Seismic network modelling and design in an interactive web-based environment

To detect and locate earthquakes effectively and accurately, seismologists must design and install a network of seismometers that can capture small seismic signals originating in the sub-surface. A major challenge when deploying an array of seismometers is predicting the smallest earthquake that could be detected and located by that network. Varying the number of seismometers and the geometry of the network significantly affect network sensitivity to detectable minimum magnitudes and location precision, both of which are important for meeting seismic monitoring targets. For seismic monitoring to be cost effective, it is necessary 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 seismic 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 collaborated with researchers from the seismology team at the University of Melbourne on optimising seismic array design using an analytical method called SENSI. This approach allows users to design and test seismic networks, including the GipNet seismic 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, it can be used as a basis for the design of seismic networks anywhere in the world. We 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 open-access application interface and enabled interactive visualisations to facilitate human-computer interaction and experimentation.