# Creep Bursts on the Åknes Landslide, Norway: Detection, Characterization and Possible Mechanisms

Andreas Aspaas, Pascal Lacroix, Clara Sena, Lene Kristensen, Nadège Langet,

François Renard

► To cite this version:

Andreas Aspaas, Pascal Lacroix, Clara Sena, Lene Kristensen, Nadège Langet, et al. Creep Bursts on the Åknes Landslide, Norway: Detection, Characterization and Possible Mechanisms. Journal of Geophysical Research: Earth Surface, 2024, 129 (12), e2024JF007733 [23 p.]. 10.1029/2024JF007733. hal-04983487

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# JGR Earth Surface

## RESEARCH ARTICLE

10.1029/2024JF007733

**Key Points:**

* We detected creep bursts on the shear zone of the Åknes landslide that accommodated 11% of the total displacement

* The largest creep bursts are preceded by increased water pressure and microseismicity in the shear zone

* Most of the creep bursts occur randomly over the year without correlation with other environmental parameters

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

A. Aspaas,
a.g.aspaas@geo.uio.no

**Citation:**

Aspaas, A., Lacroix, P., Sena, C., Kristensen, L., Langet, N., & Renard, F. (2024). Creep bursts on the Åknes landslide, Norway: Detection, characterization and possible mechanisms. *Journal of Geophysical Research: Earth Surface, 129*, e2024JF007733. https://doi.org/10.1029/2024JF007733

Received 13 MAR 2024

Accepted 11 NOV 2024

© 2024. The Author(s).
This is an open access article under the
terms of the Creative Commons
Attribution License, which permits use,
distribution and reproduction in any
medium, provided the original work is
properly cited.

<figure><img src="image_2.png" /></figure>

## Creep Bursts on the Åknes Landslide, Norway: Detection, Characterization and Possible Mechanisms

Andreas Aspaas¹,² ID, Pascal Lacroix³ ID, Clara Sena⁴, Lene Kristensen², Nadège Langet⁵ ID, and François Renard¹,³ ID

¹The Njord Centre, Departments of Geosciences and Physics, University of Oslo, Oslo, Norway, ²Section for Landslides and Avalanches, Norwegian Resources and Energy Directorate, Trondheim, Norway, ³ISTerre, University Grenoble Alpes, Grenoble INP, University Savoie Mont Blanc, CNRS, IRD, University Gustave Eiffel, Grenoble, France, ⁴Department of Geosciences, University of Oslo, Oslo, Norway, ⁵Department of Applied Seismology, NORSAR, Kjeller, Norway

**Abstract** Creeping landslides may fail catastrophically, posing significant threats to infrastructure and lives. Landslides weaken over time through rock mass damage processes that may occur by steady-state creep or transient accelerations of slip, called creep bursts. Creep bursts may control landslide stability by inducing short-term damage and strain localization. This study focuses on the Åknes landslide in Norway, which moves up to 6 cm per year and could potentially trigger a large tsunami in the fjord below. An 11-year data set is compiled and analyzed, including kinematic, seismic, and hydrogeological data acquired at the landslide surface and in a series of boreholes. An annual average of two creep bursts with millimeter amplitude has been recorded within the shear zone in each borehole, accounting for approximately 11% of the total displacement. Creep bursts detected simultaneously in multiple boreholes are preceded by increased seismic activity and rising water pressure. However, most creep bursts are observed in only one or a few boreholes. These bursts often happen during seasonal high and low groundwater levels in autumn and spring, respectively, correlating with local peaks in water pressure. No such correlation is observed during summer. We propose that creep bursts can have different causes and hypothesize that rock degradation leads to some creep bursts independent of water pressure variations. In contrast, the largest creep bursts are correlated with variations in absolute water pressure or gradients of water pressure within the shear zone. Our findings emphasize the complexity of a dense data set requiring multiple mechanisms to explain creep burst dynamics.

**Plain Language Summary** Slow-moving landslides, advancing from millimeters to meters per year, can have strong destruction potential if they fail catastrophically. A landslide sliding plane weakens over time by rock crushing and fracturing. One of these processes is short-lived movement accelerations that we call creep bursts. Creep bursts may control landslide stability by inducing short-term damage and the development of a plane that allows the landslide to slide, which may lead to catastrophic failure. This study focuses on the Åknes landslide in Norway, which moves at 6 cm per year and could potentially trigger a great tsunami in the fjord below. Creep bursts with millimeter movements are detected at the landslide's sliding plane. Some creep bursts occur without water pressure changes at the initiation of the motion. These bursts are likely caused by the gradual crushing of the rock mass, independent of water level fluctuations. Conversely, the largest creep bursts are correlated with seasonal variations in groundwater level within the landslide. These larger bursts may be related to an easing of sliding by the absolute or the water pressure gradient. Our data set provides quantitative insights into transient slip accelerations, and these accelerations may be crucial in driving a large-scale failure of the Åknes landslide.

## 1. Introduction

Landslides may creep for thousands of years. They can move steadily or accelerate seasonally or transiently and potentially fail catastrophically (e.g., Lacroix et al., 2020). The mechanisms of landslide deformation include slow degradation of rock mechanical properties by mechanisms such as microcracking and subcritical crack growth (e.g., Atkinson, 1984; Dille et al., 2019; Scholz, 1972) that induce bulk damage in the rock volume (e.g., Brideau et al., 2009), and sliding along a shear plane separating the landslide from the bedrock (e.g., Agliardi et al., 2020). Sliding can be influenced by external stress variations, the geometry and distribution of displacement, and the resistance to slip, as proposed by Wesson (1988). Several mechanisms have been suggested to explain landslide failure, including strain localization within a shear zone with rate-strengthening or rate-
weakening properties (Kilburn & Petley, 2003; Viesca & Rice, 2012), porosity modulations causing dilation and contraction of the shear zone (Agliardi et al., 2020; Iverson, 2005), and viscosity bifurcation or changes in the shear modulus of clay layers (Mainsant et al., 2012). Sliding occurs through either aseismic deformation at a constant rate, stick-slip processes, or transient accelerations, also called creep bursts that last from minutes to days with slips in the millimeter range (Finnegan et al., 2022). Several studies on various landslides have evidenced these different sliding mechanisms (Finnegan et al., 2022; Lacroix et al., 2022; Poli, 2017; Yamada et al., 2016). For instance, Finnegan et al. (2022), based on data acquired on two extensometers installed 100 m apart, showed that the Oak Ridge landslide displacement occurs by short stick-slip events of mm-scale slip over asperities less than 100 m in size. On other landslides, seismic repeaters were interpreted by the existence of stick-slip events on larger patches (Lacroix et al., 2022; Poli, 2017; Yamada et al., 2016).

In subduction zones, where these two mechanisms were also detected, the fault surface is modeled by seismogenic patches surrounded by creeping segments with a strong interplay between the two (e.g., Jolivet et al., 2023; Lay & Nishenko, 2022). This model is widely used in strike-slip faults (Templeton et al., 2008) and volcanoes (Gudmundsson, 2016) despite the difficulty of accessing the active shear zones that are typically deeper than 1 km. However, landslides often reveal shear zones in the upper 100 m of the Earth's crust, where direct measurements can be made in boreholes, making them ideal natural objects to study slip processes.

It has been shown that landslide motion is mostly aseismic on landslides where both local seismic arrays and displacement measurements exist simultaneously (Gomberg et al., 2011; Lacroix & Helmstetter, 2011). For instance, on the Séchilienne landslide, almost 99.9% of the slip is released aseismically (Lacroix & Helmstetter, 2011). Therefore, this aseismic displacement is an important component of the mechanics of landslide slip, and the relationships between steady and transient creep may control catastrophic failure. Furthermore, our understanding of the mechanisms of landslide slip is limited by two aspects: (a) stick-slip events have not yet been evidenced simultaneously on displacement data and seismic data in landslides; (b) transient creeps are most of the time observed on surface displacements, and very rarely directly on the sliding surface (Ruggeri et al., 2020). Therefore, the following questions about the origin of stick-slip events and transient slip events, which we call creep bursts in the present study, could be raised: Is seismicity linked to creep bursts? Is seismicity produced at the creeping surface or in the rock volume? What are the controlling factors of creep bursts? What is the link between the slip dynamics along a shear plane at the base of the landslide and the resulting surface motion?

To better quantify the sliding mechanisms of the landslide, the present study exploits the extensive instrumentation and long-term time series data of the Åknes landslide in Norway. Multi-method measurements of displacement, seismicity, and groundwater pressure were acquired from 2012 to 2023. This landslide is instrumented at its surface with kinematic and seismic networks, and at depth with seven deep boreholes equipped with lines of inclinometers above and penetrating through and below the sliding basal plane, water pressure sensors, and one string of geophones. To detect and characterize creep bursts and establish their causal relationships, the present study investigates the unique and dense data sets acquired on the Åknes landslide.

## 2. Geological Setting

The Åknes landslide, located on the west coast of Norway along the Synnyls fjord, was discovered by local inhabitants in 1964 (NGI, 1996). In the subsequent years, a few manual surface displacement measurement campaigns were conducted, followed by the installation of continuously recording surface extensometers. The landslide has been actively monitored since its discovery because of its potential to fail, which would cause a devastating tsunami that would impact nearby towns and the Geiranger fjord, a world heritage site selected by UNESCO in 2005.

The Åknes landslide is developing on a southeast-facing mountain with an average slope of 30–35° (Figure 1). An outcropping back scarp located at 900 m above sea level (masl) is approximately 20 m tall and 30 m wide to the west, and progressively decreases to about 5 m tall to 4 m wide to the east. It forms the upper boundary of the landslide. The western and eastern borders include a sub-vertical strike-slip fault and shallow west-dipping faults. At two elevations along the slope, low-angle sliding surfaces outcrop and are parallel to the local foliation of the geological formations, with the lowermost sliding surface forming the lower boundary of the landslide at 200 masl. The landslide unstable area covers a surface area of 0.56 km² and contains an estimated volume of 54 million m³ of rock (Ganerød et al., 2008; Pless et al., 2021).
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Map of the Åknes landslide with morphological structures. Symbols indicate instrumentation where data from global navigation satellite systems (GPS, yellow arrows) and differential monitoring system boreholes (KH, white arrows) are displayed as mean velocity in mm/yr for the period 01/01/2020 to 28/03/2023. The length and width of arrows are proportional to the sliding velocity, which is also given as numbers in mm/yr in parentheses on the figure. A circle is displayed if the instrument is nearly stable with a velocity of less than 1 mm/yr. The sum of the shear zone sliding velocities is indicated for the boreholes where two shear zones have been identified. The white lines are contours of equal elevations spaced every 100 m. The inset map shows the landslide location in Norway with a red marker.</figcaption></figure>

