UCVM Vs30

UCVM Vs30 Method

Vs30 at recording stations

• For interpretation of recorded data, use in order of preference
  • values listed as "Vs30 (m/s) selected for analysis" in the NGA-West2 database flatfile
  • if stations are not included in the NGA-West2 database, we will use the values from Will et al. 2015 as retrieved from UCVM (with interpolation)
• For interpretation of simulation data
  • We retrieved the Vs30 values using UCVM v19.4 for CVM-S4.26.M01 (cvmsi), for CVM-S4.26 (to show impact of adding .M01 GTL), for CVM-S4 (to check if Vs30 matches .M01 exactly), and from the Wills 2015 Vs30 model embedded in UCVM.
• For the Vs30_query against the models uses a slowness algorithm, and a 1 meter spacing.
• The Wills 2015 Vs30 values are based a processing sequence that includes converting a GIS shape file into a rasterized Vs30 grid of values produced by Kevin Milner. Kevin provided a file raster_0.00025.flt, which is rasterized with 0.00025 degree spacing (~25 meters). This file is then used to generate an etree which is used to stored the rasterized data. When query points are given between grid points, then ucvm implements interplolation of Vs30 values between associated grid points. More
• More details on the Willis Map integration here: Wills Map.
• Descriptions of UCVM Vs30 Slowness algorithm here: UCVM_Vs30.
• Description of CyberShake Vs30 Slowness algorithm here: CyberShake_Code_Base#Stochastic codes

UCVM Vs30 Values

Lat	Lon	Vs30 - Wills 2015 (UCVM v19.4)	Vs30 - Slowness Method (1m spacing) (CVM-S4.26.M01 - cvmsi)	Vs30 - Slowness Method (CVM-S4.26 - cvms5)	Vs30 - Slowness Method (CVM-S4 - cvms)	Vs30 - Slowness Method (CVM-H - cvmh)
-117.9568	33.8401	293.500	284.461	838.630	284.461	120.690
-117.8179	33.8533	351.900	329.762	850.496	329.762	394.852
-117.8869	33.8891	312.445	344.043	861.493	344.043	345.057
-117.9590	33.8663	228.200	287.845	839.552	287.845	120.690
-117.9311	33.9085	386.600	349.812	901.162	349.812	551.916
-117.9558	33.9315	386.600	349.317	889.947	349.317	705.182
-117.8033	33.9273	385.100	367.423	907.358	367.423	360.902
-117.8571	33.8530	228.200	329.762	847.254	329.762	281.789
-118.0469	33.8892	228.200	287.845	838.561	287.845	285.982
-118.0575	33.9282	386.600	354.637	855.127	354.637	224.184
-117.8656	34.0210	419.818	370.670	955.197	370.670	426.076
-117.9812	33.8077	228.200	285.871	839.554	285.871	237.354
-117.9224	33.8717	293.500	362.574	845.207	362.574	262.028
-117.9237	33.9454	385.411	331.459	932.222	331.459	726.045
-117.9508	34.0095	293.500	309.646	937.482	309.646	1062.044

We are reviewing the algorithms used to calculate Vs30 in UCVM.

UCVM CS173 Model-based Vs30

Vs30 based on tiled models queried at 1m spacing 0-29m depth using slowness calculation

UCVM Etree Vs30 Map

Vs30 from UCVM etree map, based on Willis 2006 map and Wald Topography (outside California)

Vs30 Test Points shown On Map

Vs30 from UCVM etree map, based on Willis 2006 map and Wald Topography (outside California)

Introduction

There may be an issue with how UCVM calculates Vs30 values. Presently UCVM uses the following formula:

1. sum(Vs sampled from [0,30] at 1 meters, for 31 total values) / 31

Rob has suggested that the correct formula should be:

1. 30 / sum(1 / (Vs sampled from [0, 30) at 1 meters, for 30 total values))

This can have a significant impact for Vs30 calculations. For the BBP1D model, UCVM calculates the Vs30 as 932m/s, whereas Rob's formula calculates it as 862m/s.

Another approach was used in CyberShake Study 15.12, in conversations with Rob:

1. Vs30 = 30 / Sum (1/(Vs sampled from [0.5,29.5] in 1 meter increments, for 30 total values)).

Points for comparison

We have selected 6 points to compare Vs30 as generated using the CyberShake approach (using half-meters) to other UCVM approaches.

