# Climate-change-induced seismicity: The recent onset of seasonal microseismicity at the Grandes Jorasses, Mont Blanc Massif, France/Italy

Verena Simon, Toni Kraft, Jean-Christophe Maréchal, Agnès Helmstetter, Tobias Diehl

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## To cite this version:

Verena Simon, Toni Kraft, Jean-Christophe Maréchal, Agnès Helmstetter, Tobias Diehl. Climate-change-induced seismicity: The recent onset of seasonal microseismicity at the Grandes Jorasses, Mont Blanc Massif, France/Italy. Earth and Planetary Science Letters, 2025, 666, pp.119372. 10.1016/j.epsl.2025.119372. hal-05173564

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Submitted on 21 Jul 2025

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# Climate-change-induced seismicity: The recent onset of seasonal microseismicity at the Grandes Jorasses, Mont Blanc Massif, France/Italy

Verena Simon<sup>a, ID,*</sup>, Toni Kraft<sup>a, ID</sup>, Jean-Christophe Maréchal<sup>b,c, ID</sup>, Agnès Helmstetter<sup>d, ID</sup>, Tobias Diehl<sup>a, ID</sup>

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## ARTICLE INFO

Editor: H. Thybo

**Keywords:**

Climate change

Short-term seismic hazard

Seasonal earthquake activity

Groundwater flow

Meltwater infiltration

## ABSTRACT

Modeling studies indicate that the geosphere can dynamically respond to climate change, increasing geologi- cal and geomorphological hazards. One such hazard is climate-driven seismicity due to hydrological changes, though observational evidence supporting this phenomenon remains scarce. We present the first dataset link- ing climate-change-induced snow/glacier melt to increased seismic hazard. Using a template-matching-enhanced catalog (2006–2022), we analyze the ongoing Grandes Jorasses Earthquake Sequence (GJES, Mont Blanc Massif, France/Italy; $M_I \le 3.1/M_W \le 2.9$), which exhibits a sudden onset of strong annual periodicity in fall 2015. Our relocations identify seismicity along a major fault zone outcropping in the Mont Blanc Tunnel, where runoff and isotope data suggest that inflow is dominated by young surface meltwater. Modeling meltwater infiltration with a 1D-hydraulic diffusion constrained by the S2M meteorological snowpack model confirms that most of the GJES seismicity can be meltwater-induced. Additionally, our statistical analysis reveals a migratory seismicity compo- nent, hosting the largest events. While initially triggered by seasonal meltwater, this component expands primarily via a tectonic mechanism affected by aseismic slip. We attribute the onset of increased and periodic seismicity in 2015 to intensified climate-change-driven heat waves affecting the Mont Blanc Massif’s high-altitude cryosphere. Retreating permafrost and glaciers alter meltwater-infiltration pathways, inducing pore-pressure changes that trigger seismicity in new source areas. During peak meltwater-driven seismicity, the seismic hazard levels can rise by two orders of magnitude compared to pre-2015 levels. Our findings suggest that climate change can sig- nificantly elevate the local seismic hazard in alpine regions. This phenomenon may affect other glaciated areas globally, highlighting the need for improved seismic risk assessment for impacted alpine communities.

## 1. Introduction

The climate-change-related rise in global temperatures can cause various changes in the hydro- and geosphere. Among them is the in- crease in snow and glacier melt, especially in mountainous and arctic regions (Huss and Hock, 2018; Pfeffer et al., 2014), which results in in- creased surface runoffs that can cause flooding or – when infiltrating the aquifer – local rise in the water table and geospheric pore-fluid pressure. Additionally, these melting processes can modify the cryospheric surface load on the Earth’s crust (Heki, 2003). Changes in pore-fluid pressure

and surface load translate into changes in effective normal stress – a cru- cial fault-stabilizing factor - and can facilitate rupture initiation and fault reactivation (Wang and Manga, 2021). Therefore, it has been postulated that climate change could impact seismic activity in vulnerable areas and increase the seismic hazard of alpine and arctic societies (McGuire, 2022).

