Continuous Data Handling with MsPASS


Seismologists universally use the term “continuous data” to mean long time series records with no significant gaps. The term is actually potentially confusing because the data are not really continuous at all but sampled at a fixed sample interval, nor are they of infinite duration which might be implied if you were discussing Fourier transforms on functions. In practice, “continuous data” in seismology means a series of windowed segments that can be merged to form a longer window of data. The maximum length possible is the duration of data recording. The process of gluing (merging) multiple segments is, more or less, the inverse of cutting a shorter window out of a longer window of data. Gluing/merging data algorithms have to deal with some different issues than windowing.

Some important issues about common practice and the reality of real data are:

  1. There are two common choices for how the data are blocked: (1) day volumes, and (2) raw digitizer files of irregular length created when the digitizer does a memory dump (All current generation digitizers write to an internal memory buffer that is dumped when the memory use exceeds a high water mark.) In either case there is some explicit or implicit (e.g. file naming convention) that provides a hint of the order of the segments.

  2. A large fraction of data contain various types of “data gaps”. Gaps occur for a long list of reasons that are mostly unimportant when analyzing such data. What is important is that data gaps span a range of time scales from a single sample to years.

  3. A less-common problem is a data overlap. An overlap occurs when two segments you need to merge have conflicting time stamps. To make this clear it is helpful to review two MsPASS concepts in the TimeSeries and Seismogram data objects. Let d1 and d2 be two TimeSeries objects that are successive segments we expect to merge with d2 being the segment following d1 in time. In MsPASS we use the attribute t0 (alternatively the method starttime) for the time of sample 0. We also use the method endtime to get the computed time of the last data sample. An overlap is present between these two segments when d2.t0 < d1.endtime(). We know of three ways overlaps can occur: (1) timing problems with the instrument that recorded the data, (2) hardware or software issues in recording instrument that cause packets to be duplicated in memory before they are dumped, and (3) blunders in data management where duplicate files are indexed and defined in a database wf collection (wfdisc table in Antelope).

MsPASS has some capabilities for merging data within the realities of real data noted above. These are discussed in the section below. MsPASS does not, however, substitute for nitty-gritty details network and experiment operators have to face in cleaning field data for archive. We consider that as a problem already solved by Earthscope, the USGS, and global network operators in systems they use for creating the modern data archive of the FDSN. Custom experimental data may need to utilize Earthscope tools to fix problems not covered by MsPASS.

Gap Processing

Internally MsPASS handles data gaps with a subclass of the TimeSeries called TimeSeriesWGaps<mspasspy.ccore.seismic.TimeSeriesWGaps>`. That extension of TimeSeries is written in C++ and is documented here. Like TimeSeries this class has python bindings created with pybind11. All the methods described in the C++ documentation page have python bindings. There are methods for defining gaps, zeroing data in defined gaps, and deleting gaps. See the doxygen pages linked above for details. The python functions that currently deal with data gaps have a second strategy for handling his problem best described in the context of those functions.

Merging Data Segments

There are currently two different methods in MsPASS to handle merging continuous data segments: (1) a special, implicit option of the read_data method of the Database class, and (2) the processing function merge. In addition, there is a special reader function called TimeIntervalReader that can be used to read fixed time windows of data. That function uses merge to do gap and overlap repair.

