Stephanie G. Prejean

Remotely Triggered Seismicity on the United States West Coast following the Mw 7.9 Denali Fault Earthquake

The Mw 7.9 Denali fault earthquake in central Alaska of 3 November 2002 triggered earthquakes across western North America at epicentral distances of up to at least 3660 km. We describe the spatial and temporal development of triggered activity in California and the Pacific Northwest, focusing on Mount Rainier, the Geysers geothermal field, the Long Valley caldera, and the Coso geothermal field. The onset of triggered seismicity at each of these areas began during the Love and Raleigh waves of the Mw 7.9 wave train, which had dominant periods of 15 to 40 sec, indicating that earthquakes were triggered locally by dynamic stress changes due to low-frequency surface wave arrivals. Swarms during the wave train continued for 4 min (Mount Rainier) to 40 min (the Geysers) after the surface wave arrivals and were characterized by spasmodic bursts of small (M 2.5) earthquakes. Dy- namic stresses within the surface wave train at the time of the first triggered earth- quakes ranged from 0.01 MPa (Coso) to 0.09 MPa (Mount Rainier). In addition to the swarms that began during the surface wave arrivals, Long Valley caldera and Mount Rainier experienced unusually large seismic swarms hours to days after the Denali fault earthquake. These swarms seem to represent a delayed response to the Denali fault earthquake. The occurrence of spatially and temporally distinct swarms of triggered seismicity at the same site suggests that earthquakes may be triggered by more than one physical process.

Observations of Earthquake Source Parameters at 2 km Depth in the Long Valley Caldera, Eastern California

To investigate seismic source parameter scaling and seismic efficiency in the Long Valley caldera, California, we measured source parameters for 41 earthquakes ( M 0.5 to M 5) recorded at 2 km depth in the Long Valley Exploratory Well. Borehole recordings provide a wide frequency bandwidth, typically 1 to 200–300 Hz, and greatly reduce seismic noise and path effects compared to surface recordings. We calculated source parameters in both the time and frequency domains for P and S waves. At frequencies above the corner frequency, spectra decay faster than ω3, indicating that attenuation plays an important role in shaping the spectra (path averaged Q p = 100–400, Q s = 200–800). Source parameters are corrected for attenuation and radiation pattern. Both static stress drops and apparent stresses range from approximately 0.01 to 30 MPa. Although static stress drops do not vary with seismic moment for these data, our analyses are consistent with apparent stress increasing with increasing moment. To estimate tectonic driving stress and seismic efficiencies in the region, we combined source parameter measurements with knowledge of the stress field and a Coulomb failure criterion to infer a driving stress of 40–70 MPa. Subsequent seismic efficiencies are consistent with McGarrs (1999) hypothesis of a maximum seismic efficiency of 6%.

Relations between seismicity and deformation during unrest in Long Valley Caldera, California, from 1995 through 1999

Abstract Unrest in Long Valley Caldera and the adjacent Sierra Nevada from 1995 through 2000 was dominated by three major episodes: (1) the March–April 1996 earthquake swarm in the east lobe of the south moat; (2) the July 1997–January 1998 caldera-wide unrest; and (3) a sequence of three M >5 earthquakes (9 June 1998, 13 July 1998, and 15 May 1999 UT) located in the Sierra Nevada block immediately south of the caldera. These three unrest episodes each had distinct characteristics with distinct implications for associated hazards. Seismicity developed as earthquake swarms for the 1996 and 1997–98 episodes, both of which were within the caldera. In contrast, the series of three M >5 earthquakes south of the caldera in 1998–99 each developed as a mainshock–aftershock sequence. Marginal deformation within the caldera associated with the 1996 swarm and the 1998–99 M >5 earthquakes is consistent with the cumulative seismic moments for the respective sequences. Deformation associated with the 1997–98 episode, however, was roughly five times larger than can be accounted for by the cumulative seismic moment of the associated earthquake swarm. We conclude that the 1997–98 episode was associated with mass transport (local intrusion of magma or magmatic brine) and that the associated earthquake swarm activity, which had a relatively high b -value of 1.2, was largely driven by the intrusive process. In contrast, the 1996 earthquake swarm and the 1998–99 M >5 mainshock–aftershock sequences, both with ‘normal’ b -values of ∼0.9, represent brittle relaxation to previously accumulated stresses associated with little or no mass transport. These relations emphasize the importance of simultaneous, real-time monitoring of both seismicity and deformation as a basis for judging whether an evolving unrest episode has the potential for culminating in a volcanic eruption.

