Catalog Cookbook

GISMO can read events from many different earthquake catalog file formats (e.g. Seisan, Antelope) and data sources (e.g. IRIS DMC) using the Catalog.retrieve() method.

Contents

Reading events from IRIS DMC

To load events into a Catalog object we use the Catalog.retrieve method. The first argument is the data source/format - when this is given as 'iris', retrieve uses the irisFetch.m program to retrieve event data via the IRIS webservices. To narrow down our data search we can give retrieve any name-value parameter pairs supported by irisFetch.

In this example we will use retrieve to retrieve all events at IRIS DMC with a magnitude of at least 8.0 from year 2000 to 2014 (inclusive):

greatquakes = Catalog.retrieve('iris', 'minimumMagnitude', 8.0, ...
    'starttime', '2000-01-01', 'endtime', '2015-01-01')
fetching...


20 events found *************

parsing into MATLAB structures
Got 20 events

greatquakes = 


ans =

Catalog

Number of events: 20
Biggest event: 9.100000 at 11-Mar-2011 05:46:23
      otime       yyyy_mm_dd     hh_mm_ss       lon        lat      depth    mag    magtype       etype        ontime    offtime
    __________    __________    __________    _______    _______    _____    ___    _______    ____________    ______    _______

    7.3103e+05    2001_06_23    20:33:09.3    -73.561    -16.303      2.2    8.3    'MW'       'earthquake'    NaN       NaN    
    7.3185e+05    2003_09_25    19:50:07.2     143.87     41.749       33    8.1    'MW'       'earthquake'    NaN       NaN    
     7.323e+05    2004_12_23    14:59:00.6     161.58     -49.71       10      8    'MW'       'earthquake'    NaN       NaN    
    7.3231e+05    2004_12_26    00:58:52.0     95.901     3.4125     26.1    8.2    'MW'       'earthquake'    NaN       NaN    
     7.324e+05    2005_03_28    16:09:35.2     97.113     2.0964       30    8.1    'MW'       'earthquake'    NaN       NaN    
    7.3283e+05    2006_06_01    18:57:02.9     120.88     19.054       13    8.4    'MS'       'earthquake'    NaN       NaN    
    7.3283e+05    2006_06_05    00:50:31.5     119.08     17.992      124      8    'MS'       'earthquake'    NaN       NaN    
      7.33e+05    2006_11_15    11:14:14.5     153.21     46.681     12.2    8.3    'MW'       'earthquake'    NaN       NaN    
    7.3306e+05    2007_01_13    04:23:23.2      154.5     46.231     22.5    8.1    'MW'       'earthquake'    NaN       NaN    
    7.3313e+05    2007_04_01    20:39:56.5     157.03    -8.4468      9.5    8.1    'MW'       'earthquake'    NaN       NaN    
    7.3327e+05    2007_08_15    23:40:58.4    -76.555    -13.384     41.2      8    'MW'       'earthquake'    NaN       NaN    
     7.333e+05    2007_09_12    11:10:26.8      101.4    -4.4637     35.5    8.5    'MW'       'earthquake'    NaN       NaN    
    7.3405e+05    2009_09_29    17:48:11.5    -171.94    -15.512     18.5    8.1    'MW'       'earthquake'    NaN       NaN    
     7.342e+05    2010_02_27    06:34:13.3    -72.933    -36.148     28.1    8.8    'MW'       'earthquake'    NaN       NaN    
    7.3457e+05    2011_03_11    05:46:23.2      142.5     38.296     19.7    9.1    'MW'       'earthquake'    NaN       NaN    
    7.3497e+05    2012_04_11    08:38:37.8     93.014     2.2376     26.3    8.6    'MW'       'earthquake'    NaN       NaN    
    7.3497e+05    2012_04_11    10:43:10.5     92.428     0.7675     21.6    8.2    'MW'       'earthquake'    NaN       NaN    
    7.3527e+05    2013_02_06    01:12:27.0     165.14    -10.738     28.7      8    'MW'       'earthquake'    NaN       NaN    
    7.3538e+05    2013_05_24    05:44:49.6     153.28     54.874    608.9    8.3    'MW'       'earthquake'    NaN       NaN    
    7.3569e+05    2014_04_01    23:46:47.2    -70.769     -19.61       25    8.2    'MWW'      'earthquake'    NaN       NaN    