The landslide is located in the Western Gneiss Complex, consisting of orthogneiss ranging from mafic to granitic composition, but dominantly with granodioritic composition, with some places containing migmatites. The protolith age is between 1700 and 1,600 Ma, and the rocks were metamorphosed during the Caledonian orography between 425 and 400 Ma (Corfu et al., 2014; Tveten et al., 1998). Core logs have revealed three lithological units, listed here as volume percentages: white to light gray granitic gneiss and pegmatite (62 vol.%), dark gray dioritic gneiss (23 vol.%), and black biotitic gneiss (15 vol.%). Locally, the gneiss contains biotite schist layers up to 20 cm thick (Ganerød, 2008; Ganerød et al., 2007; Langeland & Holmøy, 2018, 2019a, 2019b, 2019c; Sena & Braathen, 2021). The foliation is well-developed and folded around tight isoclinal folds plunging east-south-east (Braathen et al., 2004; Jaboyedoff et al., 2011). On outcrops, the dominating orientation of the foliation is parallel to the slope and dips at 30° on average. The dip angle of the foliation ranges from 27° to 34° in the core logs from the boreholes, which is inferred to be the range of different fold phases (Jaboyedoff et al., 2011; Kveldsvik et al., 2006). The sliding plane of the landslide and its back scarp follow the foliation and folds (Jaboyedoff et al., 2011). Drill cores show that the shear zones are clay-rich and located mainly in biotite-rich gneiss, but some also consist of a mix of biotite-rich and granitic gneiss (Ganerød, 2008, 2013; Langeland & Holmøy, 2018, 2019a, 2019b, 2019c). Investigations of core logs from the latest drilling campaign revealed no correlation between
<caption>**Table 1**</caption>

Table of Instruments Installed on the Åknes Landslide by Type, Product Name, Number of Devices (and Their Names for the Boreholes), Installation Date and Operation Period, Acquisition Frequency of the Available Data, and Measurement Resolution Provided by the Manufacturer

<table><thead><tr><th>Type</th><th>Product name</th><th>Number of instruments</th><th>Installation date(s)</th><th>Analyzed period</th><th>Sampling frequency</th><th>Measurement resolution</th></tr></thead><tbody><tr><td>GNSS</td><td>Trimble Net-R9 receivers with Trimble Zephyr 2 or 3 antennas</td><td>10</td><td>27/03/2007-03/10/2020</td><td>~12 years</td><td>1 s</td><td>&lt;1 mm</td></tr><tr><td>Borehole inclinometers</td><td>CSG DMS 2D Rock</td><td rowspan="2">3 (KH-01-12, KH-02-06, and KH-03-06)</td><td>21/10/2007-22/08/2013</td><td rowspan="2">~11 years</td><td rowspan="2">1 hr</td><td rowspan="2">&lt;0.3 mm<br>138 Pa</td></tr><tr><td>Borehole water pressure sensor</td><td></td><td></td></tr><tr><td>Temperature sensors</td><td></td><td></td><td></td><td></td><td></td><td>0.1°C</td></tr><tr><td>Borehole inclinometers</td><td>CSG DMS 3D Multipacker</td><td rowspan="2">4 (KH-02-17, KH-01-18, KH-01-17, and KH-02-18)</td><td>29/08/2018-30/10/2019</td><td rowspan="2">~3 years</td><td rowspan="2">1 hr</td><td rowspan="2">&lt;0.03 mm<br>138 Pa</td></tr><tr><td>Borehole water pressure sensors and air packers</td><td></td><td></td></tr><tr><td>Temperature sensors</td><td></td><td></td><td></td><td></td><td></td><td>0.1°C</td></tr><tr><td>Surface geophones</td><td>OYO GeoSpace GS-11D with Geometrics Geode digitizer</td><td>8</td><td>01/02/2007</td><td>~12 years</td><td>1,000 Hz [4.5 Hz resonance frequency]</td><td></td></tr><tr><td>Borehole geophones</td><td>OYO GeoSpace GS-14-L3 with Ref Tek 130 S01 digitizer</td><td>8 in one borehole</td><td>01/11/2017</td><td>~2 years</td><td>1,000 Hz [24 Hz resonance frequency]</td><td></td></tr><tr><td rowspan="3">Old meteorological station</td><td>Campbell SR50 - Snow depth</td><td>1</td><td rowspan="3">12/11/2004</td><td rowspan="3">~12 years</td><td rowspan="3">1 hr</td><td>0.25 mm</td></tr><tr><td>Vaisala HMP45A -Temperature</td><td>1</td><td>0.2°C</td></tr><tr><td>Geonor T-200B precipitation</td><td>1</td><td>0.1 mm</td></tr><tr><td rowspan="3">New meteorological station</td><td>Sommer USH-9 - Snow depth</td><td>1</td><td rowspan="3">19/10/2022</td><td rowspan="3">~1 year</td><td rowspan="3">1 hr</td><td>1 mm</td></tr><tr><td>Sommer USH-9 -Temperature</td><td>1</td><td>0.1°C</td></tr><tr><td>Vaisala WXT 530 -precipitation</td><td>1</td><td>0.1 mm</td></tr></tbody></table>

lithology and fracture frequency (Papadimitrakis, 2020). The current interpretation is that the local stress state is the controlling factor, not the lithology (Pless et al., 2021).

## 3. Monitoring Data, Acquisition, and Methods

The monitoring system for the Åknes landslide has been improved with the addition of instruments over many years. The Norwegian Water Resources and Energy Directorate currently manages the Åknes landslide early warning system. The data sets consist of a wide range of displacement, seismic, groundwater, and meteorological data densely covering the landslide's surface and at its sliding interface(s) at depth. The temporal resolution ranges from milliseconds to 1 hour, and the displacement resolution ranges from micrometers to millimeters. Data are transmitted automatically by radio link and optical fiber to be analyzed in near real-time for early warning purposes. Specifications on the type of sensors and acquisition parameters are given in Table 1. The location of the sensors is shown in Figure 1.

The instruments have been installed over two decades in an area prone to harsh weather conditions and are practically only accessible by helicopter. The monitoring network is powered by a mix of solar panels and diesel generators installed in bunkers distributed on and away from the landslide. The unstable slope is steep and prone to rock falls and snow avalanches that may damage both the sensors and their power cables. Power failure or damage to the instruments can take time to repair, explaining the gaps in the time series of some devices.

### 3.1. Surface Displacement Data

Ten Global Navigation Satellite Systems (GNSS) are distributed over the slope (Figure 1). We used relative positioning with one reference receiver on stable ground situated at 0.5 km from the landslide and post-processing on sequences of 12 hr of data to acquire displacement with millimeter precision (σ = 0.6 mm) as estimated by the company Cautus Geo, which provided Trimble equipment and processed the time series of the GNSSs. Ice or snow on the antennas may disturb the GNSS. We have used the northing and easting data providing a 2-D vector to reduce noise from height measurements. In addition, we have removed a few outliers, that is, sudden large
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Sketch (not to scale) of the instrumentation in borehole KH-01-18, including the string of biaxial inclinometers, air packers, and water pressure sensors. This borehole crosses one shear plane in the landslide, along which the basal slip occurs, and continues in the bedrock below. The borehole contains 150 bi-axial sensors. Below the sliding zone, the borehole instrumentation contains segments without sensors along unfractured regions, called extension joints, and segments with sensors that measure individual fractures identified in the core log. The sketch illustrates the use of biaxial inclinometers, air packers, and water pressure sensors to measure both displacement and water pressure in fracture zones and near other heterogeneities identified in the core log.</figcaption></figure>

spikes in the data, and filtered the time series of displacement by implementing a 2-day median filter. The landslide surface velocity is calculated from the displacement time series by fitting a linear function over a given time window and ascribing the slope to the center of that time window. For GNSS data, a linear fit over 4 days was chosen. The decision to filter and fit the GNSS data over two and 4 days balances the need to reduce noise while still detecting velocity accelerations, which may be correlated with creep bursts in the borehole data and can last from a single day to nearly a month.

## 3.2. Borehole Displacement and Piezometric Data

Twelve boreholes have been cored and logged, where seven of these are currently instrumented with a differential monitoring system (DMS) provided by the company Geoengineering Service Center (CSG s.r.l) (Figures 1 and 2). All the boreholes are named KH-XX-YY, corresponding to cored borehole (KH), number of boreholes drilled (XX), and year of drilling (YY). Three are DMS 2D Rock installations and four DMS 3D Multipacker installations that, in addition to the DMS 2D Rock, have several groundwater pressure sensors isolated by air-inflatable packers to monitor the water pressure in selected fracture zones. The depth of the boreholes ranges from 150 to 300 m, well below the basal sliding surface of the landslide, ranging from 9 to 69 m depth.

Both DMS 2D rock and DMS 3D Multipacker installations contain strings of inclinometers spaced one m apart for approximately 150 m. For the two newest DMS 3D Multipacker boreholes (KH-01-18 and KH-02-18), a few additional inclinometers are spaced more scarcely based on visual assessment of fracture zones from the core logs
<caption>Table 2</caption>

Period of Detection, Shear Plane Depth and Shear Zone Material Composition, Parameters Used for the STA-LTA Algorithm to Detect Creep Bursts, Number of Detected Creep Bursts in Each Borehole, and Total Instrument Depth

<table><thead><tr><th>Borehole</th><th>Period</th><th>Shear plane</th><th>Depth (m below ground)</th><th>Composition</th><th>Short term average (days)</th><th>Long term average (days)</th><th>Trigger</th><th>De-trigger</th><th>Number of creep bursts detected</th><th>Total depth of borehole instrument (m)</th></tr></thead><tbody><tr><td rowspan="2">KH-02-06</td><td rowspan="2">02.07.2012 to 27.03.2023</td><td>Upper</td><td>8–9</td><td>Biotitic gneiss with 50% content of biotite<sup>a</sup></td><td rowspan="2">32</td><td rowspan="2">120</td><td rowspan="2">2σ</td><td rowspan="2">1σ</td><td rowspan="2">8</td><td rowspan="2">120</td></tr><tr><td>Lower</td><td>33–34</td><td>Biotitic gneiss with 80% content of biotite<sup>a</sup></td></tr><tr><td rowspan="2">KH-01-12</td><td rowspan="2">07.11.2012 to 27.03.2023</td><td>Upper</td><td>24–25</td><td>Granitic gneiss with varying biotite thickness<sup>b</sup></td><td rowspan="2">12</td><td rowspan="2">60</td><td rowspan="2"></td><td rowspan="2"></td><td rowspan="2">11</td><td rowspan="2">150</td></tr><tr><td>Lower</td><td>62–63</td><td>Dioritic gneiss with 2 cm thick clay layer on each side of 10 cm thick crushed rock<sup>b</sup></td></tr><tr><td>KH-01-17</td><td>15.11.2019 to 27.03.2023</td><td></td><td>35–41</td><td>Biotite-rich gneiss, clay, and crushed rock 10 cm thick at 40 m depth<sup>c</sup></td><td>8</td><td></td><td></td><td></td><td>10</td><td>105</td></tr><tr><td rowspan="2">KH-02-17</td><td rowspan="2">15.10.2018 to 27.03.2023</td><td>Upper</td><td>30–34</td><td>Dioritic and biotite rich gneiss<sup>d</sup></td><td rowspan="2">12</td><td rowspan="2"></td><td rowspan="2"></td><td rowspan="2"></td><td>10</td><td rowspan="2">130</td></tr><tr><td>Lower</td><td>66–70</td><td>Biotitic gneiss with clay and 30 cm crushed rock at 69 m depth<sup>d</sup></td><td></td><td></td><td>13</td></tr><tr><td>KH-01-18</td><td>01.01.2020 to 27.03.2023</td><td></td><td>33–35</td><td>Dioritic and biotite-rich gneiss with clay and crushed rock 10 cm thick at 33 m depth<sup>e</sup></td><td>4</td><td></td><td></td><td></td><td>10</td><td>190</td></tr><tr><td>KH-02-18</td><td>01.01.2020 to 27.03.2023</td><td></td><td>14–17</td><td>Dioritic and biotite rich gneiss<sup>f</sup></td><td>4</td><td></td><td></td><td></td><td>5</td><td>171</td></tr></tbody></table>

Note. The shear plane column indicates boreholes with an upper and lower shear plane, while boreholes with one shear plane are left blank. The onset of creep burst detection is triggered when STA-LTA is above two standard deviations (2σ), and the end of each creep burst (de-trigger) is identified when STA-LTA is below one standard deviation (1σ). <sup>a</sup>Ganerød et al. (2007). <sup>b</sup>Ganerød (2013). <sup>c</sup>Langeland and Holmøy (2018). <sup>d</sup>Langeland and Holmøy (2019b). <sup>e</sup>Langeland and Holmøy (2019a). <sup>f</sup>Langeland and Holmøy (2019c).