Eruption of Alaska volcano breaks historic pattern

In the late morning of 12 July 2008, the Alaska Volcano Observatory (AVO) received an unexpected call from the U.S. Coast Guard, reporting an explosive volcanic eruption in the central Aleutians in the vicinity of Okmok volcano, a relatively young (∼2000-year-old) caldera. The Coast Guard had received an emergency call requesting assistance from a family living at a cattle ranch on the flanks of the volcano, who reported loud “thunder,” lightning, and noontime darkness due to ashfall. AVO staff immediately confirmed the report by observing a strong eruption signal recorded on the Okmok seismic network and the presence of a large dark ash cloud above Okmok in satellite imagery. Within 5 minutes of the call, AVO declared the volcano at aviation code red, signifying that a highly explosive, ash-rich eruption was under way.

Fluid-faulting interactions: Fracture-mesh and fault-valve behavior in the February 2014 Mammoth Mountain, California, earthquake swarm

Faulting and fluid transport in the subsurface are highly coupled processes, which may manifest seismically as earthquake swarms. A swarm in February 2014 beneath densely monitored Mammoth Mountain, California, provides an opportunity to witness these interactions in high resolution. Toward this goal, we employ massive waveform-correlation-based event detection and relative relocation, which quadruples the swarm catalog to more than 6000 earthquakes and produces high-precision locations even for very small events. The swarms main seismic zone forms a distributed fracture mesh, with individual faults activated in short earthquake bursts. The largest event of the sequence, M 3.1, apparently acted as a fault valve and was followed by a distinct wave of earthquakes propagating ~1 km westward from the updip edge of rupture, 1–2 h later. Late in the swarm, multiple small, shallower subsidiary faults activated with pronounced hypocenter migration, suggesting that a broader fluid pressure pulse propagated through the subsurface.