• Input points for ucvm_query:

 34.1484  -118.1712
 34.5570 -118.1250
 34.0192 -118.2860
 34.6402 -118.9192
 36.1397 -120.3602    
 34.2667 -119.2285    

lat	lon	Velocity model	CyberShake-generated Vs30	UCVM 18.5 Gen Vs30	UCVM 18.5 (Wills 2015)	UCVM Vs30 etree.map (Wills 2006)	UCVM Vs30 etree.map (Wills 2015)
34.148426	-118.17119	CVM-S4.26.M01	838.8	821.328	821.335	748.00	710.1 (cvmsi)
34.557	-118.125	CVM-S4.26.M01	2573.6	2573.634	324.974	497.68	402.1 (cca)
34.0192	-118.286	CVM-S4.26.M01	313.1	310.574	310.574	280.0	293.5 (cvmsi)
34.64021	-118.91919	CCA-06	3393.9	3331.251	561.377	748.0	710.1 (cca)
36.13968	-120.36015	CCA-06	2277.2	2279.326	261.704	349.0	293.5 (cca)
34.26667	-119.22848	CCA-06	902.3	902.013	244.135	280.0	325.5 (cca)

Querying cs173 slow due to the fine resolution

To run on max memory on hpc cluster nodes

#!/bin/bash
#SBATCH -p scec
#SBATCH --ntasks=1 #number of tasks with one per processor
#SBATCH -N 1 # number of nodes
#SBATCH --mem 0 # Set to unlimited memory
#SBATCH --time=12:00:00
#SBATCH -o ucvm2mesh_mpi_%A.out
#SBATCH -e ucvm2mesh_mpi_%A.err
#SBATCH --export=NONE
#SBATCH --mail-user=maechlin@usc.edu
#SBATCH --mail-type=END

cd /auto/scec-00/maechlin/ucvmc185/utilities
srun -v --mpi=pmi2 /auto/scec-00/maechlin/ucvmc185/utilities/plot_vs30_map.py -b 30.5,-126.0 -u 42.5,-112.5 -s 0.01 -a s -c cs173 -o vs30_cs173.png
-bash-4.2$ 

Related Links:

Plot command used to plot_vs30_map. ./plot_vs30_map.py -b 30.5,-126.0 -u 42.5,-112.5 -s 0.05 -a s -c cs173 -o vs30_cs173.png

Scott posted vs30 values from CyberShake here: http://scec.usc.edu/scecpedia/CyberShake_Study_15.12#Vs_values_v2.0

Notes on Software Implementation for Review

     UCVM/src/ucvm/ucvm_map.c
     -- etree from Walls data
        is used to calculate interpolated vs30 values
          4 surrounding points -- interploate_bilinear
     
     UCVM/src/vs30/vs30_query.c
     -- vs30 calculated as 'slowness'
        vs= 31/vs_sum  (vs_sum => sum of(1/query_data.cmb.vs) 
                        where z from 0 to 30) 

     cvmh1511/src/vs30_gtl.c
     -- vs30 derived GTL base on Ely (2010)
        gtl model file is loaded into model 
        location is 'rounded' in retrieving
        -> These gtl file are generated from Wills
             cvm_vs30_wills.hdr
             cvm_vs30_wills.mdl

     cvms5/src/cvms5.c, cca/src/cca.c 
     -- cvms5_get_vs30_based_gtl()
           calls
           cvms5_get_vs30_value()
              -- get 4 surrounding points from etree(payload 0,1,2,3) 
                 and bilinear interpolation?
suspicous selection:
   vs30_payload[0].vs30 = percent * vs30_payload[0].vs30 + (1 - percent) * vs30_payload[1].vs30;
   vs30_payload[1].vs30 = percent * vs30_payload[2].vs30 + (1 - percent) * vs30_payload[3].vs30;
        return vs30_payload[0].vs30;

     cvms -- no vs30
     cencal -- no vs30

     cvms426/src/vs30_gtl.c 
     -- >>vs30_gtl.c - Vs30 derived GTL based on Ely (2010)
        >>06/2013: DJG - taken from CVM-H and adapted to CVM-S4.23
        >>sprintf(gtldir, "%s/cvm_vs30_wills", modelpath);
        >>if (ADD_GTL) {
        >>   gtl_setup(gtldir);
  gcoor[0]=round((entry->coor_utm[0]-gtl.extent[0])/gtl.spacing);
  gcoor[1]=round((entry->coor_utm[1]-gtl.extent[2])/gtl.spacing);
     location is using rounded() to get the grid point

Related Entries

• UCVM