc_step). I only see one trace in this step with the number of windows given by cc_len and cc_step. The question is whether the RawData function can operate on all channels found in S to convert all SeisData to RawData. Also, suppose we use the correlate function. In that case, ¿it is necessary to perform the individual correlation between pairs of stations (through a loop), or can you achieve the correlation between all pairs of stations and store it? Finally, ¿is possible to export Crosscorrelation results in SAC format?

Thanks in advance

Martín

[tclements]

Hello Martín!

Great to see you're using SeisNoise.jl. RawData(S) will only convert the first channel. RawData structures are intended to be used for a single station component each. To convert the other channels in S to RawData you could do something like this:

Rarray = Array{RawData}(undef,S.n) # preallocate array of RawData
for ii in 1:S.n
    Rarray[ii] = RawData(S[ii],cc_len,cc_step)
end

You might also want to check out SeisIO.Nodal, which might be a better fit for the type of data you have https://seisio.readthedocs.io/en/latest/src/Submodules/nodal.html. I've started adding functionality for correlating Nodal/array data but it's still a work in progress: https://github.com/tclements/SeisNoise.jl/blob/466a9bf900753c430ed789ac6a76b687b0e89be4/src/nodalcorrelation.jl.

For saving data in SAC format, @xtyangpsp has a package called SeisConvert.jl for converting CorrData to SAC and other formats. Xiaotao and I will have to discuss if we should move SeisConvert.jl into a SeisNoise.jl or keep it as a standalone package.

[mcardenass]

Hello Tim

Thanks for your quick response, I will continue to try SeisNoise, and I will recommend them to my students. I have a mixture of programs to process data, with them I have obtained results that find in https://www.researchgate.net/profile/Martin-Cardenas-Soto.

Some of your publications have helped us to illustrate to students the potential of noise methods, for example, the article Tracking Groundwater Level ...

It is a pleasure to greet you, we keep in touch

Martin

On Fri, Aug 13, 2021 at 12:59 PM Tim Clements @.***> wrote:

Hello Martín!

Great to see you're using SeisNoise.jl. RawData(S) will only convert the first channel. RawData structures are intended to be used for a single station component each. To convert the other channels in S to RawData you could do something like this:

Rarray = Array{RawData}(undef,S.n) # preallocate array of RawData for ii in 1:S.n

Rarray[ii] = RawData(S[ii],cc_len,cc_step)

end

You might also want to check out SeisIO.Nodal, which might be a better fit for the type of data you have https://seisio.readthedocs.io/en/latest/src/Submodules/nodal.html. I've started adding functionality for correlating Nodal/array data but it's still a work in progress: https://github.com/tclements/SeisNoise.jl/blob/466a9bf900753c430ed789ac6a76b687b0e89be4/src/nodalcorrelation.jl .

For saving data in SAC format, @xtyangpsp https://github.com/xtyangpsp has a package called SeisConvert.jl https://github.com/xtyangpsp/SeisConvert.jl for converting CorrData to SAC and other formats. Xiaotao and I will have to discuss if we should move SeisConvert.jl into a SeisNoise.jl or keep it as a standalone package.

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[xtyangpsp]

Thanks for looping me in, Tim. Let's talk about incorporating some converting functions in SesiConvert.jl to SeisNoise.