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Surficial mass wasting events are a hazard worldwide. Seismic and acoustic signals from these often remote processes, combined with other geophysical observations, can provide key information for monitoring and rapid response efforts and enhance our understanding of event dynamics. Here, we present seismoacoustic data and analyses for two very large ice–rock avalanches occurring on Iliamna Volcano, Alaska (USA), on 22 May 2016 and 21 June 2019. Iliamna is a glacier-mantled stratovolcano located in the Cook Inlet, ∼200 km from Anchorage, Alaska. The volcano experiences massive, quasi-annual slope failures due to glacial instabilities and hydrothermal alteration of volcanic rocks near its summit. The May 2016 and June 2019 avalanches were particularly large and generated energetic seismic and infrasound signals which were recorded at numerous stations at ranges from ∼9 to over 600 km. Both avalanches initiated in the same location near the head of Iliamna's east-facing Red Glacier, and their ∼8 km long runout shapes are nearly identical. This repeatability – which is rare for large and rapid mass movements – provides an excellent opportunity for comparison and validation of seismoacoustic source characteristics. For both events, we invert long-period (15–80 s) seismic signals to obtain a force-time representation of the source. We model the avalanche as a sliding block which exerts a spatially static point force on the Earth. We use this force-time function to derive constraints on avalanche acceleration, velocity, and directionality, which are compatible with satellite imagery and observed terrain features. Our inversion results suggest that the avalanches reached speeds exceeding 70 m s−1, consistent with numerical modeling from previous Iliamna studies. We lack sufficient local infrasound data to test an acoustic source model for these processes. However, the acoustic data suggest that infrasound from these avalanches is produced after the mass movement regime transitions from cohesive block-type failure to granular and turbulent flow – little to no infrasound is generated by the initial failure. At Iliamna, synthesis of advanced numerical flow models and more detailed ground observations combined with increased geophysical station coverage could yield significant gains in our understanding of these events.

Two catastrophic landslides occurred in quick succession on 13 and 16 May 2019, from the north face of Joffre Peak, Cerise Creek, southern Coast Mountains, British Columbia. With headscarps at 2560 m and 2690 m elevation, both began as rock avalanches, rapidly transforming into debris flows along middle Cerise Creek, and finally into debris floods affecting the fan. Beyond the fan margin, a flood surge on Cayoosh Creek reached bankfull and attenuated rapidly downstream; only fine sediment reached Duffey Lake. The toe of the main debris flow deposit reached 4 km from the headscarp, with a travel angle of 0.28, while the debris flood phase reached the fan margin 5.9 km downstream, with a travel angle of 0.22. Photogrammetry indicates the source volume of each event is 2–3 Mm3, with combined volume of 5 Mm3. Lidar differencing, used to assess deposit volume, yielded a similar total result, although error in the depth estimate introduced large volume error masking the expected increase due to dilation and entrainment. The average velocity of the rock avalanche-debris flow phases, from seismic analysis, was ~ 25–30 m/s, and the velocity of the 16 May debris flood on the upper fan, from super-elevation and boulder sizes, was 5–10 m/s. The volume of debris deposited on the fan was ~ 104 m3, 2 orders of magnitude less than the avalanche/debris flow phases. Progressive glacier retreat and permafrost degradation were likely the conditioning factors; precursor rockfall activity was noted at least ~6 months previous; thus, the mountain was primed to fail. The 13 May landslide was apparently triggered by rapid snowmelt, with debuttressing triggering the 16 May event.