(Figure 2). Depending on the borehole location, each inclinometer string crosses one to two shear zones before entering the stable bedrock. The DMS 2D Rock is installed inside a plastic tube in the borehole, while the DMS 3D Multipacker is installed without a plastic tube and relies on air packers to keep the instrument centered in the borehole. Hourly sampling frequency is provided by both borehole types.

As the landslide moves along one or two shear zones, the DMS instrumentation line deforms over time and will eventually break in the future. We use data from six of the seven boreholes, as borehole KH-03-06, located on the Åknes slope below the active landslide zone, has intersected a shear zone visible in core logs but does not record a significant displacement (1 mm/yr). The data set provides maximum displacement in the direction of slip and is downloaded through the software DMS EW (CSG, 2007).

Two long time series are considered. The 11-year time series are recorded in boreholes KH-01-12 and KH-02-06 since late 2012. Four 3-year time series have been acquired on boreholes KH-01-18 and KH-02-18 since late 2019 and boreholes KH-01-17 and KH-02-17 since late 2018 (Table 2). The data set from the former two boreholes had noise caused by packer pressure changes, while the latter two boreholes suffered power issues several times. Consequently, the latter time series investigated here starts on 01.01.2020.

### 3.2.1. Borehole Displacement Processing

In the boreholes, a shear zone deforms the line of inclinometers into an S-shape where the part below the shear plane is stable, and the part above it moves downhill (Figure 3). The actual displacement is given by summing the displacement recorded by one or more inclinometer modules inside the shear zone (Ruggeri et al., 2020).
<figure><img src="image_5.png" /><figcaption>**Figure 3.** A conceptual model of the upper and lower shear zones identified in the boreholes installed on the Åknes landslide. The two profile lines with the respective boreholes are displayed on the map. The displacements measured in the boreholes display one or two distinct shear zones, except for borehole KH-03-06, where no active shear zone could be identified. On the data plots, the y-axis is the depth below the landslide surface, and the x-axis shows the cumulated slip in each borehole for the period 01/01/2020-27/03/2023. The y-axis and x-axis scales are different for each plot. The displacement is measured in the direction of slip and represents the maximum displacement recorded in each borehole. The dark blue and yellow lines on the plots indicate the upper and lower shear zone locations, while the dashed light blue lines indicate the mean groundwater level. The upper shear zone in KH-01-18 was not evaluated further in this study as it became active only a few months before the end of this data set. On the 2D profiles, the shear zones where slip occurs are represented in dark blue and yellow dashed lines. The outcropping sliding plane is interpreted based on borehole data, landslide topography, mapped morphological structures, and geophysical interpretations from the work of Ganerød et al. (2008). The spatial distribution of the proposed sliding scenarios is highlighted by transparent dark blue and yellow polygons on the map.</figcaption></figure>
For the DMS 3D Multipacker boreholes, the instrument string shows displacement created by inflation of the packers before a better inflation solution resolved the problem. Therefore, we have removed this period from the time series, starting the DMS 3D Multipacker time series from 01.01.2020. In addition to the inflation problems, a few electrical or technical errors on the borehole instruments have been solved by remotely resetting the modules and resetting the displacement to zero. We have detected these resetting events and raised the displacement to previous levels by adding the difference in a one-day median before and after the zero resets. We have removed a few outliers, that is, sudden large spikes in the data due to lightning strikes, and filtered the time series by implementing a low pass filter of second order and a one-day cut-off frequency.

We calculated a time series of instantaneous velocity through a linear regression of the displacement time series with data every hour over one day that is 24 data points per day. From this time series, we detect creep bursts through a short-time-average over long-time-average algorithm (STA/LTA), which has been developed to analyze seismic signals (Allen, 1982). The algorithm calculates the average value data on two successive time windows of differing durations and calculates the ratio or the difference between the two averages. The user then defines a threshold where the ratio or the difference is significant to detect a short-term event in the long-term time window.

We apply the STA/LTA algorithm to the velocity data calculated from the displacements measured in the shear plane of the landslide. Because the noise level varies in each borehole, we used different time windows for STA and LTA (Table 2) after a process of trial and error. The LTA was set to 60 or 120 days, depending on the STA time window (Trnkoczy, 2012). Then, we calculated the difference *d* between the STA and LTA averages and defined the onset of a creep burst when *d* becomes larger than two times the standard deviation (σ^d^). The end of each creep burst is defined when *d* decreases below one σ^d^. The two standard deviation thresholds enabled us to focus on fewer and larger events. The duration of the creep bursts depends on the choice of the values of STA and LTA. However, we have verified that the number and amplitude of the creep bursts detected do not vary significantly when these two values are modified by 10%. The durations of the creep bursts we detect range from 1 to 39 days. Because of the high-quality displacement borehole data with a standard deviation of 0.02 mm, our STA/LTA algorithm can detect very small creep bursts with slip amplitude down to 0.1 mm.

### 3.3. Surface Geophone Data

The Norwegian Seismic Array Institute (NORSAR) installed eight geophones at the surface close to the back scarp (black stars on Figure 1) starting in 2005 (Roth et al., 2006). The geophones are three-component short-period (4.5 Hz) sensors that record data with a 1,000 Hz sampling rate and operate in trigger mode. They are connected to the acquisition system by long cables that have been damaged by rock falls over the years. Currently, four of the geophones are operative. A seismic catalog from the surface geophone network is generated fully automatically and provides the time of the detected seismic events and their class (Langet & Silverberg, 2023). Detection times are helpful to compare and correlate the microseismic activity with other environmental parameters. Classes are essential to distinguish between seismic events directly associated with the landslide movement and other events (e.g., regional earthquakes, artificial noise, etc.). Additionally, the classes help differentiate between different types of events occurring on the landslide, as microseismic events could exhibit distinct waveforms depending on their mechanism (e.g., shear sliding or tensile opening). Here, seismic events related to the landslide's activity are classified into tremors, low-frequency earthquakes, and high-frequency earthquakes based on the characteristics of their signals (e.g., duration, frequency content, and shape). The catalog also includes other signals, such as rock falls, snow avalanches, and helicopter flights, that were removed in the present study.

### 3.4. Borehole Geophone Data

In 2017, NORSAR deployed a string of eight geophones in a borehole located in the most active part of the landslide (yellow star in Figure 1). The geophones are located between 15 and 50 m depth and equally spaced every 5 m. They are three-component sensors, and data are recorded continuously with a sampling rate of 1,000 Hz. Initially, the geophones were freely hanging on a string in the borehole. This free-hanging produced noise, a bias resolved in November 2021 by gluing plastic spacers around the geophone pods, thus enabling a good coupling with the borehole casing. Only borehole data after this date are considered here.
The borehole geophone catalog is produced from a semi-automatic analysis of the data. While the detection is automatic, the event classification is performed manually by visually inspecting the waveforms. In the present study, only very high-frequency (100–400 Hz) and short-duration events (<50 ms) are considered. These events likely occur very close to the borehole and are not detected by the surface geophones. Hence, although the exact location of the events cannot be determined, their approximate depth can be inferred from the geophone that first detects the onset, positioned between 15 and 50 m depth, while the other geophones receive that signal after a delay. This depth information is used further to compare the data to other time series.

### 3.5. Meteorological Data

A meteorological station was installed in 2004 above the back scarp at 900 masl (blue circle on Figure 1). The location of this meteorological station is prone to damage resulting from severe weather events, resulting in periods of missing data. A new meteorological station has been installed at a more suitable location and has been operational since 2022 (green circle on Figure 1). For the meteorological parameters, we have downloaded data from seNorge (seNorge, 2023), a weather model service provided by the Norwegian Water Resources and Energy Directorate, the Norwegian Meteorological Institute, the Norwegian Public Road Administration, and the Norwegian Mapping Authority. We relied on this model due to missing data since 2021. The seNorge weather model uses input from all weather stations approved by the Norwegian Meteorological Institute and performs a statistical interpolation over a regular grid with grid spacing of 1 km, providing a time resolution of daily average values. The model is based on non-homogenized data, and after interpolation, the meteorological values are adjusted locally to better fit the observed data (Lussana, 2021). The seNorge model resembles the observed data from both the old and new meteorological stations where the standard deviations of the observed subtracted from modeled data for temperature are 1.97°C (old station) and 2.09°C (new station) and for precipitation, it is 3.17 mm (old station). The newest meteorological station does not yet transmit precipitation data due to sensor problems. However, it records snow measurements, which the old station did not provide, and the standard deviation of the observed subtracted from the modeled value of snow depth is 72 cm (measured in one season only).

### 3.6. Integration of Data

We have utilized the database høydedata (Høydedata, 2023) to get a 1-m-resolution digital elevation model (DEM) when calculating contours in the ArcGIS geographical program (ESRI, 2023). In ArcGIS, we display and compare the spatial movement patterns of the landslide's GNSS and borehole data. The DEM has also been used to create profile lines, and, together with borehole data, we have interpolated shear zones and evaluated possible failure scenarios.

To investigate creep bursts, we have utilized the creep burst database of all boreholes to analyze the relationships between the number of events, slip distance, duration, and velocity. We have also searched for correlations between boreholes and looked for correlation in time between each creep burst and a single water pressure sensor in DMS 2D and both at depth and in the shear plane for DMS 3D boreholes. Thus, an analysis of each creep burst event in relation to water pressure, GNSS surface data at the same location, and seismic data, both at the surface and in a borehole, has been evaluated to decipher possible correlations. Temperature measurements from the individual water pressure sensor depths have also been analyzed in relation to water pressure variations and meteorological parameters.

All data normalized in the following plots have been normalized by the equation $z_i = (x_i - \min(x_i)/(\max(x_i) - \min(x_i)))$, where $z_i$ is the ith normalized value in the data set, $x_i$ is the ith value in the data set, $\min(x_i)$ is the minimum value in the data set, and $\max(x_i)$ is the maximum value in the data set.

## 4. Results

### 4.1. Long-Term and Seasonal Displacements Recorded at the Surface and at Depth

The displacement accumulated over all the line of inclinometers for the two 11-year long time series is 269.9 and 545.0 mm for boreholes KH-02-06 and KH-01-12, respectively. For the same period, the GNSSs at these locations (GPS-5 and GPS-4, see Figure 1) measured 307.6 and 556.5 mm of displacement, respectively.