To access any particular property we can use dot notation, as if the object were a structure, e.g.:

greatquakes.mag
ans =

    8.3000
    8.1000
    8.0000
    8.2000
    8.1000
    8.4000
    8.0000
    8.3000
    8.1000
    8.1000
    8.0000
    8.5000
    8.1000
    8.8000
    9.1000
    8.6000
    8.2000
    8.0000
    8.3000
    8.2000

greatquakes is a Catalog object, an instance of the Catalog class. To see a list of functions ("methods" in object-oriented speak) we can apply to a Catalog object, use the methods command:

methods(greatquakes)
Methods for class Catalog:

Catalog       disp          hist          plot_time     summary       
addwaveforms  eev           plot          plotprmm      webmap        
bvalue        eventrate     plot3         subclassify   write         
combine       gettimerange  plot_counts   subset        

Static methods:

cookbook      retrieve      

Save this dataset so you can use it again later:

save('great_earthquakes.mat', 'greatquakes')

Now we'll do another example - we will get events within 200 km of the great M9.0 Tohoku earthquake that occurred on 2011/03/11. The mainshock parameters are:

   Date/Time:  "2011/03/11 05:46:24"
   Longitude:  142.372
   Latitude:   38.297
   Depth:      30 km

We will limit our search to 1 day before and after the earthquake:

mainshocktime = datenum('2011/03/11 05:46:24');
tohoku_events = Catalog.retrieve('iris', ...
            'radialcoordinates', [38.297 142.372 km2deg(200)], ...
            'starttime', mainshocktime - 1, ...
            'endtime', mainshocktime + 1);
fetching...


1136 events found *************

parsing into MATLAB structures
Got 1136 events

This returns 1136 earthquakes. Let's get a summary:

tohoku_events.summary()
Variables:

    otime: 1136x1 double
        Values:

            min       7.3457e+05
            median    7.3457e+05
            max       7.3457e+05

    yyyy_mm_dd: 1136x10 char

    hh_mm_ss: 1136x10 char

    lon: 1136x1 double
        Values:

            min       140.31
            median    142.59
            max       144.63

    lat: 1136x1 double
        Values:

            min       36.517
            median     37.88
            max       40.044

    depth: 1136x1 double
        Values:

            min           0  
            median       25  
            max       149.7  

    mag: 1136x1 double
        Values:

            min       0.1  
            median    4.3  
            max       9.1  
            NaNs        4  

    magtype: 1136x1 cell string

    etype: 1136x1 cell string

    ontime: 1136x1 double
        Values:

            min        NaN    
            median     NaN    
            max        NaN    
            NaNs      1136    

    offtime: 1136x1 double
        Values:

            min        NaN     
            median     NaN     
            max        NaN     
            NaNs      1136     

Save this dataset so you can use it again later:

save('tohoku_events.mat', 'tohoku_events')

Readings events from an Antelope database

To load event data from an Antelope/Datascope CSS3.0 database you will need to have Antelope (http://www.brtt.com/software.html) installed, including the Antelope toolbox for MATLAB (ATM). To see if ATM is installed, use the admin.antelope_exists() command, e.g.

if admin.antelope_exists()
    disp('Antelope Toolbox for MATLAB found')
else
    disp('Sorry, Antelope not found')
end
Antelope Toolbox for MATLAB found

If you do not have ATM installed, any attempt to read from an Antelope database will result in a warning like:

     Warning: Sorry, cannot read event Catalog from Antelope database as Antelope toolbox for MATLAB not found

and an empty Catalog object will be returned.

%

For the purpose of this exercise we will be using data from Redoubt volcano from 2009/03/20 to 2009/03/23. We will use snippets from two catalogs that are provided with GISMO in Antelope format:

Both catalog segments are included in the "demo" directory. We will now load the official AVO catalog into an Events object:

dbpath = Catalog.demo.demodb('avo');
avocatalog = Catalog.retrieve('antelope', 'dbpath', dbpath);
Loading data from /Users/glennthompson/src/GISMO/core/+Catalog/+demo/css3.0/avodb200903
Got 1441 events

This should load 1441 events. What if we only want events within 20km of Redoubt volcano? There are two ways to do this. The first is the use the radialcoordinates parameter:

redoubtLon = -152.7431;
redoubtLat = 60.4853;
maxR = km2deg(20.0);
redoubt_events = Catalog.retrieve('antelope', 'dbpath', dbpath, ...
    'radialcoordinates', [redoubtLat redoubtLon maxR])
Loading data from /Users/glennthompson/src/GISMO/core/+Catalog/+demo/css3.0/avodb200903
Got 1397 events

redoubt_events = 


ans =

Catalog

Number of events: 1397
Biggest event: 2.000000 at 22-Mar-2009 20:21:20
      otime       yyyy_mm_dd     hh_mm_ss       lon       lat      depth    mag     magtype    etype    ontime    offtime
    __________    __________    __________    _______    ______    _____    ____    _______    _____    ______    _______

