A new operational model that increases experiment diversity and shortens time to publication for research Seismology

Traditionally, the ability to study seismic phenomena is dependant on both the available hardware and time for processing data needed to produce a research grade catalogue. Consequently, shortages in either of these resources constrain the scope of studies available to the research scientist. This is becoming especially challenging as networks become larger and more dense, and as the community moves towards Large-N networks and arrays. We will look at alternative solutions to address these resource constraints and open the scientist up to a broader field of study.

Ownership of equipment or waiting in a queue for loan pool assets are the two most common methods for acquiring the hardware necessary to conduct a scientific study. Further, once the data has been collected, a good deal of time is spent processing that data to produce a catalogue before the scientific inquiry can begin.   

There is now an alternative model for acquiring and processing data in seismology that shortens the time and effort necessary to produce a research grade catalogue. We will demonstrate how we can customize acquisition arrays to meet experimental goals and apply proven processing models and AI techniques to deliver a bespoke research grade catalogue at a fraction of the time and cost of traditional acquisition and processing methods. This removes several of the challenging aspects of running an experiment in order to enable researchers to get straight to their science and shortening the time to publication.