The cumulative displacements in the four boreholes for the 3-year, shorter time series ranged from 42.7 mm in borehole KH-01-17 to 186.2 mm in borehole KH-02-17, with a median value of 105.7 mm for all the boreholes.
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Time series recorded on the Åknes landslide in the period 01/01/2012-27/03/2023, with vertical black lines indicating the 1st of January of each year. (a) Shear plane slip velocities (linear regression over 60 days) measured with inclinometers, (b) variations of groundwater levels with 2-month median filter, and (c) surface slip velocities (linear regression over 60 days) measured with Global Navigation Satellite Systems are displayed for boreholes KH-01-12 upper shear zone and GPS-4 (blue curves) and KH-02-06 lower shear zone and GPS-5 (black curves). (d) Surface geophones record of seismic events. (e) Air temperature (2-month median filter) and water at the ground level (water equivalent of snowmelt and rain), see explanations in Section 3.5. Seismic events and water at the ground level are summed over 2-month periods. The red dotted rectangle in panel (a) indicates the zoomed-in area shown in Figures S2 and S3 in Supporting Information S1.</figcaption></figure>

All boreholes move at different rates at the assumed shear planes (Figures 1 and 3). The six GNSS stations measure displacement at the locations of the six boreholes for the same period in the range of 41.5 to 164.1 mm, with a median value of 72.3 mm. GPS 4 and GPS 10 are moving at the same rate of 53 mm/yr, and GPS 5 and GPS 11 are moving at the same rate of 23 mm/yr. Along the shear planes in all boreholes, the displacements ranged from 1.2 mm in KH-01-12 lower shear zone to 146.2 mm in KH-01-18, with a median value of 42.2 mm.

Four boreholes display two shear zones along the borehole column (Figure 3). In borehole KH-02-17, the upper- and lower-shear zones exhibit similar displacement rates (19.1 and 20.9 mm/year, respectively). These displacement rates are similar to borehole KH-02-18 (20.1 mm/yr) until the recording in this borehole ended in January 2022. The lower shear zone in borehole KH-02-06 has shown a consistent long-term rate of 4.7 mm/year, while the upper shear zone has increased its steady state creep over the past few years from 1.2 mm/year to 4.1 mm/year. For borehole KH-01-12, the lower shear plane dominated the displacement earlier, but this zone has been quiescent for the past 3 years. The movement has transitioned to the upper zone (Figure S1 in Supporting Information S1), increasing its displacement rate in the same period from 6.8 mm/year to 18.5 mm/year. A transition from the main shear zone in borehole KH-01-18 to a new shear zone at 9 m depth also started in the last months of the analyzed data set. We believe that a locked segment of the shear zone could distribute stress, and as the normal stress increases with depth, it is most likely that some weaker material above the shear zone starts yielding, that is, in the upper sliding zone.

Surface displacements measured with GNSS display a seasonal pattern with one or two maximum velocities in early spring and late autumn to early winter and a minimum in summer (Figures 4, S2, and S3 in Supporting Information S1). The shear zone displacements in the boreholes also display a seasonal pattern with two velocity peaks similar to the GNSS data. However, these patterns are not constant from year to year except for GPS 6
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Time series of the displacements measured with inclinometers in boreholes KH-01-18 (upper panel) and KH-02-18 (lower panel), both located within the lower shear plane of the landslide. Periods of fairly steady-state sliding (black) alternate with 11 and six periods of accelerated velocity identified as creep bursts (pink), respectively. Periods with missing data are shown with horizontal black bars at the bottom of each plot. The inset in the lower panel indicates the locations of the two boreholes. For these boreholes, creep bursts contribute to 7.9% and 9.8% of the total displacement over the periods 01/01/2020-28/03/2023 and 01/01/2020-20/01/2022, respectively.</figcaption></figure>

(Figure S4 in Supporting Information S1). The seasonal groundwater level recorded in open boreholes shows a maximum in the autumn and a minimum in early summer. The long-term displacement rate in the boreholes is quite constant but interrupted by creep bursts (Figure 5).

## 4.2. Seasonal Variations in Temperature, Precipitations, Water Pressure and Seismicity

The monthly average air temperature has a median value of -0.3°C. The maximum monthly average temperature is 13.5°C, and the minimum is -10.8°C. The yearly amount of water accumulated at the ground level ranged from 1,250 mm in 2010–2889 mm in 2022, with a median value of 1,734 mm/year. The timing and intensity of the water accumulated at the ground level are also variable. However, most of it is concentrated in early summer due to snow melt, where the monthly maximum ranged from 479 mm in 2014 to 894 mm in 2020.

The water pressure in most boreholes changes with the seasons (Figure 4). In the water pressure sensor data, we generally observe that water pressure is higher in autumn and lower in spring, a well-known groundwater level fluctuation pattern observed in the Scandinavian mountains (Pedersen & Kirkhusmo, 2003). This effect is less visible in borehole KH-01-17, where the water pressure behaves atypically, and not as significant in boreholes KH-02-06 and KH-01-12, as these are open boreholes without isolated zones by air packers. In these boreholes several fractures drain the water, and therefore data display lower pressure amplitudes (Roques et al., 2014). We observe water pressure values indicating that the groundwater table is above the shear plane in boreholes KH-02-17 upper plane, KH-01-18, and KH-01-17 for the water pressure sensors isolated in the shear zones (see Figure 3). The other boreholes show no water standing above the shear plane where the boreholes are instrumented within the shear plane. Televiewer data revealed water flowing from the shear planes in all boreholes before instrumentation (Movie S1). For water pressure sensors located deeper than the shear plane in boreholes KH-02-17, KH-01-18, and KH-02-18, water pressures are above the elevation of the shear plane, and for borehole KH-01-18, these values are up to 30 m of the water column above the shear plane (Figure S5 in Supporting Information S1). The groundwater pressure was stable in boreholes KH-02-06 and KH-02-18 and had a slightly decreasing trend in borehole KH-01-12 and the upper shear zone of borehole KH-01-18. A trend of a 5-m yearly decrease in water pressure was observed in the lower parts of borehole KH-01-18. Water pressure in boreholes KH-01-17 and KH-02-17 increased in the upper and lower shear zones, and a slight increase was observed in the year 2023 for
borehole KH-03-06 (Table S1 in Supporting Information S1). The temperature measured at the depths of the water pressure sensors remained stable throughout the year. The water pressure sensor situated at 20 m depth in borehole KH-02-18 is sufficiently close to the surface to be affected by the seasonal variations of the temperature of the recharging water. The seasonal decline in groundwater temperature, which starts in April and ends in November with minima in July, reflects the pulse of cold snowmelt water that recharges groundwater every spring (Figure S6 in Supporting Information S1).

The endogenic microseismicity measured from the surface geophone data displays a distinct seasonal pattern with a maximum in the spring and a minimum in the autumn (Figures 4d and S7 in Supporting Information S1). High-frequency events occur throughout the year, while the proportion of low-frequency and tremor events increases in the spring.

The endogenic microseismicity from the borehole geophones shows a less distinct pattern overall, with a general trend of more events being detected in spring and autumn. The time series is dominated by microseismicity detected by the two uppermost geophones located at 20 and 25 m depths (Figure S8 in Supporting Information S1).

### 4.3. Creep Bursts Statistics

Using the STA-LTA method of analysis on the borehole deformation data, we detected 98 creep bursts in total over the six boreholes, with a median frequency of two creep burst events per year per borehole, ranging from 0.8 to 3.7 events per year (Table S2 in Supporting Information S1). The detected creep bursts occur throughout the year (Figures 5, 7, S9, and Table S2 in Supporting Information S1). Two periods of elevated creep burst activity occur in spring and autumn, where the latter is most prominent. The durations of creep bursts vary in the range 1–39 days with a median value of 9 days, while the slip magnitude vary in the range 0.1–3.4 mm with a median value of 0.7 mm (Table S2 in Supporting Information S1). These creep bursts contribute between 7.9% and 23.6% of the total slip along the shear zones of the landslide (total of 150.4 and 28.0 mm of slip over 3 and 11 years, respectively). The contribution of creep bursts to the total slip of the landslide is estimated to be 11% over the period 2012–2023, 14% over the period 2012–2020%, and 8% over the period 2020–2023.

In our catalog of creep bursts, in each borehole, strong positive correlations exist between total slip distance and duration ($r^2 > 0.7$), and moderate to strong correlation ($0.4 < r^2 < 0.7$) between maximum velocity and total slip distance, and maximum creep velocity and creep burst duration (Figure S9 in Supporting Information S1). For the latter two, no correlation ($r^2 < 0.2$) was observed in boreholes KH-01-18 and KH-02-18, which could be due to the small number of data points.

From 2012 to 2020, when only two boreholes were installed, most creep bursts were detected in a single borehole (96%) and few in both boreholes (4%). Since 2020, with six boreholes installed, most creep bursts were detected in a single (58%) or few boreholes (13%). However, as much as 29% of the creep bursts were detected in all the boreholes (Figure 6).

Boreholes KH-01-12 and KH-02-17 are located within 50 m of each other (Figure 3) but display quite different behaviors. Comparatively, borehole KH-01-12 shows large creep bursts, whereas borehole KH-02-17 has tiny bursts (Figure 6). However, in the 3-year time series, the upper and lower shear zones of borehole KH-02-17 and the upper shear zone of borehole KH-01-12 show a similar total displacement in the range 60–66 mm.

### 4.4. Correlation of Creep Bursts With Groundwater Pressure, Meteorological Parameters, and Microseismicity

To assess the correlation of creep bursts with groundwater pressure, we examined the water pressure sensor located in the shear zone of every borehole. When evaluating the effect of groundwater pressure on creep burst initiation, we observed a general relationship between creep bursts occurring at the seasonal high groundwater level in late autumn and at the seasonal low groundwater level in the spring (Figure 7). Looking closely at each creep burst, we often observe a local peak in water pressure preceding the onset of creep bursts occurring from autumn to spring (September-May) (Figures 6 and 8 and Table 3). This local peak is clearly observed in the water pressure sensors in the shear zone and not in the ones deeper below the shear zone (Figures 6, S11, S12, S13, and S14 in Supporting Information S1). We did not observe these local peaks preceding creep bursts in the summer period (June-August).
<figure><img src="image_8.png" /><figcaption>**Figure 6.** Time series of slip velocity (black curves) along the landslide shear zones at depth in five different boreholes. (a–i) Slip velocity in five boreholes. Boreholes KH-02-17, KH-01-12, and KH-02-06 cross two active shear zones, and data from these two zones are shown in separate panels. Creep bursts are highlighted by a vertical patch extending the entire plot for creep bursts occurring either in only a single borehole (orange), two boreholes (blue), or multiple boreholes (green). Creep bursts occur throughout the year. However, events detected in several boreholes occur primarily in Norway's late autumn and winter. The light gray vertical bar extending over all panels highlights the creep burst plotted in Figure 8. Data from water pressure sensors and seismic events from the borehole geophones are added to Figure S9 in Supporting Information S1.</figcaption></figure>

In spring (March-May), the groundwater level rises before creep bursts as a response to the infiltration of increased water at the ground level derived from snowmelt. In autumn (September-November), all the snow has melted, and the groundwater level is influenced mainly by rainfall and, to a much lesser extent, by sporadic cold spells causing minor snowmelt events. Looking at the groundwater temperature change measured at the depth of the water pressure sensors (Figure S6 in Supporting Information S1), the data do not show any change in temperature at the time of the creep bursts in any borehole except one creep burst in borehole KH-02-18. At this event, a drop in water temperature of 0.3°C occurred at the water pressure spike during spring snow melting. These results show that the groundwater reaching the water pressure sensors at depth is older than the recent infiltration as it has equilibrated to the local groundwater temperature and is warmer than the recent cold infiltration (Figure S6 in Supporting Information S1).