    7.3385e+05    2009_03_20    00:24:41.6     -152.8    60.481     3.62     0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    07:29:44.8    -152.74    60.494       -3    -0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    07:43:40.6    -152.78    60.484       -3     0.4    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    07:48:53.5    -152.75    60.455     0.44     0.7    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    11:09:21.9    -152.77    60.497     3.09     0.5    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    11:10:43.2    -152.77    60.488     0.69     0.8    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    12:20:02.7    -152.77     60.48    -0.15     0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    13:21:06.0    -152.76    60.488    -2.78     0.4    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    14:04:39.6    -152.76     60.48     3.72     0.5    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    15:25:39.3    -152.76    60.486     -2.9     0.4    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    16:51:47.1    -152.78    60.483       -3     0.5    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    16:52:08.1    -152.77    60.501     2.12     0.5    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    17:10:26.7    -152.77    60.484     0.61     0.9    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    17:17:16.1    -152.73    60.495       -3    -0.1    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    17:46:27.3    -152.78    60.505     2.59    -0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    17:46:39.2    -152.76    60.489    -2.46     0.8    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    17:59:41.8    -152.77    60.487    -2.67     0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    19:42:17.3    -152.77    60.484     2.28     0.4    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    19:44:11.7    -152.76     60.49     0.04     0.7    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    19:46:08.9    -152.77    60.483       -3    -0.2    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    20:02:49.4    -152.77    60.484     0.12     0.9    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    20:23:13.9    -152.74    60.489    -1.39     0.4    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    20:27:29.6    -152.77    60.491    -2.55     0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    20:35:09.7    -152.77    60.462     5.19     0.5    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    20:36:50.5    -152.76    60.487    -2.92     0.4    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    20:39:44.5    -152.77    60.485     3.52     0.5    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    20:52:01.3    -152.78    60.474     2.36     0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    21:00:45.1    -152.76    60.484     0.65       1    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    21:11:41.9    -152.77    60.483    -2.48     0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    21:13:05.4    -152.78    60.461      6.4       1    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    21:45:52.3    -152.76    60.482       -3     0.1    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    21:45:54.8    -152.81    60.482    -2.74     0.8    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    22:13:19.7    -152.77    60.488    -2.92     0.1    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    22:20:14.2    -152.77    60.487      2.8     0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    22:32:48.9    -152.77    60.501       -3    -0.5    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    22:39:30.5    -152.76    60.491        1     0.6    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    22:42:14.6    -152.77     60.49     3.57     0.5    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    22:59:34.3    -152.78    60.478       -3     0.5    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    23:20:30.0    -152.77    60.491     0.59     0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    23:43:54.8    -152.76    60.489     0.48     0.9    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    23:52:53.7    -152.77     60.49     1.14     0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_21    00:09:20.6    -152.72    60.487       -3    -0.9    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_21    00:09:34.6    -152.77    60.491     3.04     0.5    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_21    00:19:32.5    -152.77    60.489    -2.83       0    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_21    00:24:39.7    -152.77    60.487     0.66     0.4    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_21    00:42:16.9    -152.77    60.497     2.73     0.4    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_21    00:44:30.9    -152.77    60.487    -2.06     0.4    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_21    00:47:06.5    -152.77    60.492    -0.14    -0.1    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_21    00:47:12.3    -152.77    60.493    -0.49     0.6    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_21    01:16:51.1    -152.77    60.483     -0.4     0.6    'ml'       'a'      NaN       NaN    

* Only showing first 50 rows/events - to see all rows/events use:
*      catalogObject.disp(true)

Anyone familiar with Antelope will know that it subsets databases by using a dbeval subset expression, and the command above does this internally. You can also specify a subset expression directly. The following example is completely equivalent to that above:

expr = sprintf('distance(lat, lon, %f, %f) < %f',redoubtLat, redoubtLon,maxR)
redoubt_events = Catalog.retrieve('antelope', 'dbpath', dbpath, ...
    'subset_expression', expr)
expr =

distance(lat, lon, 60.485300, -152.743100) < 0.179864

Loading data from /Users/glennthompson/src/GISMO/core/+Catalog/+demo/css3.0/avodb200903
Got 1397 events

redoubt_events = 


ans =

Catalog

Number of events: 1397
Biggest event: 2.000000 at 22-Mar-2009 20:21:20
      otime       yyyy_mm_dd     hh_mm_ss       lon       lat      depth    mag     magtype    etype    ontime    offtime
    __________    __________    __________    _______    ______    _____    ____    _______    _____    ______    _______