Seasonal variations in seismic activity have been observed since the onset of earthquake monitoring (e.g., Omori, 1902). Benefiting from improved observational evidence, several studies have confirmed the existence of these variations globally. For example, seasonal snow and

<footnote><sup>a</sup> Swiss Seismological Service, ETH Zürich, Sonneggstrasse 5, Zürich, 8006, Zürich, Switzerland</footnote>

<footnote><sup>b</sup> BRGM, Univ. Montpellier, 1039 rue de Pinville, Montpellier, 34000, Occitania, France</footnote>

<footnote><sup>c</sup> G-EAU, Univ. Montpellier, AgroParisTech, BRGM, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France</footnote>

<footnote><sup>d</sup> ISTERRE, CNRS, Université Grenoble Alpes, BP 53, Grenoble, 38041, Isère, France</footnote>

<footnote>* Corresponding author. Swiss Seismological Service (SED) at ETH Zurich, Sonneggstrasse 5, 8092 Zürich, Switzerland. E-mail address: verena.simon@sed.ethz.ch (V. Simon).</footnote>
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Seismotectonic overview of the study region. (a) Top: Epicenter map of the study area showing earthquakes since 1984 (blue dots: relocated earthquakes of the GJES; green dots: Background seismicity since 1984, relocated by Diehl et al. (2021); all dots are scaled by magnitude). Yellow stars indicate $M_w>4$ earthquakes. Triangles show the seismic network used in this study (red triangles: broadband stations: IV.MRGE (used for template matching) and CH.MFERR; orange triangles: temporary short-period stations (8D.VFERR/2, 8D.AMIDI/2); gray triangles: other broadband stations). Bottom: Overview map indicating the study region as a red square and legend for symbols used in the top map. Schematic tectonic map (b) and northwest-southeast cross-section (c) of the study area showing the major tectonic lines and units, adapted from Egli and Mancktelow (2013) and Bergemann et al. (2019). Profile trace, C–C', indicated in (b). Major tectonic units are indicated in the legend. Major tectonic fault zones: Mont Blanc Shear Zone (MBSZ), Penninic Basal Thrust (PBT), Faille du Midi (FM), Rhône Simplon Fault (RSF) and Mont Blanc Back Thrust (MBBT). The eroded part of the Mont Blanc Massif is shown by transparent colors in (c).</figcaption></figure>

water loads are suggested to drive annual fluctuations in seismicity in parts of California (Christiansen et al., 2007), Japan (Heki, 2003), and the Himalayan seismic zone (Bettinelli et al., 2008). Gao et al. (2000) propose that atmospheric pressure variations caused seasonal seismicity variations in parts of California following the 1992 Landers earthquake. Snow and glacier melt (Montgomery-Brown et al., 2019; Johnson et al., 2020), as well as intense rainfall events (Hainzl et al., 2006; Husen et al., 2007) have been suggested as triggers for seismic activity, attributed to increased subsurface recharge and the corresponding increase in pore-fluid pressure. Ben-Zion and Allam (2013) postulated that seasonal thermoelastic strain resulting from atmospheric temperature fluctuations could impact shallow earthquake activity. Finally, also periodic anthropogenic activities, such as fluid injection in geothermal fields (Johnson et al., 2016), variations in reservoir-lake levels (Smirnov et al., 2017), and the redistribution of sediment loads due to gold mining (Westaway, 2002), have been suggested to influence earthquake occurrence.

Extensive research examines how external forcing affects seismicity rates. However, direct observations of how these external factors impact the seismic hazard of a region are currently limited. This study presents the ongoing Grandes Jorasses Earthquake Sequence (GJES) in the Mont Blanc Massif (MBM; France/Italy). Based on a 16-year-long enhanced earthquake catalog, our analysis reveals a striking increase in seismic activity in fall 2015, dominated by an annual periodicity. Notably, this surge corresponds with an augmented short-term seismic hazard (STSH). During peak seismicity episodes in fall, the STSH escalates by up to four orders of magnitude compared to pre-2015 levels. Worldwide, glaciated regions with suitable subsurface conditions may experience similar impacts. Therefore, it is essential to quantify the fluctuations in seismic risks associated with these phenomena to safeguard affected alpine and arctic communities.