We did not observe a systematic correlation between the seismic events from the surface geophones and creep bursts. However, we observed a correlation between the largest creep burst recorded in January 2022 in multiple
<figure><img src="image_9.png" /><figcaption>**Figure 7.** Time series of changes in the water pressure head below the shear zone (blue curves) and pressure head reaching above the shear zone (red curves) at depth in the five different boreholes. Note that the y-axis scale is different for each plot. Boreholes KH-02-17, KH-01-12, and KH-02-06 cross two active shear zones, and data from these two zones are shown in separate panels. The black stars highlight creep bursts that correlate with a local peak in water pressure preceding the initiation of the transient acceleration. In time-series of plots (c, d, e, f), we have subtracted the minimum value of the water pressure head for the DMS 2D open boreholes to zoom in on the changes in the water pressure head.</figcaption></figure>

boreholes and above-average seismic activity in the borehole geophone data (Figure 8). The large majority of seismic events detected during this creep burst occur at a depth of around 25 m, that is, at the level of the shear zone in the geophone borehole located within 50 m from borehole KH-01-12.

# 5. Discussion

## 5.1. Seasonal Displacements

The time series of inclinometers and GNSS displacement display long-term motions with seasonal fluctuations. The Åknes landslide demonstrates a pattern of seasonal surface movement, reaching its highest velocity in late autumn and its lowest during summer in Norway. Meanwhile, the sub-surface displacement shows velocity peaks during both spring and autumn. This seasonal motion is related to snowmelt and precipitation and is associated with increased seismicity within the landslide (Figure 4 and Table S2 in Supporting Information S1). The surface geophones display a distinct seasonal pattern of events, with a maximum in the spring and a minimum in the autumn. The high-frequency micro-seismic events are consistent throughout the year, while low-frequency and
<figure><img src="image_10.png" /><figcaption>**Figure 8.** Zoom on the creep burst detected between the 12th and the 26th of January 2022, which was recorded in multiple boreholes (vertical light gray bar in Figure 6). The data show an increase in groundwater pressure at the depth of the shear zone in one of the boreholes followed by an increase in seismic activity lasting approximately 12 hr at a depth of 25 m, where the upper sliding plane is located. Then, the onset of creep burst occurred along the shear plane in the two boreholes. Here, we show the data of two boreholes where the magnitude of the signal is highest. (a) Normalized borehole displacement measured by inclinometers in boreholes KH-01-17 and KH-02-18. Gray trend lines highlight changes in velocity between before and during the creep burst with their respective velocities. (b) Normalized water pressure from the shear zone of boreholes KH-01-17 and KH-02-18. (c) Seismic activity measured with borehole geophones. The vertical red dashed lines indicate the onset of an increase in each data set. The Global Navigation Satellite Systems data are not displayed due to insufficient resolution to view the event. Data from all boreholes are shown in Figure S15 in Supporting Information S1. Normalization is performed by dividing the data set subtracted by minimum values by the data set maximum subtracted by minimum values ($z_i = (x_i - \min(x))/(\max(x) - \min(x))$).</figcaption></figure>

<caption>**Table 3**</caption>

Summary of Creep Bursts per Season With Respective Events Correlated With a Change in Water Pressure in the Shear Zone

<table><thead><tr><td>Seasons in Norway</td><td>Percent of events</td><td>Percent of events correlated to water pressure change</td></tr></thead><tbody><tr><td>Spring (March-May)</td><td>23.5</td><td>47.8</td></tr><tr><td>Summer (June-August)</td><td>14.3</td><td>14.3</td></tr><tr><td>Autumn (September-November)</td><td>36.7</td><td>41.7</td></tr><tr><td>Winter (December-February)</td><td>25.5</td><td>36.0</td></tr></tbody></table>

*Note.* The table shows data of all creep bursts detected from 2012 to 2023.
tremor seismic events increase in spring. Langet and Silverberg (2023) interpreted the high-frequency pattern as a constant signal from friction along the sliding plane, while the low-frequency and tremor events could correspond to fracturing events and water infiltration.

Previous studies on other landslides have shown a direct link between landslide velocity and groundwater pressure conditions forced by precipitations and snowmelt (e.g., Iverson & Major, 1987). Two processes can be invoked: either a reduction of the normal stress due to pore-water variations at the level of the shear surface (e.g., Agliardi et al., 2020; Rubey & King Hubbert, 1959) or enhanced rock degradation in the presence of water (e.g., Atkinson, 1984; Voigtländer et al., 2018).

In the water pressure data of the Åknes landslide, we observe that several shear zones in boreholes, KH-02-17 lower shear zone, KH-01-12 upper shear zone, KH-02-06, and KH-02-18, display groundwater levels below the sliding zones. The water pressure sensor data show that the shear zones effectively drain excess water pressure. When looking below the shear zones, we observe seasonal changes with high and low groundwater pressures in the autumn and spring, respectively. Sena and Braathen (2021) observed that the groundwater pressure fluctuated more and tended to increase in the upper part of the landslide, while it was stable in the middle and lower parts. With newer data, we found similar annual fluctuations ranging from two to four m in the open boreholes. For the Multipacker boreholes, we observed fluctuations from 14 to 30 m in water pressure sensors located below the shear zones and in the range of less than one to two m of water pressure fluctuations above the shear plane in the shear zones. Groundwater pressure data analyzed now showed no relationship between decreasing trends and the altitude of the landslide surface. However, a large 5-m annual decrease in water pressure was observed for the lower water pressure sensor in borehole KH-01-18 (Table S1 in Supporting Information S1).

Sena and Braathen (2021) modeled that ~90% of groundwater recharge infiltrates at the back scarp, a graben structure that behaves as a groundwater reservoir. The seasonal groundwater fluctuations in the boreholes are the cumulative effect of a long recharge period, starting from early spring with snowmelt followed by rainfall throughout summer and autumn. If groundwater recharge occurred via relatively fast and vertical pathways near the boreholes, we would expect to observe a sharp decrease in groundwater temperature due to the infiltration of cold water during the snow-melting season. However, this is not evident in our measurements, corroborating a longer recharge period with a considerably higher infiltration in the back scarp (Figure S6 in Supporting Information S1). On the other hand, Frei (2008) measured the flow velocity of springs from the outcropping sliding planes. The peak flow velocity of 17.4 m/hr showed a similar order of magnitude compared to the runoff measurements peak of 30 m/hr. These observations suggest that water infiltrates the back scarp, mixes, and equilibrates its temperature before reaching the borehole sensors.

Water at the ground level varies in timing and intensity during the measuring period. The wettest year was more than twice as wet as the driest year, and the month with the most intense rate experienced double the intensity compared to the least intense month. We would expect an equivalent effect on the water pressure with higher peaks in the wetter years and months, but we did not detect such an effect in the boreholes. Previous studies indicate that there are likely many water pathways through fractures other than the two sliding planes and that these fractures are interconnected at different locations, which could explain the highly variable behavior of the different water pressure sensors (Frei, 2008; Sena & Braathen, 2021).

However, the seasonal water fluctuations align with the landslide's velocity changes, but the groundwater pressure variations recorded in the isolated shear zone are very small and water is present above the shear zone in only three out of eight measurement locations. In these three locations, variations in water pressure may trigger creep bursts. Additionally, the water pressure sensors beneath the shear zone are hydraulically disconnected by air packers and are incapable of elucidating the fluctuations in landslide velocity. Thus, the water pressure measurements in the three boreholes with a water level above the shear plane can explain the landslide velocity fluctuations by the conventional reduction of normal stress caused by elevated groundwater levels above the shear plane (e.g., Rubey & King Hubbert, 1959). These observations contradict some numerical modeling studies that concluded that small water table increases above the sliding plane are insufficient to reduce the normal stress to promote sliding (Cancino & Lorig, 2020; Shabanimashcool & Kveldsvik, 2020).

The groundwater flow directions are strongly anisotropic, displaying changing inflow, outflow, and up-and-down-flow gradients of water pressure in the boreholes, inducing seepage forces along the slope that may affect the stability of the landslide (Elvebakk & Pless, 2018). Seepage forces change the direction of the total
forces to point at some angle to the direction of flow rather than parallel to gravity (Mourgues & Cobbold, 2003). The heterogeneity of hydraulic diffusivity could also be important as low diffusivity leads to destabilization and high creep rates that, at some point, fail to decelerate (Zhang et al., 2023). Using the Quiñones-Rozo (2010) method to estimate the hydraulic conductivity from the Lugeon tests conducted by the company Geodrilling (Langeland & Holmøy, 2018, 2019a, 2019b, 2019c), we obtained low hydraulic conductivity ($1 \times 10^{-5}$ – $6 \times 10^{-5}$ cm/s) for boreholes KH-01-18, KH-02-17 and KH-01-17, and medium hydraulic conductivity ($2 \times 10^{-4}$ – $6 \times 10^{-4}$ cm/s) for borehole KH-02-18. The higher creep rates of boreholes KH-01-18 and KH-02-17 corroborate the interpretation by Zhang et al. (2023). Borehole KH-01-17 shows a lower creep rate than borehole KH-02-18, which has a much higher hydraulic conductivity but is also located in a slower-moving part of the landslide.

Yet an alternative explanation to the seasonal velocity changes could be that the decreased strength of the rock is caused by a combination of degradation factors such as the redistribution of stress after glacier retreat (Ballantyne & Stone, 2012; Cossart et al., 2008; Hilger et al., 2018; McColl, 2012) that can promote progressive damage to the slope (Eberhardt et al., 2004) and by static fatigue (Anderson & Grew, 1977; Atkinson, 1984; Cruden & Varnes, 1996; Scholz, 1972) due to seasonal water saturation, both by the changing groundwater levels and the infiltration from the surface which can lead to higher water flow rates in the shear zones.

## 5.2. Creep Bursts

The cumulated borehole displacement measured using inclinometers over the 11-year period (2012–2023) displays a similar magnitude and orientation to the surface displacement measured by GNSS. This is also the case for the 3-year period (2020–2023), except for borehole KH-01-12. This observation validates the inclinometer measurements and processing.

Despite this long-term consistency, the creep bursts are observed only in the inclinometer time series. We assume this is due (a) to the small magnitudes of the creep bursts (0.1–3.4 mm) in relation to the lower resolution of the GNSS measurements ($\sigma$ = 0.6 mm) compared to the borehole inclinometers ($\sigma$ = 0.02 mm), and (b) to the fact that the GNSS surface data measures the displacement cumulated from the bottom to the surface of the landslide, compared to the local measurements of the inclinometers over a shear zone extending at most over a few meters and hence more likely to capture brittle deformation. Therefore, relying solely on surface measurements would exclude important observations from the landslide's shear planes, especially when it deals with identifying when velocity changes occur.