Earthquake-induced landslide inventories can be generated using field observations but doing so can be challenging if the affected landscape is large or inaccessible after an earthquake. Remote sensing data can be used to help overcome these limitations. The effectiveness of remotely sensed data to produce landslide inventories, however, is dependent on a variety of factors, such as the extent of coverage, timing, and data quality, as well as environmental factors such as atmospheric interference (e.g., clouds, water vapor) or snow and vegetation cover. With these challenges in mind, we use a combination of field observations and remote sensing data from multispectral, light detection and ranging (lidar), and synthetic aperture radar (SAR) sensors to produce a ground failure inventory for the urban areas affected by the 2018 magnitude (Mw) 7.1 Anchorage, Alaska earthquake. The earthquake occurred during late November at high latitude (∼61°N), and the lack of sunlight, persistent cloud cover, and snow cover that occurred after the earthquake made remote mapping challenging for this event. Despite these challenges, 43 landslides were manually mapped and classified using a combination of the datasets mentioned previously. Using this manually compiled inventory, we investigate the individual performance and reliability of three remote sensing techniques in this environment not typically hospitable to remotely sensed mapping. We found that differencing pre- and post-event normalized difference vegetation index maps and lidar worked best for identifying soil slumps and rapid soil flows, but not as well for small soil slides, soil block slides and rock falls. The SAR-based methods did not work well for identifying any landslide types because of high noise levels likely related to snow. Some landslides, especially those that resulted in minor surface displacement, were identifiable only from the field observations. This work highlights the importance of the rapid collection of field observations and provides guidance for future mappers on which techniques, or combination of techniques, will be most effective at remotely mapping landslides in a subarctic and urban environment.

Surficial mass movements, such as debris avalanches, rock falls, lahars, pyroclastic flows, and outburst floods, are a dominant hazard at many volcanoes worldwide. Understanding these processes, cataloging their spatio-temporal occurrence, and detecting, tracking, and characterizing these events would advance the science of volcano monitoring and help mitigate hazards. Seismic and acoustic methods show promise for achieving these objectives: many surficial mass movements generate observable seismic and acoustic signals, and many volcanoes are already monitored. Significant progress has been made toward understanding, modeling, and extracting quantitative information from seismic and infrasonic signals generated by surficial mass movements. However, much work remains. In this paper, we review the state of the art of the topic, covering a range of scales and event types from individual rock falls to sector collapses. We consider a full variety of volcanic settings, from submarine to subaerial, shield volcano to stratovolcano. Finally, we discuss future directions toward operational seismo-acoustic monitoring of surficial mass movements at volcanoes.

Summary statistics derived from the frequency–area distribution (FAD) of inventories of triggered landslides allows for direct comparison of landslides triggered by one event (e.g. earthquake, rainstorm) with another. Such comparisons are vital to understand links between the landslide‐event and the environmental characteristics of the area affected. This could lead to methods for rapid estimation of landslide‐event magnitude, which in turn could lead to estimates of the total triggered landslide area. Previous studies proposed that the FAD of landslides follows an inverse power‐law, which provides the basis to model the size distribution of landslides and to estimate landslide‐event magnitude (mLS), which quantifies the severity of the event. In this study, we use a much larger collection of earthquake‐induced landslide (EQIL) inventories (n=45) than previous studies to show that size distributions are much more variable than previously assumed. We present an updated model and propose a method for estimating mLS and its uncertainty that better fits the observations and is more reproducible, robust, and consistent than existing methods. We validate our model by computing mLS for all of the inventories in our dataset and comparing that with the total landslide areas of the inventories. We show that our method is able to estimate the total landslide area of the events in this larger inventory dataset more successfully than the existing methods. © 2018 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.

Large earthquakes initiate chains of surface processes that last much longer than the brief moments of strong shaking. Most moderate‐ and large‐magnitude earthquakes trigger landslides, ranging from small failures in the soil cover to massive, devastating rock avalanches. Some landslides dam rivers and impound lakes, which can collapse days to centuries later, and flood mountain valleys for hundreds of kilometers downstream. Landslide deposits on slopes can remobilize during heavy rainfall and evolve into debris flows. Cracks and fractures can form and widen on mountain crests and flanks, promoting increased frequency of landslides that lasts for decades. More gradual impacts involve the flushing of excess debris downstream by rivers, which can generate bank erosion and floodplain accretion as well as channel avulsions that affect flooding frequency, settlements, ecosystems, and infrastructure. Ultimately, earthquake sequences and their geomorphic consequences alter mountain landscapes over both human and geologic time scales. Two recent events have attracted intense research into earthquake‐induced landslides and their consequences: the magnitude M 7.6 Chi‐Chi, Taiwan earthquake of 1999, and the M 7.9 Wenchuan, China earthquake of 2008. Using data and insights from these and several other earthquakes, we analyze how such events initiate processes that change mountain landscapes, highlight research gaps, and suggest pathways toward a more complete understanding of the seismic effects on the Earth's surface.