## 2. Geological and tectonic background

The MBM is located in the Western Alps, within the tri-national region where the borders of France, Italy, and Switzerland converge (Fig. 1a). The northeast-southwest trending mountain range is characterized by its high relief and steep topography. It hosts 11 summits surpassing 4000 m in altitude, including Europe's highest peak (Mont Blanc, 4808 m) and is largely covered by glaciers above 3000 m.

From a geological perspective, the MBM is a tectonic window, where pre-Carboniferous crystalline rocks were uplifted in a dome-like manner and exposed through the younger Helvetic and Penninic nappes that overthrusted them (Fig. 1c). Part of the MBM consists of strongly folded micaschists and gneisses, which mainly outcrop at the southwestern end and northwestern rim of the MBM, and are separated by the sheet-like Montenverts Granite and a zone of coarse-grained augen gneisses, related to an Ordovician intrusion. The main part of the MBM is composed of the Mont Blanc Granite (MBG) and its associated dikes. The eastern side consists of an Upper Carboniferous rhyolite complex. Northwest of the MBM and Chamonix Valley, the Aiguille Rouges Massif (ARM) forms a second basement window. The two massifs are part of five similar tectonic windows in the outer parts of the Alpine chain (Helvetic realm), which are referred to as External Massifs (von Raumer and Bussy, 2004).

The MBM is delineated by valleys associated with large alpine deformation zones. On their southeastern side, the Italian Val Ferret and Val Veny follow the Penninic Basal Thrust, a primary fault zone separating the inner and outer parts of the Western Alps, which dips to the southeast at this location. However, in the uppermost kilometers, the contact between the MBM basement and Helvetic cover is overturned, forming a series of steeply northwestward-dipping thrust faults, the most prominent being the Mont Blanc Back Thrust (MBBT, Fig. 1b). The northwest-
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** (a) Geological cross-section through the MBM along the MBT (adapted from Maréchal et al., 1999). (b) Initial water inflows recorded less than a day after tunnel excavation (Maréchal et al., 1999). Most significant inflows are observed in the MBM around 3.6 km from the Italian portal (fracture zone FZ367). (c) Groundwater inflow temperature profile through the MBT (Maréchal, 2000). (d) Top and Middle: Evolution of flowrate and groundwater electrical conductivity of inflow from FZ367 measured in the MBT (1996–1998) (Maréchal, 2000). Bottom: S2M-Surface-Runoff (see text) for elevation range of suspected surface inflow zone (2700 m S2M elevation bin, Vernay et al., 2022).</figcaption></figure>

ern boundary of the MBM is defined by the French Chamonix Valley hosting the Helvetic units of the Chamonix Zone, which are squeezed between the crystalline MBM and ARM. The strongly sheared Mont Blanc Shear Zone (MBSZ) at the northwestern side of the MBM borders the Chamonix Zone and runs between the Chamonix Valley and the Faille du Midi (FM), a massif-scale fault that broadly follows the MBG/gneiss boundary and only in the south diverts into the crystalline schists. The shear zone is linked to the dextral Rhone Simplon Fault, which enters the valley from Martigny in the northeast (Egli and Mancktelow, 2013).

The 11.6 km-long Mont Blanc Tunnel (MBT; Fig. 1) crosses the MBM at an altitude of roughly 1300 m, connecting the cities of Courmayeur, Italy, and Chamonix, France. From the Italian portal, the tunnel traverses 1.3 km of Mesozoic limestone of the Helvetic cover, then crosses the MBBT, enters the MBM, and passes through 6.8 km of the MBG and 3.5 km of micaschists and gneisses (Fig. 2a; Maréchal et al. 1999). The contact between the MBG and the Helvetic cover (MBBT) is tectonic, while the boundary with the micaschists gradually extends over 425 m and corresponds to contact metamorphism.