Looking at the spatio-temporal distribution of creep bursts since 2020, 58% are detected in single boreholes, 13% in two boreholes, and 29% in all boreholes. These observations suggest that the landslide shear zones contain some asperities that mostly slip individually but sometimes together. In addition, all boreholes move at different rates along the observed shear planes. This observation indicates that each borehole measures displacement at separate asperities inside the landslide. Until borehole KH-02-18 failed, it moved at a similar rate to borehole KH-02-17. This observation could indicate that those two asperities are underway to coalesce or already have. These observations give an idea of the asperity size, as the inter-distance between neighboring boreholes in the landslide is between 50 and 150 m, while the distance between KH-02-18 and KH-02-17 is 300 m. The same assumption was made by Finnegan et al. (2022) in the Oak Ridge earthflow by observing two extensometers separated by 100 m that slipped independently. This observation and the observation of larger magnitude slips being geometrically limited by the asperities corroborated their hypothesis that larger-size asperities exhibited velocity-weakening properties. However, the difference in rock type between the gneisses at Åknes and rock mélange in Oak Ridge prevents further comparison of these two landslides.

## 5.3. Mechanisms of Creep Burst

In the Åknes landslide, various observations both at the seasonal and weekly-daily scales suggest that transient pore fluid pressure variations are a crucial mechanism for a fraction of the creep bursts, either by a reduction of normal stress by an increase of water pressure in the basal shear zone or by increased driving stress by seepage forces of water levels below the basal shear zone. However, many creep bursts could not be related to water pressure variations in the shear zone or below it, and other mechanisms may be proposed, as discussed below.
### 5.3.1. Seasonal Scales

The creep burst activity shows a slight variation on the seasonal scale. We observed higher creep burst activity from autumn to spring, with most bursts occurring during autumn (Table 3). These bursts of activity correspond to the seasonal high and low groundwater levels, respectively. In the meantime, most of the creep bursts are not correlated with pore water pressure changes (Table 3).

The borehole geophones detect very low magnitude and high-frequency events, indicating that the slip source is very close. These events peak in spring and autumn. The geophones closest to the free surface at 15- and 20-m depth capture most of these events, while deeper geophones at 25 and 50 m (corresponding to the depth of the slip surface) detect sporadic events preceding the detected creep bursts (Figures 8, S8, and S16 in Supporting Information S1). These high-frequency signals may be attributed to either the fracturing of small asperities or the stick-slip events along the shear planes.

Based on these observations, we can hypothesize that two main mechanisms control the creep burst activity: (a) pore fluid effects that reduce normal stress or increase driving stress by water pressure or seepage forces, respectively, and (b) rock strength is likely reduced by long- and short-term rock degradation from stress redistribution and static fatigue. To investigate which of these two mechanisms is at play, we look at a single creep event in the following section.

### 5.3.2. Weekly-Daily Scales

Local peaks in groundwater levels are observed before many creep bursts (Figure 7 and Table 3). However, water pressure is only measurable within the shear zones of KH-01-18, KH-01-17, KH-02-17 upper shear zone, and KH-01-12 lower shear zone. Their water pressures indicated variations of less than two m of water above the shear zones. The other borehole shear zones are dry when considering their water pressure sensor data. Televiewer data in these boreholes show that some free water was running along the shear planes, but the water flow rate is likely insufficient to build pressure above the shear plane (Movie S1).

An increase in micro-seismicity at the shear zone depth less than one day before the largest creep burst event shown in Figure 8 indicates a progressive damage process. Indeed, we have recorded two events during the 3-year time series recorded in the geophone's borehole, where all boreholes slipped (Figures 8, S15, and S16 in Supporting Information S1). In both instances, a spike of high-frequency microseismicity above the average was recorded at the depth of the shear planes (i.e., geophones located at 25 and 50 m) a few hours before the creep burst. In Figure 8, we observe an increase in high-frequency events lasting 11 hr, where the second hour contained the highest rate of events. These events are not detected with the surface geophones, located approximately 200 m from the sub-surface geophones, which indicates the very small magnitudes of these seismic events. The principal acceleration of slip occurs at the end of the period with increased seismic events. This observation suggests that the creep burst nucleated following a phase of very small micro-quakes located at the level or near the shear zone. This behavior mimics the behavior of large earthquake nucleation (e.g., Bouchon et al., 2011).

Water pressure increases precede seismic activity a few hours before the two recorded events (Figures 8, S15, and S16 in Supporting Information S1). This increase in water pressure likely suggests that small water level fluctuations in the shear zone have eased sliding (Agliardi et al., 2020). For the boreholes containing running water in the shear zone, this sliding may be attributed to seepage forces caused by permeability heterogeneities within the landslide (Mourgues & Cobbold, 2003; Tacher et al., 2005). However, our data are insufficient to separate the contributions of the normal stress reduction from those of seepage forces.

During the creep bursts, almost no seismic activity is detected by either surface or subsurface geophones (Figures 8, S15, and S16 in Supporting Information S1), indicating that the slip is mostly aseismic. This lack of seismicity shows that stick-slip is not the mechanism for these creep bursts. Previous experiments on a natural fault have shown an aseismic deformation in response to fluid injection (Cappa et al., 2019, 2022), explained by a permeability increase because of reducing effective normal stress. In that case, the seismicity measured was a remote response of the fractured rock volume to the forcing caused by the aseismic deformation. In our case, the timing of the seismic events preceding the aseismic deformation indicates another mechanism. These new observations would deserve a specific focus on seismic analysis to understand the slip mechanism (Langet et al., 2024), which is beyond the scope of the present study.
<figure><img src="image_11.png" /><figcaption>**Figure 9.** Sketch of the proposed creep burst mechanisms. (t1) Triggering of creep burst by increasing water pressure or large water pressure gradient along the shear zone or slow degradation with some flowing water or no water, breaking small asperities that may produce seismic activity. (t2) Transient slip acceleration on the broken asperity. The velocity may eventually slow down by contact with other asperities in the shear zone. The size of the water arrows illustrates the pressure differences between two timesteps (t1 and t2). The absolute water pressure is higher in t1 and lower in t2. The seepage pressure is higher upslope the asperity than downslope in t1 and reduces to an equal pressure in t2.</figcaption></figure>

For creep bursts in boreholes with dry shear zones where no particular change in water pressure is recorded, a slow degradation of the rock could develop small fractures due to progressive damage that could cause transient slip accelerations by breaking small asperities in the shear zone (Lacroix & Amitrano, 2013; McColl, 2012; Scholz, 1972).

In summary, our observations suggest that even minor water level fluctuations are important for landslide stability and that rock degradation could cause creep bursts unrelated to water level changes, as illustrated in Figure 9.

5.4. Controlling Factors of Creep Bursts

Boreholes KH-02-17 and KH-01-12 are 50 m apart on the same landslide unit (Figure 3) but display very different behaviors. In the last three years of the data set, borehole KH-02-17 measured a cumulated displacement twice larger than borehole KH-01-12. The lower shear zone in borehole KH-01-12 is quiescent, and the displacement in the upper shear zone follows a similar trend but with different rates for both boreholes. Also, the creep bursts in borehole KH-01-12 were about 10 times larger than those in borehole KH-02-17. Local differences in lithological (Green & Marone, 2002; Moore & Lockner, 2004; Noda & Lapusta, 2013) or geometrical (Jolivet et al., 2015) properties near these two boreholes could explain a significant contrast of friction over such a small distance. Indeed, the cored shear zones of KH-02-17 and KH-01-12 differ by comprising biotitic gneiss with a 30 cm thick clay layer in borehole KH-02-17, compared to dioritic gneiss with a 2 cm thick clay layer in borehole KH-01-12 (Table 2). This observation may explain the smaller creep bursts recorded in borehole KH-02-17 by a rate-strengthening behavior and the larger creep bursts recorded in borehole KH-01-12 by a rate-weakening
behavior. Also, the slip velocity of the upper shear zone in borehole KH-01-12 increased while it decreased in the lower shear zone, which could be explained by an interaction between the displacement patterns observed at the shear zones that cross the two boreholes. As the upslope borehole (KH-02-17) moves more than the downslope borehole (KH-01-12), some stress must accumulate between them (Cruden & Varnes, 1996), increasing the shear stress at borehole KH-01-12. This effect could explain the larger creep bursts measured in borehole KH-01-12 compared to borehole KH-02-17, which displays a steadier creep displacement.

Another interesting development is the current slow-down of slip recorded in borehole KH-01-18 (Figure 5), which was the fastest-moving borehole from its inception in 2020 until late 2022. This borehole displayed an up-flow of 2,400 l/hr in a flowmeter test (Elvebakk & Pless, 2018) and has shown groundwater levels up to the shear plane for all water pressure sensors (Figure S5 in Supporting Information S1). The lowermost water pressure sensors display a downward water level trend of 5 m per year since 2020. This lowering rate is exceptionally high compared to the other boreholes (Table S1 in Supporting Information S1). This lowering of the groundwater level may explain the change in the borehole's displacement rate if it is connected to the shear zone by lowering the amount of available water. However, no such connecting trends are observed in the water sensors close to the shear plane, and there is no indication that the water pressure in the shear zone is affected by water pressure fluctuations registered deeper down in this borehole.

Wei et al. (2013) suggested that lithological heterogeneities may control frictional properties that are affected by the thickness of the shear zone and the imposed stress. All shear zones contain some weak clay layers where the rock type mainly comprises biotite-rich gneiss, with some also consisting of a mix of biotite-rich and granitic gneiss (Ganerød, 2013; Ganerød et al., 2008; Langeland & Holmøy, 2018, 2019a, 2019b, 2019c). This heterogeneous composition of the rock will result in different frictional properties, but more so, differences in shear zone thickness, which might help explain why boreholes KH-01-18 and KH-02-18 display higher displacement rates, as they exhibit comparatively narrow shear zones.

The question of how and if water pressure far below the shear plane affects the stability of a landslide remains unanswered. A potential explanation may be that the deeper zones with high water pressure are connected to the shear zone through fractures, making their fluctuations affect the flow rate in the shear zone.

## 5.5. Implications for the Understanding of Landslide Behavior

Previous studies have shown transient landslide accelerations, also called creep bursts (Finnegan et al., 2022; Lacroix et al., 2022; Poli, 2017; Yamada et al., 2016). These accelerations are sometimes used as an early warning signal prior to a major landslide (Kristensen et al., 2021; Lei et al., 2023; Leinauer et al., 2023). In the case of the Åknes landslide, the unique and dense observational data sets (multiple boreholes crossing several shear zones recording displacement and water pressure data together with seismic recordings at depth) allow us to detect these creep bursts with a resolution of less than 100 µm slip. Understanding creep bursts and being able to detect them is crucial from an early warning perspective as these events could be precursors of large-scale failure. We expect that our results will trigger more studies in other landslides to detect if the creep is steady-state or also contains transient acceleration events and if these creep bursts are correlated to environmental parameters.