We present surface velocity maps derived from repeat terrestrial radar interferometry (TRI) measurements and use these time series to examine seasonal and diurnal dynamics of alpine glaciers at Mount Rainier, Washington. We show that the Nisqually and Emmons glaciers have small slope-parallel velocities near the summit (< 0.2 m day−1), high velocities over their upper and central regions (1.0–1.5 m day−1), and stagnant debris-covered regions near the terminus (< 0.05 m day−1). Velocity uncertainties are as low as &pm;0.02–0.08 m day−1. We document a large seasonal velocity decrease of 0.2–0.7 m day−1 (−25 to −50 %) from July to November for most of the Nisqually Glacier, excluding the icefall, suggesting significant seasonal subglacial water storage under most of the glacier. We did not detect diurnal variability above the noise level. Simple 2-D ice flow modeling using TRI velocities suggests that sliding accounts for 91 and 99 % of the July velocity field for the Emmons and Nisqually glaciers with possible ranges of 60–97 and 93–99.5 %, respectively, when considering model uncertainty. We validate our observations against recent in situ velocity measurements and examine the long-term evolution of Nisqually Glacier dynamics through comparisons with historical velocity data. This study shows that repeat TRI measurements with > 10 km range can be used to investigate spatial and temporal variability of alpine glacier dynamics over large areas, including hazardous and inaccessible areas.

Earthquake‐induced landslide (EQIL) inventories are essential tools to extend our knowledge of the relationship between earthquakes and the landslides they can trigger. Regrettably, such inventories are difficult to generate and therefore scarce, and the available ones differ in terms of their quality and level of completeness. Moreover, access to existing EQIL inventories is currently difficult because there is no centralized database. To address these issues, we compiled EQIL inventories from around the globe based on an extensive literature study. The database contains information on 363 landslide‐triggering earthquakes and includes 66 digital landslide inventories. To make these data openly available, we created a repository to host the digital inventories that we have permission to redistribute through the U.S. Geological Survey ScienceBase platform. It can grow over time as more authors contribute their inventories. We analyze the distribution of EQIL events by time period and location, more specifically breaking down the distribution by continent, country, and mountain region. Additionally, we analyze frequency distributions of EQIL characteristics, such as the approximate area affected by landslides, total number of landslides, maximum distance from fault rupture zone, and distance from epicenter when the fault plane location is unknown. For the available digital EQIL inventories, we examine the underlying characteristics of landslide size, topographic slope, roughness, local relief, distance to streams, peak ground acceleration, peak ground velocity, and Modified Mercalli Intensity. Also, we present an evaluation system to help users assess the suitability of the available inventories for different types of EQIL studies and model development.

We have detected over 150,000 small (M 3000 m) on the glacier-covered edifice and occur primarily in weeklong to monthlong swarms composed of simultaneous distinct families of events. Each family contains up to thousands of earthquakes repeating at regular intervals as often as every few minutes. Mixed polarity first motions, a linear relationship between recurrence interval and event size, and strong correlation between swarm activity and snowfall suggest the source is stick-slip basal sliding of glaciers. The sudden added weight of snow during winter storms triggers a temporary change from smooth aseismic sliding to seismic stick-slip sliding in locations where basal conditions are favorable to frictional instability. Coda wave interferometry shows that source locations migrate over time at glacial speeds, starting out fast and slowing down over time, indicating a sudden increase in sliding velocity triggers the transition to stick-slip sliding. We propose a hypothesis that this increase is caused by the redistribution of basal fluids rather than direct loading because of a 1–2 day lag between snow loading and earthquake activity. This behavior is specific to winter months because it requires the inefficient drainage of a distributed subglacial drainage system. Identification of the source of these frequent signals offers a view of basal glacier processes, discriminates against alarming volcanic noises, documents short-term effects of weather on the cryosphere, and has implications for repeating earthquakes, in general.