    7.3385e+05    2009_03_20    00:24:41.6     -152.8    60.481     3.62     0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    07:29:44.8    -152.74    60.494       -3    -0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    07:43:40.6    -152.78    60.484       -3     0.4    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    07:48:53.5    -152.75    60.455     0.44     0.7    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    11:09:21.9    -152.77    60.497     3.09     0.5    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    11:10:43.2    -152.77    60.488     0.69     0.8    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    12:20:02.7    -152.77     60.48    -0.15     0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    13:21:06.0    -152.76    60.488    -2.78     0.4    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    14:04:39.6    -152.76     60.48     3.72     0.5    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    15:25:39.3    -152.76    60.486     -2.9     0.4    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    16:51:47.1    -152.78    60.483       -3     0.5    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    16:52:08.1    -152.77    60.501     2.12     0.5    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    17:10:26.7    -152.77    60.484     0.61     0.9    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    17:17:16.1    -152.73    60.495       -3    -0.1    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    17:46:27.3    -152.78    60.505     2.59    -0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    17:46:39.2    -152.76    60.489    -2.46     0.8    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    17:59:41.8    -152.77    60.487    -2.67     0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    19:42:17.3    -152.77    60.484     2.28     0.4    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    19:44:11.7    -152.76     60.49     0.04     0.7    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    19:46:08.9    -152.77    60.483       -3    -0.2    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    20:02:49.4    -152.77    60.484     0.12     0.9    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    20:23:13.9    -152.74    60.489    -1.39     0.4    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    20:27:29.6    -152.77    60.491    -2.55     0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    20:35:09.7    -152.77    60.462     5.19     0.5    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    20:36:50.5    -152.76    60.487    -2.92     0.4    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    20:39:44.5    -152.77    60.485     3.52     0.5    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    20:52:01.3    -152.78    60.474     2.36     0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    21:00:45.1    -152.76    60.484     0.65       1    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    21:11:41.9    -152.77    60.483    -2.48     0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    21:13:05.4    -152.78    60.461      6.4       1    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    21:45:52.3    -152.76    60.482       -3     0.1    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    21:45:54.8    -152.81    60.482    -2.74     0.8    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    22:13:19.7    -152.77    60.488    -2.92     0.1    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    22:20:14.2    -152.77    60.487      2.8     0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    22:32:48.9    -152.77    60.501       -3    -0.5    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    22:39:30.5    -152.76    60.491        1     0.6    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    22:42:14.6    -152.77     60.49     3.57     0.5    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    22:59:34.3    -152.78    60.478       -3     0.5    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    23:20:30.0    -152.77    60.491     0.59     0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    23:43:54.8    -152.76    60.489     0.48     0.9    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_20    23:52:53.7    -152.77     60.49     1.14     0.3    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_21    00:09:20.6    -152.72    60.487       -3    -0.9    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_21    00:09:34.6    -152.77    60.491     3.04     0.5    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_21    00:19:32.5    -152.77    60.489    -2.83       0    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_21    00:24:39.7    -152.77    60.487     0.66     0.4    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_21    00:42:16.9    -152.77    60.497     2.73     0.4    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_21    00:44:30.9    -152.77    60.487    -2.06     0.4    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_21    00:47:06.5    -152.77    60.492    -0.14    -0.1    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_21    00:47:12.3    -152.77    60.493    -0.49     0.6    'ml'       'a'      NaN       NaN    
    7.3385e+05    2009_03_21    01:16:51.1    -152.77    60.483     -0.4     0.6    'ml'       'a'      NaN       NaN    

* Only showing first 50 rows/events - to see all rows/events use:
*      catalogObject.disp(true)

Save this dataset so you can use it again later:

save('redoubt_events.mat', 'redoubt_events')

Reading events from a Seisan database

Here we load events from a Seisan catalog. A Seisan "Sfile" contains all the metadata for 1 event. These Sfiles are stored in a flat-file database structure the path to which is: $SEISAN_TOP/REA/databaseName. Sfiles are organized in year/month subdirectories under this path.

