GSAC - Generic Seismic Application Computing

With the success of the IRIS data management center, the use of large data sets in seismological research has become common. Such data sets, and especially the significantly larger data sets expected from EarthScope, present challenges for analysis with existing tools developed over the last 30 years. For much of the community, the primary format for data analysis is the Seismic Analysis Code (SAC) format developed by Lawrence Livermore National Laboratory. Although somewhat restrictive in meta-data storage, the simplicity and stability of the format has established it as an important component of seismological research. Tools for working with SAC files fall into two categories - custom research quality processing codes and shared display - processing tools such as SAC2000, MatSeis,etc., which were developed primarily for the needs of individual seismic research groups. While the current graphics display and platform dependence of SAC2000 may be resolved if the source code is released, the code complexity and the lack of large-data set analysis or even introductory tutorials could preclude code improvements and development of expertise in its use. We believe that there is a place for new, especially open source, tools. The GSAC effort is an approach that focuses on ease of use, computational speed, transportability, rapid addition of new features and openness so that new and advanced students, researchers and instructors can quickly browse and process large data sets. We highlight several approaches toward data processing under this model. gsac - part of the Computer Programs in Seismology 3.30 distribution has much of the functionality of SAC2000 and works on UNIX/LINUX/MacOS-X/Windows (CYGWIN). This is completely programmed in C from scratch, is small, fast, and easy to maintain and extend. It is command line based and is easily included within shell processing scripts. PySAC is a set of Python functions that allow easy access to SAC files and enable efficient manipulation of SAC files under a variety of operating systems. PySAC has proven to be valuable in organizing large data sets. An array processing package includes standard beamforming algorithms and a search based method for inference of slowness vectors. The search results can be visualized using GMT scripts output by the C programs, and the resulting snapshots can be combined into an animation of the time evolution of the 2D slowness field.