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Abstract The Nevada Seismological Laboratory (NSL) at the University of Nevada, Reno, installed eight temporary seismic stations following the 15 May 2020 Mww 6.5 Monte Cristo Range earthquake. The mainshock and resulting aftershock sequence occurred in an unpopulated and sparsely instrumented region of the Mina deflection in the central Walker Lane, approximately 55 km west of Tonopah, Nevada. The temporary stations supplement NSL’s permanent seismic network, providing azimuthal coverage and near-field recording of the aftershock sequence beginning 1–3 days after the mainshock. We expect the deployment to remain in the field until May 2021. NSL initially attempted to acquire the Monte Cristo Range deployment data in real time via cellular telemetry; however, unreliable cellular coverage forced NSL to convert to microwave telemetry within the first week of the sequence to achieve continuous real-time acquisition. Through 31 August 2020, the temporary deployment has captured near-field records of three aftershocks ML≥5 and 25 ML 4–4.9 events. Here, we present details regarding the Monte Cristo Range deployment, instrumentation, and waveform availability. We combine this information with waveform availability and data access details from NSL’s permanent seismic network and partner regional seismic networks to create a comprehensive summary of Monte Cristo Range sequence data. NSL’s Monte Cristo Range temporary and permanent station waveform data are available in near-real time via the Incorporated Research Institutions for Seismology Data Management Center. Derived earthquake products, including NSL’s earthquake catalog and phase picks, are available via the Advanced National Seismic System Comprehensive Earthquake Catalog. The temporary deployment improved catalog completeness and location quality for the Monte Cristo Range sequence. We expect these data to be useful for continued study of the Monte Cristo Range sequence and constraining crustal and seismogenic properties of the Mina deflection and central Walker Lane. 

Abstract Magmatic systems are composed of melt accumulations and crystal mush that evolve with melt transport, contributing to igneous processes, volcano dynamics, and eruption triggering. Geophysical studies of active volcanoes have revealed details of shallow-level melt reservoirs, but little is known about fine-scale melt distribution at deeper levels dominated by crystal mush. Here, we present new seismic reflection images from Axial Seamount, northeastern Pacific Ocean, revealing a 3–5-km-wide conduit of vertically stacked melt lenses, with near-regular spacing of 300–450 m extending into the inferred mush zone of the mid-to-lower crust. This column of lenses underlies the shallowest melt-rich portion of the upper-crustal magma reservoir, where three dike intrusion and eruption events initiated. The pipe-like zone is similar in geometry and depth extent to the volcano inflation source modeled from geodetic records, and we infer that melt ascent by porous flow focused within the melt lens conduit led to the inflation-triggered eruptions. The multiple near-horizontal lenses are interpreted as melt-rich layers formed via mush compaction, an interpretation supported by one-dimensional numerical models of porous flow in a viscoelastic matrix. 

Corroboration of Geostationary Operational Environmental Satellite-17 (GOES-17) wildland fire detection capabilities occurred during the 24 October 2019 (evening of 23 October LST) ignition of the Kincade Fire in northern California. Post-analysis of remote sensing data compared to observations by the ALERTWildfire fire surveillance video system suggests that the emerging Kincade Fire hotspot was visually evident in GOES17 shortwave infrared imagery 52 s after the initial near-infrared heat source detected by the ground-based camera network. GOES-17 Advanced Baseline Imager Fire Detection Characteristic algorithms registered the fire 5 min after ignition. These observations represent the first documented comparative dataset between fire initiation and satellite detection, and thus provide context for GOES-16/17 wildland fire detections.