During tunneling (1959–1962), cataclastic fracture zones caused significant challenges on the Italian side, resulting in rock bursts and massive water inflows (Dal Piaz and Argentieri, 2021, Fig. 2a and b). The most dominant one, a 600 m-wide, steeply northwest-dipping and N225°E-striking, tectonic shear zone, 3.67 km from the Italian portal, is associated with a prominent groundwater temperature anomaly that reached a minimum of 13 °C at excavation time (Fig. 2c). Notably, the shear zone consists of alternating draining and impervious layers (the latter containing impermeable clay, a product of intense cataclastic and mylonitization) inhibiting tunnel-parallel groundwater circulation in the MBG (Maréchal, 2000).

Numerical modeling suggests that the temperature anomaly developed by meltwater inflow into this fault zone after the last ice age (Maréchal et al., 1999). This interpretation is supported by the observation of massive colder-water inflow from this zone (1086 L/s) during tunneling, which halted excavation works for several weeks (Dal Piaz and Argentieri, 2021). More recent hydrogeological measurements in the MBT suggest that meltwater inflow continues at reduced flow rates
(196 L/s) today (Maréchal et al., 1999, Fig. 2d). We will discuss this aspect later in the manuscript (Sec. 5.1) and will refer to the mentioned fault zone as "FZ367".

### 3. Background seismicity

To provide context for the discussion of the GJES, we briefly examine the seismicity within a 25 km radius:

The earthquakes, instrumentally recorded by the Swiss Seismological Service at ETH Zürich (SED) between January 1984 and May 2020 and consistently relocated by Diehl et al. (2021), are shown in Fig. 1. These earthquakes primarily concentrate in well-defined northeast-southwest-oriented bands. One band originates from the northern side of the Swiss Upper Rhone Valley, bifurcating into two segments running along the ARM's northwestern rim and through the Chamonix Valley. Another band appears at the southern side of the Rhone Valley, passing northeast of Courmayeur (Sion Courmayeur Zone). These bands closely follow the tectonic features previously described.

The region experienced significant seismic activity, notably the $M_W$ 5.3 Chamonix earthquake in 1905, followed by aftershocks along with a $M_W$ 4.3 earthquake, and more recent seismic events including the $M_W$ 4.5 Vallorcine earthquake in 2005 and a $M_W$ 3.4 earthquake in 2022 near Argentière (Suppl. Material, Sec. 2).

### 4. Analysis of the Grandes Jorasses Earthquake Sequence

The GJES is situated beneath the well-known homonymous mountain range on the northwestern side of the MBM. Its centroid is approximately 5 km northeast of the MBT. Its elongated seismic cloud extends about 9 km in the east-northeast direction, has a width of about 3.5 km, and aligns well with the extrapolation of FZ367. Reliable locations indicate source depths between 0–7 km. The SED catalog lists 1042 earthquakes in our study period (10 November 2006 to 2 October 2022), with magnitudes ranging from $M_L$-0.5 ($M_W$ 0.5) to $M_L$ 3.1 ($M_W$ 2.9) for the sequence. The main activity started in 2015, with only a few events before that. The six strongest earthquakes (which we refer to as main-events) with magnitudes between $M_L$ 2.5 ($M_W$ 2.5) and $M_L$ 3.1 ($M_W$ 2.9), occurred in February and March 2019. The focal mechanisms of the two strongest mainevents indicate normal faulting with a minor strike-slip component (Suppl. Material, Table S2).

