Routine Data Processing in Earthquake Seismology: With Sample Data, Exercises and Software


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Peter Brosche. Source Mechanisms of Earthquakes. Zhizheng Wu. Bradley Hacker. Particle Image Velocimetry: Recent Improvements. Michel Stanislas. ASDF greatly eases that pain by storing everything in a well-defined place within the same file. The need to find and assemble the different pieces can thus be performed by software, thereby requiring less mental work from scientists. ASDF, as shown in the previous sections, can store waveforms, events, station meta information, provenance, and auxiliary data all in the same file.


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Additionally, it permits relations between these items. For example, each waveform trace can be associated with a certain event, or a certain event origin or focal mechanism. Relations for each block of data to its provenance record are also retained. All in all this allows for fully self-explanatory, complete data sets preserving complex internal relations.

This is something that is constantly required in scientific and data driven applications. Today, people usually deal with this by using project-specific directory structures that cannot be exchanged nor properly archived, and ASDF clearly improves that system on all fronts.

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Having yet another format induces more complexity and, potentially, noise into the community using that type of data and the landscape of software able to deal with it. This sections addresses why no single existing data format in seismology is able to satisfy our needs and thus justifies the introduction of the ASDF format. We limit ourselves to detailing alternative waveform formats as we directly incorporate the StationXML and QuakeML formats and no true alternative to storing derived data or provenance is currently in existence. A wide variety of different seismological data formats is used by researchers world wide.

Please see Bormann and Havskov for additional information and descriptions of more formats. SEED was developed in the late s and at least the data-only part MiniSEED continues to be in wide use today, and will likely continue to be the dominant data streaming and archival format for the foreseeable future. The ASDF format does not attempt to replace it. Some of MiniSEED's features, such as the ability to build up large data volumes by concatenating small and short pieces, are very well suited for their use in data archives, where data is constantly streamed in.


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  • While the full SEED format can in theory store waveforms as well as station meta information, the complexity of the format hinders that. It furthermore can only properly store raw waveform recordings and no event information. Additionally, the dataless part of SEED, e. MiniSEED, on the other hand, is more than capable of storing arbitrarily large waveform volumes, but the file then contains no index of what is in it, so one must always read the entire file to figure that out, making large data volumes fairly impractical.

    Additionally, the amount of meta information in MiniSEED files is strongly limited, so one always needs additional files to work with it. Summing up, MiniSEED is a good data archival format for data centres, streaming and low-latency applications, but it is not well suited for the later research and processing stages, where ASDF has significant advantages.

    This is likely due to two reasons: the popularity of the SAC program itself and the relative simplicity of the format with a number of header fields that can be adapted to different purposes. The most obvious ones are the ability to store multiple components—including gaps and overlaps—in a single file without awkward workarounds, as well as the potential to create full data sets incorporating all necessary meta information.

    ASDF is, for large workflows, also more efficient, facilitates the storage of different data types—integers as well as floats—and, with the help of HDF5 offers file compression and check summing. The combination of these factors results in ASDF being more suitable and convenient for many workflows.

    Routine Data Processing in Earthquake Seismology: With Sample Data, Exercises and Software Routine Data Processing in Earthquake Seismology: With Sample Data, Exercises and Software
    Routine Data Processing in Earthquake Seismology: With Sample Data, Exercises and Software Routine Data Processing in Earthquake Seismology: With Sample Data, Exercises and Software
    Routine Data Processing in Earthquake Seismology: With Sample Data, Exercises and Software Routine Data Processing in Earthquake Seismology: With Sample Data, Exercises and Software
    Routine Data Processing in Earthquake Seismology: With Sample Data, Exercises and Software Routine Data Processing in Earthquake Seismology: With Sample Data, Exercises and Software
    Routine Data Processing in Earthquake Seismology: With Sample Data, Exercises and Software Routine Data Processing in Earthquake Seismology: With Sample Data, Exercises and Software
    Routine Data Processing in Earthquake Seismology: With Sample Data, Exercises and Software Routine Data Processing in Earthquake Seismology: With Sample Data, Exercises and Software
    Routine Data Processing in Earthquake Seismology: With Sample Data, Exercises and Software Routine Data Processing in Earthquake Seismology: With Sample Data, Exercises and Software
    Routine Data Processing in Earthquake Seismology: With Sample Data, Exercises and Software Routine Data Processing in Earthquake Seismology: With Sample Data, Exercises and Software

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