read_data merge algorithm

This approach is only relevant if you have raw miniseed files you plan to read to initiate your processing sequence. The miniseed format uses a packet structure with each packet normally defining a single channel (Note the standard allows multiplexed data but none of us have ever encountered such data.). The order of the packets is used by all readers we know of to determine if a sequence of packets are a single waveform. If the station codes (“net”, “sta”, “chan”, and “loc” attributes in all MsPASS schemas) change in a sequence of packets readers universally assume that is the end of a given segment. How readers handle a second issue is, however, variable. Each miniseed packet has a time tag that is comparable to the t0 attribute of a TimeSeries object and end time field equivalent to the output of the TimeSeries endtime method. If the t0 value of a packet is greater than some fractional tolerance of 1 sample more than the endtime of the previous packet, a reader will invoke a gap handler. A reader’s gap handler commonly has options for what to do with different kinds of “gaps”, but for this section our definition is defined by the way obspy handles this problem with their Stream merge method described here. That particular algorithm is invoked when reading miniseed data if and only if a block of data defined running the mspass function index_mseed_file is run with the optional argument segment_time_tears is set False. (Note the default is True.). If you need to use this approach, you will need to also take care in defining the value of the following arguments that are passed to obspy’s merge function for gap handle: merge_method, merge_fill_value, and merge_interpolation_samples. Those three arguments are passed directly to obspy merge arguments with a variant of the same names: method, fill_value, and interpolation_samples.

Note an alternative user’s who have previously used obspy for this functionality may want to consider is to write a custom function that utilizes obspy’s merge directly rather than the implied used in read_data.

MsPASS merge function

MsPASS has a native version of a function with a capability similar to the obspy merge function noted above. The MsPASS function add some additional features and, although not verified by formal testing, is likely much faster than the obpsy version due to fundamental differences in the implementation. The docstring for merge describes more details but some key features of this function are:

  • Like obspy’s function of the same name its purpose is to glue/merge a set of waveform components into a single, continuous time series. A key difference is that the obspy function requires an obspy Stream object as input while the MsPASS function uses the “member” container of a TimeSeriesEnsemble object as input.

  • It provides for an optional windowing of the merged result. That approach is useful, for example, for carving events out from a local archive of continuous waveform data in a single step. This feature is useful for reducing the memory footprint of a parallel job.

  • Gaps are flagged and posted with a Metadata approach. Obspy has a set of options for gap handling that are inseparable from the function. Any detected gaps in the MsPASS merge function are posted to the Metadata component of the TimeSeries it returns accessible with the key “gaps”. The content of the “gaps” attribute is a list of one or more python dictionaries with the keyworks “starttime” and “endtime” defining the epoch time range of all gaps in the returned datum. The function also has an optional “zero_gaps”. When set True (default is False) any gaps are explicitly set to zeros. By default the values should be treated as undefined, although in practice they are likely zeros.

  • Overlap handling is controlled by another boolean parameter with the name “fix_overlaps”. When set True the function will check for overlapping data and attempt to repair overlaps only if the sample numerical data match within machine tolerance. The default behavior is to mark the return dead if any overlap is detected. Obspy uses a less dogmatic algorithm driven by an optional function argument called “interpolation_samples”. As noted above it has been our experience that, in general, overlapping data always indicate a data quality problem that invalidates the data when the samples do not match. If you need the obspy functionality use the TimeSeriesEnsemble2Stream and the inverse Trace2TimeSeriesEnsemble to create the obspy input and then restore the returned data to the MsPASS internal data structures


A second MsPASS tool for working with continuous data is a function with the descriptive name TimeIntervalReader. It is designed to do the high-level task of cutting a fixed time interval of data from one or more channels of a continuous data archive. This function is built on top of the lower-level merge but is best thought of as an alternative reader to create ensembles cut from a continuous data archive. For that reason the required arguments are a database handle and the time interval of data to be extracted from the archive. Gap and overlap handling is handled by merge.


Example 1: Create a single waveform in a defined time window from continuous data archive. This script will create a longer TimeSeries object from a set day files for the BHZ channel of GSN station AAK. Ranges are constant for a simple illustration:

# code above would define database handle db
from mspasspy.algorithms.window import merge
from obspy import UTCDateTime
from bson import json_utils  #TODO  verify this is right
net ="II"
loc="00"    # STS-1 sensor at AAK
# TODO:   select a reasonable time interval
# this is a MongoDB query to retrieve all segments with data in the
# desired time range of output_stime to output_etime
query = {
  "$and": [
    { "sta" : {"$eq" : sta}},
    { "net" : {"$eq" : net}},
    { "chan" : {"$eq" : chan}},
    { "loc" : {"$eq" : loc}},
    { "starttime" : {"$lte" : output_etime}},
    { "endtime" : {"$gte" : output_stime}}
cursor=db.wf_miniseed.find(query).sort()   # TODO work out sort format
tsens = db.read_data(query,collection="wf_miniseed")
  merged_data = merge(tsens.member,output_starttime,output_endtime)
    print("Output is ok and has ",merged_data.npts," data samples")
    print("Data have problems - gaps or overlaps caused the datum to be killed")
  print("The following query yielded no data:")