#### 4.1. Method

We utilize the semi-automatic analysis workflow of Simon et al. (2021) to generate an enhanced and consistent microearthquake catalog of the GJES (Suppl. Material, Sec. 1). Starting with SED's routine catalog, we improved earthquake detection through a single-station template matching (TM) approach, combined with bias-reduced, local magnitude estimation ($M_{Lx}$; Herrmann et al., 2019). The TM scan used the closest, long-term-operating seismic station (IV.MRGE; Istituto Nazionale di Geofisica e Vulcanologia (INGV), 2005), which is located 13 km south of the GJES and provided continuous recordings since 10 November 2006 (Fig. 1). We utilized 372 templates to identify 12,303 additional earthquakes undetected in the routine catalog (Fig. 3).

We then analyzed the enhanced catalog at a high temporal resolution using statistical methods (Gulia et al., 2016; Tormann et al., 2015). For this analysis, we converted $M_{Lx}$ to moment magnitude $M_W$, using the scaling relationship of Edwards et al. (2015). The overall completeness magnitude for the entire sequence is $M_C = M_W$ 0.6 = $M_L$ -0.2. We used the slightly higher $M_C = M_W$ 0.7 obtained for the period before 2015 and only included earthquakes with larger magnitudes in the statistical analysis. We then computed three statistical parameters for the earthquake sequence: the diurnalized *a*-value and the *b*-value of the Gutenberg-Richter law (Gutenberg and Richter, 1944), and the occurrence probability of earthquakes equal or larger than a given target magnitude in the next 24 hours ($P_{M \ge m_t}$), where $P_{M \ge m_t} = 1 - e^{-1/T_{m_t}}$, and

$T_{m_t} = 1/(10^{a-bm_t})$ is the recurrence time of a target-magnitude event. Each statistical parameter was calculated at the end of a 100-event-long, event-wise-moving window, ensuring that only past earthquakes influenced the results. The resulting timelines are shown in Fig. 3c and 3d.

To resolve the spatiotemporal behavior of the GJES, we relocated 1142 sufficiently large ($M_L$>0.5/$M_W$>1.2) earthquakes of the enhanced catalog by employing the double-difference technique (Waldhauser and Ellsworth, 2000) in combination with cross-correlation-improved differential times derived using the method of Diehl et al. (2017). To improve the accuracy of earthquake location in the MBM region, we installed a temporary network of three stations (CH.MFERR, 8D.VFERR/2, 8D.AMIDI/2; Swiss Seismological Service (SED) At ETH Zurich, 2005, 1983) between June 2019 and October 2020. Additionally, we added more stations to our study via a data-sharing agreement with ISTerre and IGE in Grenoble, France (ZO.ARGSF, ZO.ARGFO, ZO.ARGB3, 8C.CI19, 8C.CI20, 8C.CI21). This way, we maintained the appropriate station coverage for our analysis over the entire study period (Fig. 1a; data references are given in the Suppl. Material, Table S1).

(Technical details on the workflow: Suppl. Material, Sec. 1.)

#### 4.2. Results

Our TM-analysis has unveiled the temporal activity pattern of the GJES with exceptional detail (Fig. 3). Before September 2015, the sequence exhibited low activity, with only 390 detected events and no discernible occurrence pattern. Starting in September 2015, we observed a significant increase in seismicity and the onset of an annual periodicity. In the following we will discuss this latter time span. For a more straightforward discussion, we will refer to the interval from July of a given year to June of the subsequent year as a "Period" (e.g., "Period 15/16" or "P15/16").

##### 4.2.1. Temporal evolution and statistical analysis

Typically, the peak seismic activity of a given Period manifests in its colder months and is characterized by a rapid increase in fall (fall-peak), followed by a gradual decrease extending into early spring. This pattern is evident in the histogram displayed in Fig. 3a and further documented by the fluctuation of the *a*-value, which typically shows a step-like increase between September and November and a rapid, slowly-decelerating decrease extending into March (Fig. 3c) in every Period after September 2015. The *a*-value fluctuations align with significant *b*-value variations. Generally, the *b*-values tend to be higher in fall than in spring, reaching their lowest levels in summer. Higher *b*-values indicate more small-magnitude events within the associated subset of analyzed earthquakes. This characteristic is also evident in the seismicity timeline in Fig. 3b. In contrast, the *a*- and *b*-values remained relatively constant before September 2015, with slight long-wavelength variations.