SCAFFOLD: INCLUDE DEMO DATASET FROM MVOE

The following will navigate this where in this case $SEISAN_TOP = '/raid/data/seisan' and the databaseName is MVOE_ which stands for the Montserrat Volcano Observatory Event database. (In Seisan, databaseName is limited to exactly 5 characters).

This example will load Sfiles from 4 hours on 1st Nov, 1996. This is a slow function to run as MATLAB is slow at parsing text files, and there are many events per day in this particular database.

demodir = Catalog.demo.demo_path();
dbpath = fullfile(demodir,'seisan');
montserrat_events = Catalog.retrieve('seisan', ...
    'dbpath', dbpath, ...
	'startTime', '1996/11/01 11:00:00', ....
	'endTime', '1996/11/01 15:00:00')
There are 29 sfiles matching your request in /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1108-25L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1115-03L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1131-08L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1135-09L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1141-34L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1146-34L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1154-07L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1214-17L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1232-42L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1239-55L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1246-24L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1301-08L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1306-18L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1314-39L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1320-33L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1326-24L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1329-18L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1334-58L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1336-31L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1353-50L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1408-52L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1412-15L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1417-35L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1424-29L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1432-44L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1433-40L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1443-02L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1450-08L.S199611
Processing /Users/glennthompson/src/GISMO/core/+Catalog/+demo/seisan/1996/11/01-1459-30L.S199611
Got 29 events

montserrat_events = 


ans =

Catalog

Number of events: 29
Biggest event: NaN at 01-Nov-1996 11:08:25
      otime       yyyy_mm_dd     hh_mm_ss       lon       lat      depth    mag    magtype    etype    ontime    offtime
    __________    __________    __________    _______    ______    _____    ___    _______    _____    ______    _______

    7.2933e+05    1996_11_01    11:08:25.0        NaN       NaN    NaN      NaN    'u'        'h'      NaN       NaN    
    7.2933e+05    1996_11_01    11:15:03.0        NaN       NaN    NaN      NaN    'u'        't'      NaN       NaN    
    7.2933e+05    1996_11_01    11:31:08.0        NaN       NaN    NaN      NaN    'u'        't'      NaN       NaN    
    7.2933e+05    1996_11_01    11:35:20.0    -62.177    16.713    1.7      NaN    'u'        'h'      NaN       NaN    
    7.2933e+05    1996_11_01    11:41:34.0        NaN       NaN    NaN      NaN    'u'        't'      NaN       NaN    
    7.2933e+05    1996_11_01    11:46:42.9    -62.156     16.63    3.1      NaN    'u'        'h'      NaN       NaN    
    7.2933e+05    1996_11_01    11:54:07.0        NaN       NaN    NaN      NaN    'u'        'h'      NaN       NaN    
    7.2933e+05    1996_11_01    12:14:17.0        NaN       NaN    NaN      NaN    'u'        'h'      NaN       NaN    
    7.2933e+05    1996_11_01    12:32:42.0        NaN       NaN    NaN      NaN    'u'        'h'      NaN       NaN    
    7.2933e+05    1996_11_01    12:39:55.0        NaN       NaN    NaN      NaN    'u'        'h'      NaN       NaN    
    7.2933e+05    1996_11_01    12:46:24.0        NaN       NaN    NaN      NaN    'u'        'h'      NaN       NaN    
    7.2933e+05    1996_11_01    13:01:08.0        NaN       NaN    NaN      NaN    'u'        'h'      NaN       NaN    
    7.2933e+05    1996_11_01    13:06:18.0        NaN       NaN    NaN      NaN    'u'        'h'      NaN       NaN    
    7.2933e+05    1996_11_01    13:14:39.0        NaN       NaN    NaN      NaN    'u'        'h'      NaN       NaN    
    7.2933e+05    1996_11_01    13:20:43.0    -62.176    16.713      0      NaN    'u'        'h'      NaN       NaN    
    7.2933e+05    1996_11_01    13:26:24.0        NaN       NaN    NaN      NaN    'u'        'h'      NaN       NaN    
    7.2933e+05    1996_11_01    13:29:18.0        NaN       NaN    NaN      NaN    'u'        'e'      NaN       NaN    
    7.2933e+05    1996_11_01    13:34:58.0        NaN       NaN    NaN      NaN    'u'        'h'      NaN       NaN    
    7.2933e+05    1996_11_01    13:36:42.0    -62.177    16.715      0      NaN    'u'        'h'      NaN       NaN    
    7.2933e+05    1996_11_01    13:53:50.0        NaN       NaN    NaN      NaN    'u'        'h'      NaN       NaN    
    7.2933e+05    1996_11_01    14:08:52.0        NaN       NaN    NaN      NaN    'u'        't'      NaN       NaN    
    7.2933e+05    1996_11_01    14:12:15.0        NaN       NaN    NaN      NaN    'u'        'h'      NaN       NaN    
    7.2933e+05    1996_11_01    14:17:35.0        NaN       NaN    NaN      NaN    'u'        'u'      NaN       NaN    
    7.2933e+05    1996_11_01    14:24:40.0    -62.176    16.714    1.3      NaN    'u'        'h'      NaN       NaN    
    7.2933e+05    1996_11_01    14:32:44.0        NaN       NaN    NaN      NaN    'u'        'r'      NaN       NaN    
    7.2933e+05    1996_11_01    14:33:40.0        NaN       NaN    NaN      NaN    'u'        'h'      NaN       NaN    
    7.2933e+05    1996_11_01    14:43:13.0    -62.177    16.711    1.8      NaN    'u'        'h'      NaN       NaN    
    7.2933e+05    1996_11_01    14:50:08.0        NaN       NaN    NaN      NaN    'u'        'h'      NaN       NaN    
    7.2933e+05    1996_11_01    14:59:30.0        NaN       NaN    NaN      NaN    'u'        'h'      NaN       NaN    

