VERCE Delivers a Productive E-science Environment for Seismology Research

Abstract

The VERCE project has pioneered an e-Infrastructure to support researchers using established simulation codes on high-performance computers in conjunction with multiple sources of observational data. This is accessed and organised via the VERCE science gateway that makes it convenient for seismologists to use these resources from any location via the Internet. Their data handling is made flexible and scalable by two Python libraries, ObsPy and dispel4py and by data services delivered by ORFEUS and EUDAT. Provenance driven tools enable rapid exploration of results and of the relationships between data, which accelerates understanding and method improvement. These powerful facilities are integrated and draw on many other e-Infrastructures. This paper presents the motivation for building such systems, it reviews how solid-Earth scientists can make significant research progress using them and explains the architecture and mechanisms that make their construction and operation achievable. We conclude with a summary of the achievements to date and identify the crucial steps needed to extend the capabilities for seismologists, for solid-Earth scientists and for similar disciplines.

Active Provenance for Data-Intensive Workflows: Engaging Users and Developers

“…We show how users and developers contextualise the lineage with control over inclusion and activation. A preliminary version is actively used in an HPC context [7], where run-time validation and visualisation were essential requirements. To meet requirements we have added mechanisms to include in provenance traces, system-and user-specified properties about the data and the computation.…”

“…The options currently available are described in detail in Table III. In our implementation, the assignment of the provenance types is automatically handled by the framework which adopts metaprogramming 7 techniques. This design and implementation choice, explained in Figure 2, allows the attribution of provenance types by declaratively combining different categories of types.…”

“…The framework is available for data-streaming applications using dispel4py [19] mapped to several platforms. It is in use for demanding applications in seismology and climate-impact research, for the VERCE 1 [7] and CLIPC 2 projects, and now the DARE project 3 . The DARE architecture and vision, described in a contemporary paper [8], is the context for our recent achievements, illustrated with the seismic rapid assessment use case.…”

dispel4py: An Agile Framework for Data-Intensive eScience

“…However, potential techniques, including seismic interferometry and earthquake "repeater" analysis, require a large number of waveform cross-correlations, which is computationally intensive, and is particularly challenging in real-time. With dispel4py we have developed the Seismic Ambient Noise Cross-Correlation workflow (also called the xcorr workflow) as part of the VERCE 12 project [18], which preprocesses and cross-correlates traces from several stations in real-time. The xcorr workflow consists of two main phases:…”

“…It distinguishes between nouns, verbs, adjectives and adverbs, and each synset is automatically annotated according to positivity, negativity and neutrality. The original code used for building the sentiment workflow can be found at 18 . Figure 7 shows the sentiment workflow, which first scans the tweets preprocessing the words they contain, and then classifies each tweet based on the total counts for positive and negative words.…”