# 6. Conclusions

Creep bursts are key expressions of landslide, tectonic fault or glacier stability. Their mechanics remain unclear due to their small magnitudes and the previous focus of only detecting and studying them from observations at the surface. Here, we utilize the extensive data set on the Åknes landslide, Norway, that provides a unique opportunity to study the landslide's mechanics by comparing the shear zone's local dynamics, measured in a series of boreholes, and the resulting displacement at the surface. We detect creep bursts at the depth of the shear planes and analyze them regarding the surface motion. We also analyze water pressure and seismic data to correlate them to the creep burst mechanism. We measured that creep bursts accommodate 11% of the total displacement of the Åknes landslide. The surface displacement measured by GNSS is similar to the entire borehole's cumulated displacement but cannot resolve the creep bursts detected by the borehole inclinometers along the shear plane. Creep bursts are spread over the entire year with an increased frequency from autumn to spring. Most creep bursts occurred in separate locations and times, but when all boreholes slipped at once, we measured a correlation between water pressure rise before the creep burst and an increasing microseismic activity at the depth of the shear plane shortly after, followed by a transient acceleration. These observations could indicate that creep bursts result
from a slow degradation process in the landslide shear basal plane rather than an expression of a stick-slip mechanism, as the former would cause microseismic activity during the event. Even with such an extensive data set, it is, however, difficult to pinpoint the cause of these events. The smallest creep events occur without water pressure changes at the initiation of the motion. We hypothesize that these could be caused by rock degradation by stress redistribution and static fatigue on damaging asperities. The largest creep bursts are correlated to groundwater pressure variations. They could result from reduced normal stress from water pressure above the shear plane or, for boreholes with water pressure changes below the shear plane, additional driving stress by seepage forces. Measuring the flow rate in the shear planes in the future, along with a more extensive analysis of microseismic and ambient noise data, could help test the mechanisms at the origin of the observed creep bursts.

## Data Availability Statement

The time series data of the Åknes landslide are available (Aspaas, 2024).

## Acknowledgments

The Norwegian Water Resources and Energy Directorate (NVE) and the University of Oslo (UiO) funded this work. We thank Mario Lovisolo and Enza Garbarino at CSG for technical support. François Renard acknowledges support from the project FricFrac funded by the Center for Advanced Study (CAS) at the Norwegian Academy of Science and Letters during the academic year 2023–2024. We thank Ioannis Papadimitrakis, Fabian Barras, Einat Aharonov, and Mario Lovisolo for helpful discussions.

## References

Agliardi, F., Scuderi, M. M., Fusi, N., & Collettini, C. (2020). Slow-to-fast transition of giant creeping rockslides modulated by undrained loading in basal shear zones. *Nature Communications*, 11(1), 1–11. https://doi.org/10.1038/s41467-020-15093-3

Allen, R. (1982). Automatic phase pickers: Their present use and future prospects. *Bulletin of the Seismological Society of America*, 72(6B), S225-S242. https://doi.org/10.1785/BSSA07206B0225

Anderson, O. L., & Grew, P. C. (1977). Stress corrosion theory of crack propagation with applications to geophysics. *Reviews of Geophysics*, 15(1), 77–104. https://doi.org/10.1029/rg015i001p00077

Aspaas, A. (2024). Time series Åknes landslide, Norway. *Norstore* [Dataset]. https://doi.org/10.11582/2024.00036

Atkinson, B. K. (1984). Subcritical crack growth in geological materials. *Journal of Geophysical Research: Solid Earth*, 89(B6), 4077–4114. https://doi.org/10.1029/JB089iB06p04077

Ballantyne, C. K., & Stone, J. O. (2012). Did large ice caps persist on low ground in north-west Scotland during the Lateglacial Interstade. *Journal of Quaternary Science*, 27(3), 297–306. https://doi.org/10.1002/jqs.1544

Bouchon, M., Karabulut, H., Aktar, M., Özalaybey, S., Schmittbuhl, J., & Bouin, M. P. (2011). Extended nucleation of the 1999 M w 7.6 Izmit earthquake. *Science*, 331(6019), 877–880. https://doi.org/10.1126/science.1197341

Braathen, A., Blikra, L. H., Berg, S. S., & Karlsen, F. (2004). Rock-slope failures in Norway; type, geometry, deformation mechanisms and stability. *Norwegian Journal of Geology*, 84(1), 67–88.

Brideau, M. A., Yan, M., & Stead, D. (2009). The role of tectonic damage and brittle rock fracture in the development of large rock slope failures. *Geomorphology*, 103(1), 30–49. https://doi.org/10.1016/j geomorph.2008.04.010

Cancino, C., & Lorig, L. (2020). Geomechanical modeling for Åknes landslide. Retrieved from https://publikasjoner.nve.no/eksternrapport/2024/eksternrapport2024_05.pdf

Cappa, F., Guglielmi, Y., & De Barros, L. (2022). Transient evolution of permeability and friction in a slowly slipping fault activated by fluid pressurization. *Nature Communications*, 13(1), 3039. https://doi.org/10.1038/s41467-022-30798-3

Cappa, F., Scuderi, M. M., Collettini, C., Guglielmi, Y., & Avouac, J. P. (2019). Stabilization of fault slip by fluid injection in the laboratory and in situ. *Science Advances*, 5(3), eaau4065. https://doi.org/10.1126/sciadv.aau4065

Corfu, F., Andersen, T., & Gasser, D. (2014). The Scandinavian Caledonides: Main features, conceptual advances and critical questions. *Geological Society*, 390(1), 9–43. https://doi.org/10.1144/SP390.25

Cossart, E., Braucher, R., Fort, M., Bourlès, D. L., & Carcaillet, J. (2008). Slope instability in relation to glacial debutressing in alpine areas (Upper Durance catchment, southeastern France): Evidence from field data and 10Be cosmic ray exposure ages. *Geomorphology*, 95(1–2), 3–26. https://doi.org/10.1016/j geomorph.2006.12.022

Cruden, D., & Varnes, D. (1996). Landslide types and processes. In A. K. Turner & R. L. Schuster (Eds.), *Landslides, investigation and mitigation* (Vol. 247). Transportation Research Board, Special Report.36475

CSG. (2007). DMS EW (Version 4.9.21.9.): Centro Servizi de Geoingegneria (CSG) [Software]. https://www.csgsrl.eu/eng/dms-software.html

Dille, A., Kervyn, F., Bibentyo, T. M., Delvaux, D., Ganza, G. B., Mawe, G. I., et al. (2019). Causes and triggers of deep-seated hillslope instability in the tropics—Insights from a 60-year record of Ikoma landslide (DR Congo). *Geomorphology*, 345, 106835. https://doi.org/10.1016/j geomorph.2019.106835

Eberhardt, E., Stead, D., & Coggan, J. S. (2004). Numerical analysis of initiation and progressive failure in natural rock slopes—The 1991 Randa rockslide. *International Journal of Rock Mechanics and Mining Sciences*, 41(1), 69–87. https://doi.org/10.1016/s1365-1609(03)00076-5

Elvebakk, H., & Pless, G. (2018). Borehullslogging Åknes, Stranda kommune, 2017–2018. Retrieved from https://www.ngu.no/publikasjon/borehullslogging-aknes-stranda-kommune-2017-2018

ESRI. (2023). ArcGIS Pro (Version 3.1.3.) [Software]. https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview

Finnegan, N., Brodsky, E., Savage, H., Nereson, A., & Murphy, C. (2022). Seasonal slow landslide displacement is accommodated by mm-scale stick-slip events. *Geophysical Research Letters*, 49(20), e2022GL099548. https://doi.org/10.1029/2022GL099548

Frei, C. (2008). Groundwater flow at the Åknes rockslide site (Norway): Results of a multi-tracer test (Master thesis).

Ganerød, G. V. (2008). Structural mapping of the Åknes Rockslide, Møre and Romsdal County, Western Norway. NGU Report nr. 42. https://hdl.handle.net/11250/2664622

Ganerød, G. V. (2013). Geological logging of drill cores from borehole KH-08-12 at Åknes, Møre and Romsdal, Western Norway. Retrieved from https://www.ngu.no/publikasjon/geological-logging-drill-cores-borehole-kh-08-12-aknes-more-og-romsdal-western-norway

Ganerød, G. V., Grøneng, G., Aardal, I. B., & Kveldsvik, V. (2007). Logging of drill cores from seven boreholes at Åknes, Stranda municipality, Møre and Romsdal County. Retrieved from https://www.ngu.no/publikasjon/logging-drill-cores-seven-boreholes-aknes-stranda-municipality-more-og-romsdal
Ganerød, G. V., Grøneng, G., Rønning, J. S., Dalsegg, E., Elvebakk, H., Tønnesen, J. F., et al. (2008). Geological model of the Åknes rockslide, western Norway. *Engineering Geology*, **102**(1–2), 1–18. https://doi.org/10.1016/j.enggeo.2008.01.018

Gomberg, J., Schulz, W., Bodin, P., & Kean, J. (2011). Seismic and geodetic signatures of fault slip at the Slumgullion landslide natural laboratory. *Journal of Geophysical Research*, **116**(B9), B09404. https://doi.org/10.1029/2011JB008304

Green, H. W., & Marone, C. (2002). Instability of deformation. *Reviews in Mineralogy and Geochemistry*, **51**(1), 181–199. https://doi.org/10.2138/gsrmg.51.1.181

Gudmundsson, A. (2016). The mechanics of large volcanic eruptions. *Earth-Science Reviews*, **163**, 72–93. https://doi.org/10.1016/j.earscirev.2016.10.003

Hilger, P., Hermanns, R. L., Gosse, J. C., Jacobs, B., Etzelmüller, B., & Krautblatter, M. (2018). Multiple rock-slope failures from Mannen in Romsdal Valley, western Norway, revealed from Quaternary geological mapping and 10Be exposure dating. *The Holocene*, **28**(12), 1841–1854. https://doi.org/10.1177/0959683618798165

Høydedata. (2023). Høydedata [Dataset]. https://hoydedata.no/LaserInnsyn2/

Iverson, R. M. (2005). Regulation of landslide motion by dilatancy and pore pressure feedback. *Journal of Geophysical Research*, **110**(F2), F02015. https://doi.org/10.1029/2004JF000268

Iverson, R. M., & Major, J. J. (1987). Rainfall, ground-water flow, and seasonal movement at Minor Creek landslide, northwestern California: Physical interpretation of empirical relations. *Geological Society of America Bulletin*, **99**(4), 579–594. https://doi.org/10.1130/00016-7606 (1987)99%3C579:RGFASM%3E2.0.CO;2

Jaboyedoff, M., Oppikofer, T., Derron, M.-H., Blikra, L. H., Böhme, M., & Saintot, A. (2011). Complex landslide behaviour and structural control: A three-dimensional conceptual model of Åknes rockslide, Norway. *Geological Society, London, Special Publications*, **351**(1), 147–161. https://doi.org/10.1144/SP351.8

Jolivet, R., Candela, T., Lasserre, C., Renard, F., Klinger, Y., & Doin, M. P. (2015). The burst-like behavior of aseismic slip on a rough fault: The creeping section of the Haiyuan fault, China. *Bulletin of the Seismological Society of America*, **105**(1), 480–488. https://doi.org/10.1785/0120140237

Jolivet, R., Jara, J., Dalaison, M., Rouet-Leduc, B., Özdemir, A., Dogan, U., et al. (2023). Daily to centennial behavior of aseismic slip along the central section of the north anatolian fault. *Journal of Geophysical Research: Solid Earth*, **128**(7), e2022JB026018. https://doi.org/10.1029/2022JB026018

Kilburn, C. R., & Petley, D. N. (2003). Forecasting giant, catastrophic slope collapse: Lessons from Vajont, northern Italy. *Geomorphology*, **54**(1–2), 21–32. https://doi.org/10.1016/S0169-555X(03)00052-7

Kristensen, L., Czekirda, J., Penna, I., Etzelmüller, B., Nicolet, P., Pullarello, J. S., et al. (2021). Movements, failure and climatic control of the Veslemannen rockslide, Western Norway. *Landslides*, **18**, 1963–1980. https://doi.org/10.1007/s10346-020-01609-x

Kveldsvik, V., Eiken, T., Ganerød, G. V., Grøneng, G., & Ragvin, N. (2006). Evaluation of movement data and ground conditions for the Åknes rock slide. *International Symposium on Stability of Rock Slopes in Open Pit Mining and Civil Engineering Situations*, 3, 279–299.