Save this dataset so you can use it again later:

save('montserrat_events.mat', 'montserrat_events')

Only a few of these earthquakes have been located and even fewer have magnitudes. This is common for volcanic earthquakes. Most of these are of type 'h' - a hybrid earthquake.

Converting a Zmap data structure to a Catalog object

ZMap is a graphical application written by Max Wyss & Stefan Wiemer for statistical analysis of catalogs. GISMO can convert a ZMap data structure into a Catalog object with:

  catalogObject = Catalog.retrieve('zmap', zmapdata)

Plotting hypocenter maps

Catalog objects have three builtin ways for plotting hypocenters

Reload the Tohoku dataset

load tohoku_events.mat

Map view & cross-sections

tohoku_events.plot()

3D-Hypocenters

tohoku_events.plot3()

web map

tohoku_events.webmap()
tohoku_events.webmap()
wmzoom(7)

Plotting time series of events

Magnitude-time plot

tohoku_events.plot_time()

Earthquake event counts (number of events per unit time) A plot of seismic catalog per day is often called an "event counts" plot. In GISMO, we call this an "event rate plot" and the first step is to generate an EventRate object. Here our binsize is 1/24 days, i.e. 1 hour.

eventrateObject = tohoku_events.eventrate('binsize', 1/24)
eventrateObject = 

  EventRate with properties:

            time: [1x47 double]
          counts: [1x47 double]
       mean_rate: [1x47 double]
     median_rate: [1x47 double]
         cum_mag: [1x47 double]
        mean_mag: [1x47 double]
      median_mag: [1x47 double]
          energy: [1x47 double]
    total_counts: 1136
       total_mag: 9.1024
         numbins: 47
         min_mag: [1x47 double]
         max_mag: [1x47 double]
           etype: {'earthquake'}
            snum: 7.3457e+05
            enum: 7.3457e+05
         binsize: 0.0417
        stepsize: 0.0417
     misc_fields: {}
     misc_values: {}

Now plot the EventRate object:

eventrateObject.plot()

We can do the same thing for another dataset, e.g. redoubt_events

redoubt_events.plot_time()
erobj_red = redoubt_events.eventrate('binsize', 1/24)
erobj_red.plot()
erobj_red = 

  EventRate with properties:

            time: [1x71 double]
          counts: [1x71 double]
       mean_rate: [1x71 double]
     median_rate: [1x71 double]
         cum_mag: [1x71 double]
        mean_mag: [1x71 double]
      median_mag: [1x71 double]
          energy: [1x71 double]
    total_counts: 1397
       total_mag: 2.6140
         numbins: 71
         min_mag: [1x71 double]
         max_mag: [1x71 double]
           etype: {2x1 cell}
            snum: 7.3385e+05
            enum: 7.3385e+05
         binsize: 0.0417
        stepsize: 0.0417
     misc_fields: {}
     misc_values: {}

To see more of the things we can do with EventRate objects see the EventRate cookbook EventRate.html

Analysis

Peak event rate (PR) and maximum magnitude A common type of analysis is to identify the peak rate in an earthquake sequence such as this preshock-mainshock-aftershock sequence or an earthquake swarm. This can be done with:

tohoku_events.plotprmm()
MM=9.1 occurs at 50.0% of time series
PR=32 occurs at 53.5% of time series

In the command window this returns: MM=9.1 occurs at 50.0% of time series PR=32 occurs at 53.5% of time series

These are labelled on the plot above with PR and MM.

