Composite mechanism?

[shicks-seismo]

Hi again David,

I'm looking at a seismic warm with near-repeating waveforms. Polarity / amplitude data are sparse, so I'd like to compute a single moment tensor / focal mechanism for all events using all available polarities.

I really like using the NLLoc take-off angle uncertainties to compute the range of focal mechanisms, but I was wondering how easy it might be to do this for multiple events? I.e. to read multiple NLL .hyp files to compute a single composite moment tensor? Do you have any thoughts on whether you think this might be feasible?
Thanks!

[djpugh]

Hi @shicks-seismo - short answer - yes, absolutely possible (it's how i dealt with velocity model uncertainty by sampling from multiple nlloc scatter files - see https://github.com/djpugh/MTfit/blob/develop/scripts/model_sampling.py

Without worrying about the location uncertainty to start with, you can create a composite "event" from across the data you have if a) you are happy that the events are co-located, and b) you are happy that the mechanisms are the same - MTfit has I think been used by someone else for this before, but I don't have any of the scripts for it...

There is also the relative amplitude approach, which will work for co-located events, but that doesn't work with the location uncertainty very well (marginalising the scale factor across the samples isn't implemented - I have done the maths in my thesis, but haven't implemented in code)

[shicks-seismo]

Thanks. I was actually ideally want to do a composite mechanism that accounts for different uncertainities for different events, but I don’t think that looks possible as it is only possible to provide a single probablity scatter file. I am wondering instead whether I can compute the MTs for each event, and then somehow “stack” the different solutions to make a single composite focal mechanism that shows all the different solutions? From: David J Pugh <[email protected]> Reply-To: djpugh/MTfit <[email protected]> Date: Wednesday, 29 August 2018 at 18:20 To: djpugh/MTfit <[email protected]> Cc: "Hicks S.P." <[email protected]>, Mention <[email protected]> Subject: Re: [djpugh/MTfit] Composite mechanism? (#49) Hi @shicks-seismo<https://github.com/shicks-seismo> - short answer - yes, absolutely possible (it's how i dealt with velocity model uncertainty by sampling from multiple nlloc scatter files - see https://github.com/djpugh/MTfit/blob/develop/scripts/model_sampling.py Without worrying about the location uncertainty to start with, you can create a composite "event" from across the data you have if a) you are happy that the events are co-located, and b) you are happy that the mechanisms are the same - MTfit has I think been used by someone else for this before, but I don't have any of the scripts for it... There is also the relative amplitude approach, which will work for co-located events, but that doesn't work with the location uncertainty very well (marginalising the scale factor across the samples isn't implemented - I have done the maths in my thesis, but haven't implemented in code) — You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub<#49 (comment)>, or mute the thread<https://github.com/notifications/unsubscribe-auth/AYmd8fPl9Uk1Ut9iseTDbGwVnTsrF5Vpks5uVs1SgaJpZM4WR744>.