Lacroix, P., & Amitrano, D. (2013). Long-term dynamics of rockslides and damage propagation inferred from mechanical modeling. *Journal of Geophysical Research: Earth Surface*, **118**(4), 2292–2307. https://doi.org/10.1002/2013JF002766

Lacroix, P., Belart, J. M., Berthier, E., Sæmundsson, Þ., & Jónsdóttir, K. (2022). Mechanisms of landslide destabilization induced by glacier-retreat on Tungnakvíslarjökull area, Iceland. *Geophysical Research Letters*, **49**(14), e2022GL098302. https://doi.org/10.1029/2022GL098302

Lacroix, P., Handwerger, A. L., & Bièvre, G. (2020). Life and death of slow-moving landslides. *Nature Reviews Earth & Environment*, **1**(8), 404–419. https://doi.org/10.1038/s43017-020-0072-8

Lacroix, P., & Helmstetter, A. (2011). Location of seismic signals associated with microearthquakes and rockfalls on the Séchilienne landslide, French Alps. *Bulletin of the Seismological Society of America*, **101**(1), 341–353. https://doi.org/10.1785/0120100110

Langeland, H., & Holmøy, K. H. (2018). Data report core logging KH-01-17. Retrieved from https://hdl.handle.net/11250/3105277

Langeland, H., & Holmøy, K. H. (2019a). Data report core logging KH-01-18. Retrieved from https://hdl.handle.net/11250/3105288

Langeland, H., & Holmøy, K. H. (2019b). Data report core logging KH-02-17. Retrieved from https://hdl.handle.net/11250/3105287

Langeland, H., & Holmøy, K. H. (2019c). Data report core logging KH-02-18. Retrieved from https://hdl.handle.net/11250/3105289

La页ang, N., Oye, V., Aspaas, A., Lacroix, P., & Renard, F. (2024). New insights on the Åknes rockslide (Norway) using borehole microseismic data [Conference presentation]. *Seismological Society of America*. Retrieved from https://seismosoc.secure-platform.com/a/solicitations/35/sessiongallery/978/application/11055

La页ang, N., & Silverberg, F. M. J. (2023). Automated classification of seismic signals recorded on the Åknes rock slope, Western Norway, using a convolutional neural network. *Earth Surface Dynamics*, **11**(1), 89–115. https://doi.org/10.5194/esurf-11-89-2023

Lay, T., & Nishenko, S. P. (2022). Updated concepts of seismic gaps and asperities to assess great earthquake hazard along South America. *Proceedings of the National Academy of Sciences*, **119**(51), e2216843119. https://doi.org/10.1073/pnas.2216843119

Lei, Q., Sornette, D., Yang, H., & Loew, S. (2023). Real-time forecast of catastrophic landslides via dragon-king detection. *Geophysical Research Letters*, **50**(6), e2022GL100832. https://doi.org/10.1029/2022GL100832

Leinauer, J., Weber, S., Cicoira, A., Beutel, J., & Krautblatter, M. (2023). An approach for prospective forecasting of rock slope failure time. *Communications Earth & Environment*, **4**(1), 253. https://doi.org/10.1038/s43247-023-00909-z

Lussana, C. (2021). seNorge observational gridded dataset. seNorge_2018, versions 21.09 and 21.10. MET report, Report 07/2021. https://doi.org/10.13140/RG.2.2.36336.38400

Mainsant, G., Larose, E., Brönnimann, C., Jongmans, D., Michoud, C., & Jaboyedoff, M. (2012). Ambient seismic noise monitoring of a clay landslide: Toward failure prediction. *Journal of Geophysical Research*, **117**(F1), F01030. https://doi.org/10.1029/2011JB002159

McColl, S. T. (2012). Paraglacial rock-slope stability. *Geomorphology*, **153**, 1–16. https://doi.org/10.1016/j.geomorph.2012.02.015

Moore, D. E., & Lockner, D. A. (2004). Crystallographic controls on the frictional behavior of dry and water-saturated sheet structure minerals. *Journal of Geophysical Research*, **109**(B3), B03401. https://doi.org/10.1029/2003JB002582

Mourgues, R., & Cobbold, P. (2003). Some tectonic consequences of fluid overpressures and seepage forces as demonstrated by sandbox modelling. *Tectonophysics*, **376**(1–2), 75–97. https://doi.org/10.1016/S0040-1951(03)00348-2

NGI. (1996). Åkernes landslide - General description of the Åkernes slide area and control measures (Vol. 585910-9). Norwegian Geotechnical Institute.

Noda, H., & Lapusta, N. (2013). Stable creeping fault segments can become destructive as a result of dynamic weakening. *Nature*, **493**(7433), 518–521. https://doi.org/10.1038/nature11703

Papadimitrakis, I. (2020). Åknes rock mass characterization, January-July 2020. Internal report, UiO NVE collaboration project. Retrieved from https://publikasjoner.nve.no/ekstemrapport/2024/ekstemrapport2024_09.pdf

Pedersen, T. S., & Kirkhusmo, L. A. (2003). Overvåking av grunnvann - Landsomfattende grunnvannsnett (LGN). NGU Rapport nr. 1. Retrieved from https://publikasjoner.nve.no/rapport/2003/rapport2003_01.pdf
Pless, G., Blikra, L. H., & Kristensen, L. (2021). Possibility for using drainage as mitigation to increase the stability of the Åknes rock-slope instability, Stranda in western Norway, NVE Rapport nr. 22. Retrieved from https://publikasjoner.nve.no/rapport/2021/rapport2021_22.pdf

Poli, P. (2017). Creep and slip: Seismic precursors to the Nuugaatsiaq landslide (Greenland). *Geophysical Research Letters*, 44*(17), 8832–8836.
https://doi.org/10.1002/2017GL075039

Quiñones-Rozo, C. (2010). Lugeon test interpretation, revisited. Paper presented at the Collaborative management of integrated watersheds, US Society of Dams, 30th annual conference. https://silo.tips/download/lugeon-test-interpretation-revisited-camilo-quiones-rozo-pe-1-abstract

Roques, C., Bour, O., Aquilina, L., Dewandel, B., Leray, S., Schroetter, J. M., et al. (2014). Hydrological behavior of a deep sub-vertical fault in crystalline basement and relationships with surrounding reservoirs. *Journal of Hydrology*, 509, 42–54. https://doi.org/10.1016/j.jhydrol.2013.11.023

Roth, M., Dietrich, M., Blikra, L. H., & Lecomte, I. (2006). Seismic monitoring of the unstable rock slope site at Åknes, Norway. In *Symposium on the application of geophysics to engineering and environmental problems 2006* (pp. 184–192). Society of Exploration Geophysicists. https://doi.org/10.4133/1.2923645

Rubey, W. W., & King Hubbert, M. (1959). Role of fluid pressure in mechanics of overthrust faulting: II. Overthrust belt in geosynclinal area of western Wyoming in light of fluid-pressure hypothesis. *Geological Society of America Bulletin*, 70(2), 167–206. https://doi.org/10.1130/0016-7606(1959)70[167:rofpim]2.0.co;2

Ruggeri, P., Fruzzetti, V. M., Ferretti, A., & Scarpelli, G. (2020). Seismic and rainfall induced displacements of an existing landslide: Findings from the continuous monitoring. *Geosciences*, 10(3), 90. https://doi.org/10.3390/geosciences10030090

Scholz, C. H. (1972). Static fatigue of quartz. *Journal of Geophysical Research*, 77(11), 2104–2114. https://doi.org/10.1029/jb077i011p02104

Sena, C., & Braathen, A. (2021). Åknes rock-slope failure hydrogeology: Final report. Retrieved from https://publikasjoner.nve.no/eksternrapport/2024/eksternrapport2024_09.pdf

seNorge. (2023). Varsom seNorge [Dataset]. https://www.senorge.no/

Shabanimashcool, M., & Kveldsvik, V. (2020). Drainage Åknes - coupled hydro-mechanical stability analysis of Åknes rock slope. Retrieved from https://hdl.handle.net/11250/3105292

Tacher, L., Bonnard, C., Laloui, L., & Parriaux, A. (2005). Modelling the behaviour of a large landslide with respect to hydrogeological and geomechanical parameter heterogeneity. *Landslides*, 2(1), 3–14. https://doi.org/10.1007/s10346-004-0038-9

Templeton, D. C., Nadeau, R. M., & Bürgmann, R. (2008). Behavior of repeating earthquake sequences in central California and the implications for subsurface fault creep. *Bulletin of the Seismological Society of America*, 98(1), 52–65. https://doi.org/10.1785/0120070026

Trnkoczy, A. (2012). Understanding and parameter setting of STA/LTA trigger algorithm. In P. Bormann (Ed.), *New manual of seismological observatory practice*, 2 (NMSOP-2) (pp. 1–20). Deutsches GeoForschungsZentrum, GFZ. https://doi.org/10.2312/GFZ.NMSOP-2_IS_8.1

Tveten, E., Lutro, O., & Thorsnes, T. (1998). Berggrunnskart Ålesund. 1:250000, (Ålesund, western Norway). NGU Trondheim (bedrock map). Retrieved from https://www.ngu.no/publikasjon/alesund-berggrunnskart-alesund-m-1250-000-trykt-i-farger

Viesca, R. C., & Rice, J. R. (2012). Nucleation of slip-weakening rupture instability in landslides by localized increase of pore pressure. *Journal of Geophysical Research*, 117(B03104). https://doi.org/10.1029/2011JB008866

Voigtländer, A., Leith, K., & Krautblatter, M. (2018). Subcritical crack growth and progressive failure in Carrara marble under wet and dry conditions. *Journal of Geophysical Research: Solid Earth*, 123(5), 3780–3798. https://doi.org/10.1029/2017JB014956

Wei, M., Kaneko, Y., Liu, Y., & McGuire, J. J. (2013). Episodic fault creep events in California controlled by shallow frictional heterogeneity. *Nature Geoscience*, 6(7), 566–570. https://doi.org/10.1038/ngeo1835

Wesson, R. L. (1988). Dynamics of fault creep. *Journal of Geophysical Research*, 93(B8), 8929–8951. https://doi.org/10.1029/JB093iB08p08929

Yamada, M., Mori, J., & Matsushi, Y. (2016). Possible stick-slip behavior before the Rausu landslide inferred from repeating seismic events. *Geophysical Research Letters*, 43(17), 9038–9044. https://doi.org/10.1002/2016GL069288

Zhang, H., He, S., Liu, W., Deng, Y., & Hu, W. (2023). Creep-to-Runout transition of large landslides controlled by frictional velocity strengthening and weakening (Vajont 1963, Italy). *Rock Mechanics and Rock Engineering*, 56(11), 8471–8483. https://doi.org/10.1007/s00603-023-03473-2