We demonstrate the value of utilizing broadband synthetic seismograms to assess regional seismically induced landslide hazard. Focusing on a case study of an M w 7.0 Seattle fault earthquake in Seattle, Washington, we computed broadband synthetic seismograms that account for rupture directivity and 3D basin amplification. We then adjusted the computed motions on a fine grid for 1D amplifications based on the site response of typical geologic profiles in Seattle and used these time‐series ground motions to trigger shallow landsliding using the Newmark method. The inclusion of these effects was critical in determining the extent of landsliding triggered. We found that for inertially triggered slope failures modeled by the Newmark method, the ground motions used to simulate landsliding must have broadband frequency content in order to capture the full slope displacement. We applied commonly used simpler methods based on ground‐motion prediction equations for the same scenario and found that they predicted far fewer landslides if only the mean values were used, but far more at the maximum range of the uncertainties, highlighting the danger of using just the mean values for such methods. Our results indicate that landsliding triggered by a large Seattle fault earthquake will be extensive and potentially devastating, causing direct losses and impeding recovery. The high impact of landsliding predicted by this simulation shows that this secondary effect of earthquakes should be studied with as much vigor as other earthquake effects. Online Material: High‐resolution maps of relative seismically induced landslide hazard for an M w 7.0 Seattle fault earthquake for dry and saturated soil conditions.

We observed several swarms of repeating low-frequency (1–5 Hz) seismic events during a 3 week period in May–June 2010, near the summit of Mount Rainier, Washington, USA, that likely were a result of stick–slip motion at the base of alpine glaciers. The dominant set of repeating events (‘multiplets’) featured >4000 individual events and did not exhibit daytime variations in recurrence interval or amplitude. Volcanoes and glaciers around the world are known to produce seismic signals with great variability in both frequency content and size. The low-frequency character and periodic recurrence of the Mount Rainier multiplets mimic long-period seismicity often seen at volcanoes, particularly during periods of unrest. However, their near-surface location, lack of common spectral peaks across the recording network, rapid attenuation of amplitudes with distance, and temporal correlation with weather systems all indicate that ice-related source mechanisms are the most likely explanation. We interpret the low-frequency character of these multiplets to be the result of trapping of seismic energy under glacial ice as it propagates through the highly heterogeneous and attenuating volcanic material. The Mount Rainier multiplet sequences underscore the difficulties in differentiating low-frequency signals due to glacial processes from those caused by volcanic processes on glacier-clad volcanoes.

A large rock avalanche occurred at 03:27:30 PDT, 6 August 2010, in the Mount Meager Volcanic Complex southwest British Columbia. The landslide initiated as a rock slide in Pleistocene rhyodacitic volcanic rock with the collapse of the secondary peak of Mount Meager. The detached rock mass impacted the volcano's weathered and saturated flanks, creating a visible seismic signature on nearby seismographs. Undrained loading of the sloping flank caused the immediate and extremely rapid evacuation of the entire flank with a strong horizontal force, as the rock slide transformed into a debris flow. The disintegrating mass travelled down Capricorn Creek at an average velocity of 64 m s−1, exhibiting dramatic super-elevation in bends to the intersection of Meager Creek, 7.8 km from the source. At Meager Creek the debris impacted the south side of Meager valley, causing a runup of 270 m above the valley floor and the deflection of the landslide debris both upstream (for 3.7 km) and downstream into the Lillooet River valley (for 4.9 km), where it blocked the Lillooet River river for a couple of hours, approximately 10 km from the landslide source. Deposition at the Capricorn–Meager confluence also dammed Meager Creek for about 19 h creating a lake 1.5 km long. The overtopping of the dam and the predicted outburst flood was the basis for a night time evacuation of 1500 residents in the town of Pemberton, 65 km downstream. High-resolution GeoEye satellite imagery obtained on 16 October 2010 was used to create a post-event digital elevation model. Comparing pre- and post-event topography we estimate the volume of the initial displaced mass from the flank of Mount Meager to be 48.5 × 106 m3, the height of the path (H) to be 2183 m and the total length of the path (L) to be 12.7 km. This yields H/L = 0.172 and a fahrböschung (travel angle) of 9.75°. The movement was recorded on seismographs in British Columbia and Washington State with the initial impact, the debris flow travelling through bends in Capricorn Creek, and the impact with Meager Creek are all evident on a number of seismograms. The landslide had a seismic trace equivalent to a M = 2.6 earthquake. Velocities and dynamics of the movement were simulated using DAN-W. The 2010 event is the third major landslide in the Capricorn Creek watershed since 1998 and the fifth large-scale mass flow in the Meager Creek watershed since 1930. No lives were lost in the event, but despite its relatively remote location direct costs of the 2010 landslide are estimated to be in the order of $10 M CAD.