Examining $P_{M \ge 3}$ (Fig. 3d) as an indicator of the STSH linked to GJES activity reveals a notable change in behavior around September 2015. Preceding this date, the time series exhibits minor long-wavelength fluctuations, hovering around a relatively stable level of $P_{M \ge 3} = 2.4 \cdot 10^{-4}$ [per day]. The time series shows two distinct patterns. The first, a distinguishing feature of all post-2015 Periods, manifests as a step-wise increase by up to nearly four orders of magnitude compared to the pre-2015 level in fall, indicating high occurrence probabilities of $M_W \ge 3$ ($M_L \ge 3.2$) earthquakes in these Periods. The second is marked by two upward shifts in the $P_{M \ge 3}$ baseline: one in September 2015 and another in December 2018. These two shifts culminate in an approximately 8.8 and 18.1 fold increase in $P_{M \ge 3}$ respectively.

Notable deviations from this typical temporal behavior are observed in P17/18 and P18/19. Alongside the usual fall-peak, these Periods exhibit a second seismicity peak in late winter to early spring (late-winter-peak). In both Periods, we observe a $P_{M \ge 3}$ increase and a significant *b*-value decrease before these late-winter peaks. The *b*-value
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Temporal evolution of the GJES: (a) Rate and cumulative number of earthquakes from TM detection. (b) Seismicity timeline: Moment magnitudes of TM detections color-coded by template association. (c) Statistical analysis I: Diurnalized *a*-value (i.e., normalized to reflect a rate based on a 24-hour reference period) and *b*-value of TM detections larger M<sub>C</sub> = M<sub>W</sub>0.7. (d) Statistical analysis II: Decimal probability of an earthquake with M<sub>W</sub>≥3.0 (M<sub>L</sub>≥3.2) in the next 24 hours (P<sub>M<sub>W</sub>≥3</sub>) using 100 TM detections larger than M<sub>C</sub>. Mean probabilities for different periods are indicated by dashed lines indicating shifts in the P<sub>M<sub>W</sub>≥3</sub> baseline after 2015 and 2019. Notice the use of a shorter (50-event) window before 2013 to maintain temporal resolution in this low-seismicity-rate period.</figcaption></figure>

decrease in P18/19 reaches the lowest value of the whole study period (b = 0.9). It stays on a low level until the next seismic activity surge in the fall of P19/20. The b-value decrease coincides with increased P<sub>M<sub>W</sub>≥3</sub>. Notably, the GJES' strongest earthquakes occurred during the P18/19 late-winter-peak, with six events surpassing M<sub>L</sub>2.5 (M<sub>W</sub>2.5), the largest recording M<sub>L</sub>3.1 (M<sub>W</sub>2.9). Aside from an additional M<sub>L</sub>2.5 (M<sub>W</sub>2.5) event in the fall of 2020, magnitudes remained below M<sub>L</sub>2.2 (M<sub>W</sub>2.3) throughout the study period.

Fig. 4 illustrates the cumulative number of events across different Periods plotted on a common time axis. P19/20 and P20/21 have the highest seismicity rates, reaching 562 and 777 events per ten days in early fall. Additionally, they recorded the highest total number of events per Period, surpassing 3000 earthquakes. Compared to other Periods, they also exhibited the earliest start in late August and early September, displaying high seismicity rates for the initial four to five weeks.

Fig. 4 reveals the characteristic temporal evolution of seismic activity discussed above in greater detail. In most Periods, the decline in fall-peak seismicity rate is observed before December and, at the latest, in early January. Diverging from this characteristic pattern, seismic activity persists until April in P17/18 and until March in P18/19, leading to late-winter peaks in these Periods. Furthermore, P18/19 stands out with only a modest early-Period seismicity increase. This initial rise is then succeeded by a notable surge in mid-December, which maintains a steady seismicity rate until mid-March. As mentioned above, this particular phase of P18/19 has the lowest b-value in the entire study period.