Volcanic plume height measured by seismic waves based on a mechanical model

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116, B01306, doi:10.1029/2010JB007620, 2011 Volcanic plume height measured by seismic waves based on a mechanical model Stephanie G. Prejean 1 and Emily E. Brodsky 2 Received 4 April 2010; revised 3 November 2010; accepted 23 November 2010; published 26 January 2011. [ 1 ] In August 2008 an unmonitored, largely unstudied Aleutian volcano, Kasatochi, erupted catastrophically. Here we use seismic data to infer the height of large eruptive columns such as those of Kasatochi based on a combination of existing fluid and solid mechanical models. In so doing, we propose a connection between a common, observable, short‐period seismic wave amplitude to the physics of an eruptive column. To construct a combined model, we estimate the mass ejection rate of material from the vent on the basis of the plume height, assuming that the height is controlled by thermal buoyancy for a continuous plume. Using the estimated mass ejection rate, we then derive the equivalent vertical force on the Earth through a momentum balance. Finally, we calculate the far‐field surface waves resulting from the vertical force. The model performs well for recent eruptions of Kasatochi and Augustine volcanoes if v, the velocity of material exiting the vent, is 120–230 m s −1 . The consistency between the seismically inferred and measured plume heights indicates that in these cases the far‐field ∼1 s seismic energy radiated by fluctuating flow in the volcanic jet during the eruption is a useful indicator of overall mass ejection rates. Thus, use of the model holds promise for characterizing eruptions and evaluating ash hazards to aircraft in real time on the basis of far‐field short‐period seismic data. This study emphasizes the need for better measurements of eruptive plume heights and a more detailed understanding of the full spectrum of seismic energy radiated coeruptively. Citation: Prejean, S. G., and E. E. Brodsky (2011), Volcanic plume height measured by seismic waves based on a mechanical model, J. Geophys. Res., 116, B01306, doi:10.1029/2010JB007620. 1. Introduction and Motivation [ 2 ] Empirical studies have suggested that the amplitude of high‐frequency or broadband seismic waves radiated during large volcanic eruptions generally scales with the height of an eruption column [McNutt, 1994a]. However, a direct calculation of the expected seismic wave amplitude based on physical models has not yet been successful. Connecting commonly observable data such as seismic wave amplitudes to a model of the flow in the eruptive jet would provide a new tool to test and improve our understanding of eruptive physics. For instance, small‐scale turbulence is thought to play a major role in the entrainment of hot particles and gases and hence the buoyancy of eruptive columns, yet there are few measurements of the strength or distribution of small‐scale features in real eruptive columns [Andrews and Gardner, 2009]. Using seismic data to provide observational constraints on eruption column flow processes is particularly attractive as seismic data are often available even in remote U.S. Geological Survey, Alaska Volcano Observatory, Anchorage, Alaska, USA. Department of Earth and Planetary Sciences, University of California, Santa Cruz, California, USA. Copyright 2011 by the American Geophysical Union. 0148‐0227/11/2010JB007620 settings. Thus use of the seismic database would greatly increase the number and type of eruptions amenable to study. [ 3 ] Pragmatically, a physical model connecting plume height and seismic data would allow the use of seismology as a remote sensing technology to infer volcanic plume height. It would be a particularly useful tool for exploring eruption dynamics in remote environments where direct observation is not possible, such as the volcanoes of the northern Pacific Ocean. Although many volcano observa- tories worldwide are gradually replacing short‐period seismometers with broadband seismometers, we still largely rely on short‐period instruments for forecasting, monitoring, and analyzing eruptions. These realities motivate the development of the model described below. [ 4 ] In this study, we build on previous work to develop a physical model for the expected amplitude of seismic waves from an eruption that generates a plume of a given height. As a cautionary note, we explore the potential errors in this formulation and describe situations where the model is not applicable, such as small eruptions or eruptions where most mass ejected is not entrained in the plume. After reviewing previous work on coeruptive seismology and the char- acteristics of the 2008 Kasatochi and 2006 Augustine eruptions, we use the connection between plume height and B01306 1 of 13

Triggered Deformation and Seismic Activity under Mammoth Mountain in Long Valley Caldera by the 3 November 2002 Mw 7.9 Denali Fault Earthquake

The 3 November 2002 Mw 7.9 Denali fault earthquake triggered defor- mational offsets and microseismicity under Mammoth Mountain (MM) on the rim of Long Valley caldera, California, some 3460 km from the earthquake. Such strain offsets and microseismicity were not recorded at other borehole strain sites along the San Andreas fault system in California. The Long Valley offsets were recorded on borehole strainmeters at three sites around the western part of the caldera that includes Mammoth Mountain—a young volcano on the southwestern rim of the caldera. The largest recorded strain offsets were 0.1 microstrain at PO on the west side of MM, 0.05 microstrain at MX to the southeast of MM, and 0.025 microstrain at BS to the northeast of MM with negative strain extensional. High sample rate strain data show initial triggering of the offsets began at 22:30 UTC during the arrival of the first Rayleigh waves from the Alaskan earthquake with peak-to-peak dynamic strain amplitudes of about 2 microstrain corresponding to a stress amplitude of about 0.06 MPa. The strain offsets grew to their final values in the next 10 min. The associated triggered seismicity occurred beneath the south flank of MM and also began at 22:30 UTC and died away over the next 15 min. This relatively weak seismicity burst included some 60 small events with magnitude all less than M 1. While poorly constrained, these strain observations are consistent with triggered slip and intrusive opening on a north-striking normal fault centered at a depth of 8 km with a moment of 10 16 N m, or the equivalent of a M 4.3 earthquake. The cumulative seismic moment for the associated seismicity burst was more than three orders of magnitude smaller. These observations and this model resemble those for the triggered deformation and slip that occurred beneath the north side of MM following the 16 October 1999 M 7.1 Hector Mine, California, earthquake. However, in this case, we see little post-event slip decay reflected in the strain data after the Rayleigh-wave arrivals from the Denali fault earthquake and onset of triggered seismicity did not lag the triggered defor- mation by 20 min. These observations are also distinctly different from the more widespread and energetic seismicity and deformation triggered by the 1992 M 7.3 Landers earthquake in the Long Valley caldera. Thus, each of the three instances of remotely triggered unrest in Long Valley caldera recorded to date differ. In each case, however, the deformation moment inferred from the strain meter data was more than an order of magnitude larger than the cumulative moment for the associated triggered seismicity.