Example 2: parallel read from continuous archive This example is a workflow to build a dataset of waveforms segmented around a set of previously measured P wave arrival time from an archive of continuous data. The example is not complete as it requires implementing a custom function that below is given the symbolic name “arrivals2list”. From that list we create a dask bag and use it to drive a parallel read with read_distributed_data that passes a series of enembles to a function defined at the top that runs merge. The example is made up, but is a prototype for building an event-based data set of all waveforms with P wave times packed the the Earthscope Array Network Facility (ANF) available online from Earthscope.

from mspasspy.db.DBClient import DBClient
import dask.bag as dbg
# we need two database handles.  One for the continuous data (dbc)
# and one to save the segments  (dbo).
dbc = dbclient.get_database("TA2010")   # TA continuous data from 2010
dbo = dbclient.get_database("Pdata2010")  # arrivals from ANF picks

def query_generator(doc):
  Generates a MongoDB query to run against wf_miniseed for waveform
  segments containing any of the time interval time+stwin<=t<=time+etwin.
  Returns a python dict that is used by read_distributed_data to
  generate a dask bag of ensembles.  Note this is an illustrative example
  and makes no sanity checks on inputs for simplicity.

  The input is the same python dict later loaded with the data using
  the container_to_merge argument of read_distributed_data.
  net = doc["net"]
  sta = doc["sta"]
  time = doc["arrival_time"]
  query = {
    "$and": [
      { "sta" : {"$eq" : sta}},
      { "net" : {"$eq" : net},
      { "starttime" : {"$lte" : etime}},
      { "endtime" : {"$gte" : stime}}
  return query

def make_segments(ensemble,stwin,etwin):
  Function used in parallel map operator to create the main output of
  this example workflow.  The input is assumed to be a time-sorted ensemble
  with all data overlapping with the time window defined by
    stwin <= t-arrival_time <= etwin
  where t is time of a d data sample. i.e. stwin an etwin are times relative
  to the arrival time.   The input ensemble is assumed to normally
  contain multiple channels.  The algorithm works through all channels it
  finds.  For each group if the number of segments is 1 it simply uses
  the WindowData function.  If multiple segments are present it calls the
  MsPASS merge function with fix_overlaps set True and with the time
  window requested.  That will return a single waveform segment
  when possible.  If the merge fails that segment will be posted but
  marked dead.

  :param ensemble:  input ensemble for a single station normally containing
    multiple channels.
  :param stwin:  output window relative start time
  :param etwin:  output window relative end UTCDateTime
  # handle dead (empty) ensembles cleanly returning a default constructed
  # datum dead by definition
  if ensemble.dead():
    return TimeSeriesEnsemble()
  net = ensemble["net"]

  sta = ensemble["sta"]
  time = ensemble["arrival_time"]
  # the ensemble will usually contain multiple channels.  We have to
  # handle each independently
  chanset = set()
  for d in ensemble.member:
    chan = d["chan"]
    if loc in d:
  for chan,loc in chanset:
    for d in ensemble.member:
      if d["chan"] == chan:
        if loc:
          if d.is_defined("loc"):
            if d["loc"] == loc:
    # enstmp now has only members match chan and loc - now we can run merge
    # if needed.
    if len(enstmp.member)>1:
      d = merge(enstmp.member,time+stwin,time+etwin,fix_overlaps=True)
      # above logic means this only happens if there is only one segment
      # in that case we can just use WindowData
      d = WindowData(enstmp.member,time+stwin,time+etwin)
  return ensout