We will return to this observation when discussing the spatial evolution of the GJES (Sec. 4.3).

### 4.2.2. Earthquake relocation and GJES fine structure

Fig. 5a shows an epicenter map of the 1142 relocated earthquakes. The earthquake cloud stretches about 9 km in an east-northeast direction (N243°E-striking) and widens to about 3.5 km in the central part. The relative relocation resulted in a hypocentral precision of ~30 m horizontally and 65 m vertically (Suppl. Material, catalog) and discloses the fine structure of the GJES in great detail. It reveals several smaller, 1-to-3-kilometer-long, elongated clusters aligned with the earthquake cloud. A small subset (≈10%) of the clusters exhibit north-northeast to south-southwest and, even rarer (<5%), northwest to southeast orientations. The cluster containing the most relocated events (202) is located at the southeast rim of the seismic cloud and shows its main activity in P18/19.

Fig. 5b displays a depth section perpendicular to the main strike of the earthquake cloud. The most striking feature is that the earthquake cloud splits into a shallow and a deeper part. The shallow part ranges from 0.5–4.0 km depth, contains the majority of the events, and covers the entire footprint of the seismic cloud. The deeper part extends from 4.4–7.4 km, and its events originate exclusively from the central-northeastern part of the seismic cloud (Fig. 5a). Also in depth section, the fine structure is resolved in more detail. In the shallow part, nearly all clusters seem to be northwest- to nearly vertically dipping, with only
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Comparison of seismic activity evolution in different Periods. Curves indicate seismic activity by the cumulative TM-detection rate and are color-coded by Period as in Figs. 5, 6, and 7. The numbers in the brackets indicate the cumulative number of TM-detections in a Period. Note: late-winter activity increases are only observed in P17/18 and P18/19 (Fig. 6a).</figcaption></figure>

a minor subset of southeast-dipping clusters. In the deeper part, however, southeast-dipping clusters appear to prevail.

The relocation result shows that the seismic cloud remains confined to the MBG. The fault and fracture orientations for this unit are known from geological fieldwork at the surface of the MBM (Bertini et al., 1985; Baggio, 1964; Guermani and Pennacchioni, 1998; Egli and Mancktelow, 2013; von Raumer and Bussy, 2004) and in the MBT (e.g., Dal Piaz and Argentieri, 2021). Typically, fault planes exhibit steep dip angles, ranging from 50° to vertical. They primarily strike northeast, longitudinal to the MBM. Southeast dips prevail on the northwestern side of the MBG, and northwest dips in the southeast. The longitudinal faults can be associated with oblique to transversal (i.e., northwest-striking) faults. The former primarily manifests dip-slip characteristics, whereas the latter possesses a more pronounced strike-slip component, especially when trending northwest (Bertini et al., 1985).

The orientation and fine structure of the seismic cloud are in excellent agreement with the observed fault geometries in the MBG, indicating a reactivation of the tectonically inherited structures by recent seismicity. This interpretation is further supported by the notable alignment of the seismic cloud with major cataclastic and mylonitic shear zones in the MBG (Fig. 5c; Baggio, 1964; Guermani and Pennacchioni, 1998). Particularly noteworthy is the alignment with a 600 m-wide shear zone mapped at the base of the Toule and Entreves Glaciers (Fig. 5c), extending northeastward for several kilometers (Dal Piaz and Argentieri, 2021; Baggio, 1964). This shear zone is likely hydraulically connected and possibly identical to FZ367, the primary inflow zone into the MBT. We return to this in Sec. 5.1.

