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When volcanic unrest occurs, the scientific community can advance fundamental understanding of volcanic systems, but only with coordination before, during, and after the event across academic and governmental agencies. To develop a coordinated response plan, the Community Network for Volcanic Eruption Response (CONVERSE) orchestrated a scenario exercise centered around a hypothetical volcanic crisis in Arizona’s San Francisco Volcanic Field (SFVF). The exercise ran virtually from February 4 to March 4, 2022. Over 60 scientists from both academic and governmental spheres participated. The scenario exercise was assessed for its effectiveness in supporting collaborative production of knowledge, catalyzing transdisciplinary collaboration, supporting researcher confidence, and fostering a culture of inclusion within the volcanology community. This identified a need to support early career researchers through community and allyship. Overall, the 2022 CONVERSE exercise demonstrated how a fully remote, extended scenario can be authentically implemented and help broaden participation within the volcano science community. 

We present methods for autonomous collaborative surveying of volcanic CO 2 emissions using aerial robots. CO 2 is a useful predictor of volcanic eruptions and an influential greenhouse gas. However, current CO 2 mapping methods are hazardous and inefficient, as a result, only a small fraction of CO 2 emitting volcanoes have been surveyed. We develop algorithms and a platform to measure volcanic CO 2 emissions. The Dragonfly Unpiloted Aerial Vehicle (UAV) platform is capable of long-duration CO 2 collection flights in harsh environments. We implement two survey algorithms on teams of Dragonfly robots and demonstrate that they effectively map gas emissions and locate the highest gas concentrations. Our experiments culminate in a successful field test of collaborative rasterization and gradient descent algorithms in a challenging real-world environment at the edge of the Valles Caldera supervolcano. Both algorithms treat multiple flocking UAVs as a distributed flexible instrument. Simultaneous sensing in multiple UAVs gives scientists greater confidence in estimates of gas concentrations and the locations of sources of those emissions. These methods are also applicable to a range of other airborne concentration mapping tasks, such as pipeline leak detection and contaminant localization.