Now with the Redoubt dataset

redoubt_events.plotprmm()
MM=2.0 occurs at 94.9% of time series
PR=76 occurs at 91.5% of time series

b-value and magnitude of completeness Code from "ZMap" (written by Stefan Wiemer and others) has been added to Catalog to compute and plot b-values and the magnitude of completeness.

Definitions:

Just calling the bvalue method, i.e.

catalogObject.bvalue()

displays a menu of techniques available to compute b-value (b) and magnitude of completeness (Mc):

   --------------------------------------------------------
   Usage is: eventsObject.bvalue(mcType)
   --------------------------------------------------------
   mcType can be:
   1: Maximum curvature
   2: Fixed Mc = minimum magnitude (Mmin)
   3: Mc90 (90% probability)
   4: Mc95 (95% probability)
   5: Best combination (Mc95 - Mc90 - maximum curvature)

We will use the first menu option:

tohoku_events.bvalue(1)

In this particular example, the b-value is 0.6 and the magnitude of completeness is 4.2.

Now for the Redoubt events:

redoubt_events.bvalue(1)

Saving Catalog objects to disk

Writing to a MAT file We've already seen how to do this, the general syntax is: save('myfilename.mat', 'myCatalogObject')

This can simply be loaded again with: load('myfilename.mat')

Writing to an Antelope CSS3.0 database This method requires the Antelope toolbox for MATLAB and writes the Catalog as a CSS3.0 flat-file database:

First make sure there is no database with this name already - else we will be appending to it:

delete greatquakes_db*

Now write to the database

greatquakes.write('antelope', 'greatquakes_db', 'css3.0')

This database can be reloaded with:

greatquakes2 = Catalog.retrieve('antelope', 'dbpath', 'greatquakes_db')
Loading data from greatquakes_db
Got 20 events

greatquakes2 = 


ans =

Catalog

Number of events: 20
Biggest event: 9.100000 at 11-Mar-2011 05:46:23
      otime       yyyy_mm_dd     hh_mm_ss       lon        lat      depth    mag     magtype      etype    ontime    offtime
    __________    __________    __________    _______    _______    _____    ___    __________    _____    ______    _______

    7.3103e+05    2001_06_23    20:33:09.3    -73.561    -16.303      2.2    8.3    {1x1 cell}    'eq'     NaN       NaN    
    7.3185e+05    2003_09_25    19:50:07.2     143.87     41.749       33    8.1    {1x1 cell}    'eq'     NaN       NaN    
     7.323e+05    2004_12_23    14:59:00.6     161.58     -49.71       10      8    {1x1 cell}    'eq'     NaN       NaN    
    7.3231e+05    2004_12_26    00:58:52.0     95.901     3.4125     26.1    8.2    {1x1 cell}    'eq'     NaN       NaN    
     7.324e+05    2005_03_28    16:09:35.2     97.113     2.0964       30    8.1    {1x1 cell}    'eq'     NaN       NaN    
    7.3283e+05    2006_06_01    18:57:02.9     120.88     19.054       13    8.4    {1x1 cell}    'eq'     NaN       NaN    
    7.3283e+05    2006_06_05    00:50:31.5     119.08     17.992      124      8    {1x1 cell}    'eq'     NaN       NaN    
      7.33e+05    2006_11_15    11:14:14.5     153.21     46.681     12.2    8.3    {1x1 cell}    'eq'     NaN       NaN    
    7.3306e+05    2007_01_13    04:23:23.2      154.5     46.231     22.5    8.1    {1x1 cell}    'eq'     NaN       NaN    
    7.3313e+05    2007_04_01    20:39:56.5     157.03    -8.4468      9.5    8.1    {1x1 cell}    'eq'     NaN       NaN    
    7.3327e+05    2007_08_15    23:40:58.4    -76.555    -13.384     41.2      8    {1x1 cell}    'eq'     NaN       NaN    
     7.333e+05    2007_09_12    11:10:26.8      101.4    -4.4637     35.5    8.5    {1x1 cell}    'eq'     NaN       NaN    
    7.3405e+05    2009_09_29    17:48:11.5    -171.94    -15.512     18.5    8.1    {1x1 cell}    'eq'     NaN       NaN    
     7.342e+05    2010_02_27    06:34:13.3    -72.933    -36.148     28.1    8.8    {1x1 cell}    'eq'     NaN       NaN    
    7.3457e+05    2011_03_11    05:46:23.2      142.5     38.296     19.7    9.1    {1x1 cell}    'eq'     NaN       NaN    
    7.3497e+05    2012_04_11    08:38:37.8     93.014     2.2376     26.3    8.6    {1x1 cell}    'eq'     NaN       NaN    
    7.3497e+05    2012_04_11    10:43:10.5     92.428     0.7675     21.6    8.2    {1x1 cell}    'eq'     NaN       NaN    
    7.3527e+05    2013_02_06    01:12:27.0     165.14    -10.738     28.7      8    {1x1 cell}    'eq'     NaN       NaN    
    7.3538e+05    2013_05_24    05:44:49.6     153.28     54.874    608.9    8.3    {1x1 cell}    'eq'     NaN       NaN    
    7.3569e+05    2014_04_01    23:46:47.2    -70.769     -19.61       25    8.2    {1x1 cell}    'eq'     NaN       NaN    