# This undefined function would read the arrival time data
# stored in some external form and return a list of python dict
# with the keys 'net', 'sta', and 'arrival_time' defined.
arrival_list = arrival2list(args)
sort_clause=[("chan", 1), ("time",1)]
# This creates a bag from arrival_list that we can pass to the
# reader for loading with the container_to_merge argument
arrival_bag = dbg.from_sequence(arrival_list)
window_start_time = -100.0   # time of window start relative to arrival
window_end_time = 300.0   # time of window end relative to arrival
mybag = dbg.from_sequence(arrival_list)
mybag =,window_start_time,window_end_time)
# qlist now is a list of python dict defining queries.  These are
# passed to the parallel reader  to create a bag of ensemble objects.
mybag = read_distributed_data(qlist,
mybag =
# note the output of this function, with default here, is a list of
# objectids of the saved waveforms
out_ids = write_distributed_data(mybag,dbo,collection="wf_TimeSeries")

The above example is complicated a bit as it is an example of a parallel job. The parallel IO feature of this example are important as this example could run very slowly as a serial job driven my millions of picks that exists for the problem it simulates - an Earthscope TA continuous data archive being accessed to assemble a data set of several million waveform segments built from the ANF catalog. It may be helpful to expand on the main steps of this algorithm:

  1. The first step assumes the existence of an undefined function with the name arrival2list. For the prototype example given it could be driven by the CSS3.0 tables created by the Earthscope Array Network Facility (ANF). That data can currently be found here. The actual implementation would need to select what picks to use and pull out a restricted set of attributes from the CSS3.0 tables creating a large list of tuples with each tuple containing: [‘net’, ‘sta’, ‘arrival_time’] values. Note that step can be done in a couple of lines with the pandas module but is omitted as that is not a unique solution. (e.g. one could also accomplish the same thing with a MongoDB database ‘arrival’ collection with suitable content.)

  2. The from_sequence method of dask bag creates a bag from a list. In this case it becomes a bag of python dict containers. The map call that follows using the custom function defined earlier in the code box creates a bag of python dictionaries that define queries to MongoDB. What the queries are designed to do is described in the docstring for that function.

  3. We call the compute method to actually create the list of queries that will drive the reader. That approach assumes the size of that container is not overwhelming, which is likely a good assumption since the individual dict containers are of the order of 100 bytes each.

  4. The called to read_distributed_data defines the main parallel workflow. In this mode it reads a (large) series of ensembles driven by the input query list. This usage creates a implicit parallel reader. Each instance creates a TimeSeriesEnsemble with all channels for a particular station that have waveforms that intersect with the desired output time segment around the specified arrival time. An important feature exploited in the reader here is that implemented with the argument container_to_merge. The docstrings give details but the main functionality it provides is a way to do a one-to-one mapping of a list of metadata loaded to the ensembles. That feature adds a major efficiency for large data sets compared to the alternative of millions of MongoDB queries that one might consider to solve that problem. This example also requires the sort_clause argument to assure the queries return data in an order consistent with the requirements of the make_segments function that does all main work here.

  5. The map call following read_distributed_data calls the function earlier that handles the slice and dice operation. How that is done is best gleaned fromt he docstring comments.

  6. This example calls the parallel writer, write_distributed_data, to save the results.

Example 3: Application of TimeIntervalReader. This example assumes we have a list of shot times from something like an onshore-offshore experiment using airguns or a set set of land shots with known shot times. The script is serial, but is readily converted to a parallel form using standard concepts described elsewhere in this user’s manual.

from mspasspy.db.DBClient import DBClient
import os
db = dbclient.get_database("my_continuous_dataset")
wetime=50.0   # cut 50 s listen windows
with fd = os.fopen("shottimes.txt"):
  lines = fd.readlines()
  for t in lines:
    tslist = TimeIntervalReader(db,t+wstime,t+wetime,fix_overlaps=True)
    for ts in tslist:
      db.save_data(ts)   # defaults to saving to wf_TimeSeries so omit data_tag