Alaska's Pavlof Volcano Ends 11-Year Repose

After an 11-year period of repose, Pavlof volcano on the Alaska Peninsula (Figure 1) began an episode of Strombolian eruption lasting 31 days, from 14 August to 13 September 2007. The eruption began abruptly on 14 August after a minor increase in seismicity the previous day. Nearly continuous lava fountaining, explosions, and lahars caused by minor disruption of the ice and snow cover on the volcano characterized the eruption. The eruption also produced diffuse ash plumes that reached 5–6 kilometers above sea level, but the plumes were too small and did not extend high enough to affect local or regional air travel. Melting of snow and ice on the upper part of the edifice by hot debris avalanches and lava resulted in numerous lahars that entered the sea and inundated a 2×106 square meter area on the volcanos southern slope.

Shaking up volcanoes

Data from a Japanese seismic network elucidate how large earthquakes may disrupt volcanic systems. [Also see Report by Brenguier et al.] Most volcanic eruptions that occur shortly after a large distant earthquake do so by random chance. A few compelling cases for earthquake-triggered eruptions exist, particularly within 200 km of the earthquake, but this phenomenon is rare in part because volcanoes must be poised to erupt in order to be triggered by an earthquake (1). Large earthquakes often perturb volcanoes in more subtle ways by triggering small earthquakes and changes in spring discharge and groundwater levels (1, 2). On page 80 of this issue, Brenguier et al. (3) provide fresh insight into the interaction of large earthquakes and volcanoes by documenting a temporary change in seismic velocity beneath volcanoes in Honshu, Japan, after the devastating Tohoku-Oki earthquake in 2011.

Alaska Volcano Observatory Alert and Forecasting Timeliness: 1989–2017

The Alaska Volcano Observatory (AVO) monitors volcanoes in Alaska and issues notifications and warnings of volcanic unrest and eruption. We evaluate the timeliness and accuracy of eruption forecasts for 53 eruptions at 20 volcanoes, beginning with Mount Redoubt’s 1989–1990 eruption. Successful forecasts are defined as those where AVO issued a formal warning before eruption onset. These warning notifications are now part of AVO’s Aviation Color Code and Volcanic Alert Level. This analysis considers only the start of an eruption, although many eruptions have multiple phases of activity. For the 21 eruptions at volcanoes with functioning local seismic networks, AVO has high forecasting success at volcanoes with: >15 yr repose intervals and magmatic eruptions (4 out of 4, 100%); or larger eruptions (Volcanic Explosivity Index (VEI) 3 or greater; 6 out of 10, 60%). AVO successfully forecast all four monitored, longer-repose period, VEI 3+ eruptions: Redoubt 1989-1990 and 2009, Spurr 1992, and Augustine 2005–2006. For volcanoes with functioning seismic monitoring networks, success rates are lower for: volcanoes with shorter repose periods (3 out of 16, 19%); more mafic compositions (3 out of 18, 17%); or smaller eruption size (VEI 2 or less, 1 out of 11, 9%). These eruptions (Okmok, Pavlof, Veniaminof, and Shishaldin) often lack detectable precursory signals. For 32 eruptions at volcanoes without functioning local seismic networks, the forecasting success rate is much lower (2, 6%; Kasatochi 2008 and Shishaldin 2014). For remote volcanoes where the main hazard is to aviation, rapid detection is a goal in the absence of in situ monitoring. Eruption detection has improved in recent years, shown by a decrease in the time between eruption onset and notification. Even limited seismic monitoring can detect precursory activity at volcanoes with certain characteristics (intermediate composition, longer repose times, larger eruptions), but difficulty persists in detecting subtle precursory activity at frequently active volcanoes with more mafic compositions. This suggests that volcano-specific characteristics should be considered when designing monitoring programs and evaluating forecasting success. More proximally-located sensors and data types are likely needed to forecast eruptive activity at frequently-active, more mafic volcanoes that generally produce smaller eruptions.