Development of an interactive Cloud-based seismic network modelling application on a common Geophysical Processing Toolkit platform

Detecting and locating earthquakes relies on seismic events being recorded by a number of deployed seismometers. To detect earthquakes effectively and accurately, seismologists must design and install a network of seismometers that can capture small seismic events in the sub-surface. A major challenge when deploying an array of seismometers (seismic array) is predicting the smallest earthquake that could be detected and located by that network. Varying the spacing and number of seismometers dramatically affects network sensitivity and location precision and is very important when researchers are investigating small-magnitude local earthquakes. For cost reasons, it is important to optimise network design before deploying seismometers in the field. In doing so, seismologists must accurately account for parameters such as station locations, site-specific noise levels, earthquake source parameters, seismic velocity and attenuation in the wave propagation medium, signal-to-noise ratios, and the minimum number of stations required to compute high-quality locations. AuScope AVRE Engage Program team has worked with researchers from the seismology team at the University of Melbourne to better understand their solution for optimising seismic array design to date: an analytical method called SENSI that has been developed by Tramelli et al. (2013) to design seismic networks, including the GipNet array deployed to monitor seismicity in the Gippsland region in Victoria, Australia. The underlying physics and mechanics of the method are straightforward, and when applied sensibly, can be used as a basis for the design of seismic networks anywhere in the world. Our engineers have built an application leveraging a previously developed Geophysical Processing Toolkit (GPT) as an application platform and harnessed the scalability of a Cloud environment provided by the EASI Hub, which minimised the overall development time. The GPT application platform provided the groundwork for a web-based application interface and enabled interactive visualisations to facilitate human-computer interaction and experimentation.