Compare:

greatquakes
greatquakes = 


ans =

Catalog

Number of events: 20
Biggest event: 9.100000 at 11-Mar-2011 05:46:23
      otime       yyyy_mm_dd     hh_mm_ss       lon        lat      depth    mag    magtype       etype        ontime    offtime
    __________    __________    __________    _______    _______    _____    ___    _______    ____________    ______    _______

    7.3103e+05    2001_06_23    20:33:09.3    -73.561    -16.303      2.2    8.3    'MW'       'earthquake'    NaN       NaN    
    7.3185e+05    2003_09_25    19:50:07.2     143.87     41.749       33    8.1    'MW'       'earthquake'    NaN       NaN    
     7.323e+05    2004_12_23    14:59:00.6     161.58     -49.71       10      8    'MW'       'earthquake'    NaN       NaN    
    7.3231e+05    2004_12_26    00:58:52.0     95.901     3.4125     26.1    8.2    'MW'       'earthquake'    NaN       NaN    
     7.324e+05    2005_03_28    16:09:35.2     97.113     2.0964       30    8.1    'MW'       'earthquake'    NaN       NaN    
    7.3283e+05    2006_06_01    18:57:02.9     120.88     19.054       13    8.4    'MS'       'earthquake'    NaN       NaN    
    7.3283e+05    2006_06_05    00:50:31.5     119.08     17.992      124      8    'MS'       'earthquake'    NaN       NaN    
      7.33e+05    2006_11_15    11:14:14.5     153.21     46.681     12.2    8.3    'MW'       'earthquake'    NaN       NaN    
    7.3306e+05    2007_01_13    04:23:23.2      154.5     46.231     22.5    8.1    'MW'       'earthquake'    NaN       NaN    
    7.3313e+05    2007_04_01    20:39:56.5     157.03    -8.4468      9.5    8.1    'MW'       'earthquake'    NaN       NaN    
    7.3327e+05    2007_08_15    23:40:58.4    -76.555    -13.384     41.2      8    'MW'       'earthquake'    NaN       NaN    
     7.333e+05    2007_09_12    11:10:26.8      101.4    -4.4637     35.5    8.5    'MW'       'earthquake'    NaN       NaN    
    7.3405e+05    2009_09_29    17:48:11.5    -171.94    -15.512     18.5    8.1    'MW'       'earthquake'    NaN       NaN    
     7.342e+05    2010_02_27    06:34:13.3    -72.933    -36.148     28.1    8.8    'MW'       'earthquake'    NaN       NaN    
    7.3457e+05    2011_03_11    05:46:23.2      142.5     38.296     19.7    9.1    'MW'       'earthquake'    NaN       NaN    
    7.3497e+05    2012_04_11    08:38:37.8     93.014     2.2376     26.3    8.6    'MW'       'earthquake'    NaN       NaN    
    7.3497e+05    2012_04_11    10:43:10.5     92.428     0.7675     21.6    8.2    'MW'       'earthquake'    NaN       NaN    
    7.3527e+05    2013_02_06    01:12:27.0     165.14    -10.738     28.7      8    'MW'       'earthquake'    NaN       NaN    
    7.3538e+05    2013_05_24    05:44:49.6     153.28     54.874    608.9    8.3    'MW'       'earthquake'    NaN       NaN    
    7.3569e+05    2014_04_01    23:46:47.2    -70.769     -19.61       25    8.2    'MWW'      'earthquake'    NaN       NaN    

This concludes the Catalog cookbook/tutorial.