z1.0 values from vs30

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Anne Hulsey

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Apr 13, 2021, 5:55:52 PM4/13/21
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Hello,

I am using Jupyter Notebooks to calculate mean+std ground motions for a scenario earthquake. Some of the relevant GMPEs require z1.0 values. Is there a hazardlib script that calculates z1.0 based on vs30?

If I need to make my own script for calculating z1.0, I plan to use the CY14 equations that are also referenced in ASK14. The two equations are based on either California or Japan data. Which is more appropriate for New Zealand and/or is a more relevant equation available?

Thanks,
Anne

Michele Simionato

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Apr 14, 2021, 12:25:52 AM4/14/21
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The engine uses the CY14 equations too, see
https://github.com/gem/oq-engine/blob/engine-3.11/openquake/commands/prepare_site_model.py#L33

About the applicability to New Zealand I will leave the answer to the scientific staff.

           Michele

anirudh.rao

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Apr 14, 2021, 6:02:18 AM4/14/21
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Hi Anne,

The engine has the oq prepare_site_model command that can help with this. Please see https://github.com/gem/oq-engine/pull/4076 for a brief description of the command usage and the discussions around its implementation.

Best regards,
Anirudh

Anne Hulsey

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Apr 14, 2021, 2:09:45 PM4/14/21
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Thanks Anirudh (and Michele),

The #4076 discussion was very helpful, including a link to another script that had equations pulled from several GMPE papers.

Could you confirm what the resolution was of your comment on Oct 16, 2018 and Marco's comment on Oct 17, regarding individual GMPEs that include their own equations? Do the z1pt0 and z2pt5 functions in prepare_site_model mean that the individual GMPE's will always receive a value (either provided by the user or calculated by prepare_site_model), thereby overriding the unique equations?

Thanks,
Anne

Peter Pažák

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Apr 16, 2021, 2:06:01 PM4/16/21
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H, sorry to jump into this, it would maybe be possible to have some value - like z1pt0 = -1 or z2pt5 = -1 for say California and -2 for Japan - indicating you want that the GMPE to use its own
equation(s) to estimate these values. At the moment you are required to enter something to site model or site parameters in the input that may be suitable for one GMPE in the logic tree,
but possibly not very good for another one... would something like that make sense?

Peter

Dátum: streda 14. apríla 2021, čas: 20:09:45 UTC+2, odosielateľ: annem...@gmail.com

Marco Pagani

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Apr 16, 2021, 2:51:48 PM4/16/21
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Hi Peter, rather than adding these default values wouldn’t it be better to define a model ad-hoc depending on the zone investigated? Note that these values shouldn’t change depending on the GMM. Ideally they should be obtained from geological and/or geophysical information. If nothing else is available, the results provided by different empirical relations should be considered as epistemic uncertainties.

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Peter Pažák

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Apr 16, 2021, 4:03:59 PM4/16/21
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Hi Marco, yes, I agree that the best is to have real values, the authors often say the correlation of vs30 and the depths isn't very strong. I was just thinking how to maybe make life easier for the user when he does not have the information but wants to use these GMPEs with neutral basin amplification (assuming no deep basin is present). But as you say maybe choosing one set of values for one region is the best approach.

Peter



anirudh.rao

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Apr 17, 2021, 12:07:33 PM4/17/21
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Hi Anne,

The CY2014 and ASK2014 GMPEs include a scaling factor that depends on the difference between the z1 value input through a site model and the average z1 given the vs30 which is estimated using the equations referenced in that discussion. So if the z1 values in the site model are themselves based on the vs30, this would in effect set the z1-based scaling factor to zero. I'm not sure why the equation used for estimating z1 given vs30 for California in ASK2014 is not identical to the one in CY2014. Perhaps Marco or Graeme have an idea?

Best,
Anirudh


Jonathan S

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Apr 27, 2021, 1:24:38 PM4/27/21
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Hi all,

I think Dr. Bradley and his research group have published some velocity models for NZ basins (https://www.tandfonline.com/doi/full/10.1080/00288306.2019.1636830)

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