### 4.3. Spatiotemporal evolution of the seismicity

To better explore the spatiotemporal evolution of the GJES seismicity, we zoom into the relevant Periods P15/16 to P21/22 in Fig. 6. We present the temporal evolution of the seismic activity as recorded by our TM-detections in Fig. 6a, while the spatial evolution of the high-precision relocations with depth and along-strike the profiles A–A' and B–B' of Fig. 5 is highlighted in the panels b–d, Fig. 6, respectively.

From Fig. 6b–d, we learn that seismic activity began in the shallow east-northeast part of the seismic cloud. Throughout P15/16 and P16/17, this activity stayed concentrated in that region and did not extend beyond a depth of 4.4 km. Beginning in P17/18, the seismic activity expanded west-southwest, encompassing the central 6 km of the seismic cloud along profile A–A'. Additionally, the deeper regions of the seismic cloud, ranging from 4.4 to 7.4 km in depth, became active. This deeper

seismicity persisted in subsequent Periods but was localized to several closely spaced, steeply southeast-dipping clusters in the central, north-eastern section of the seismic cloud, as illustrated in Fig. 5b. During P19/20, the seismicity began to spread beyond the central 6 km section of the seismic cloud. To the east-northeast, the seismic cloud expanded approximately 1 km, while towards the west-southwest (in the direction of the MBT), the expansion reached about 2 km and showed higher seismic activity. This seismicity footprint and pattern were observed again in P20/21. Notably, besides exhibiting the largest intra-Period footprint, the latter two Periods also show the highest seismicity rates during the study period. In P21/22, seismicity was confined mainly back to the initial central 6 km and also experienced a significant decrease in activity compared to the two preceding, highly active Periods.

Apart from the inter-Period change in the GJES' activity footprint described above, seismicity generally lacked a clear intra-Period migration pattern, dispersing across the entirety of a given Period's footprint in an apparently erratic sequence. P18/19 stands out as an exception, with most seismic activity concentrated in a narrow, east-northeast-oriented cluster located at the central south-southeastern edge of the seismic cloud (Fig. 5a). The cluster is about 2 km long and 500 m wide, extends between 2 and 3 km depth, and dips to the north-northwest at an angle of about 70° (Fig. S2 and 5). In the first half of P18/19, GJES seismic activity concentrated at the west-southwestern end of this cluster, with minimal activity elsewhere in the seismic cloud. In the second half of P18/19, the activity migrates along this cluster towards the east-northeast at a speed of about 22 m/day. This migration path featured several events with magnitudes greater than $M_L$2.0 ($M_W$2.2), marking the cluster as the primary concentration zone of $M_L \ge 2.0$ ($M_W \ge 2.2$) events within the GJSE (Figs. 6 and 5). We could derive reliable focal mechanisms for two mainevents, which both show normal faulting (Table S2). The larger one falls into the described cluster, and we could constrain the active fault plane from its alignment with the cluster orientation (Fig. 5a and b). The remarkable concentration of the GJES seismicity in the small initiation area of the described cluster during the first half of P18/19 is a unique phenomenon across all fall Periods. This concentration suggests that the fall-seismicity peak of P18/19 represents the nucleation process for the migratory seismicity observed in the later part of P18/19.

The migratory seismic activity described above is associated with the late-winter-peak of P18/19 and exhibits the lowest values observed in the high-resolution, 100-event-windowed *b*-value timeline across the study period (Fig. 6c). To validate this observation, we calculated more robust, half-year-binned *b*-values, $b_{1/2a}$, for the first and second half of
<figure><img src="image_8.png" /></figure>

**Fig. 5.** Fine structure of the GJES. (a) Epicenter map of the relatively relocated earthquakes color-coded by Period as in Figs. 4, 6, and 7 (see legend). $M_L > 2$ earthquakes are indicated by larger magnitude-scaled dots. Magenta lines denote inferred horizontal cluster orientations. Focal mechanisms of the two mainevents are indicated (lower hemisphere view; FM1: 5 February 2019, $M_L 3.1$ ($M_W 3.0$), active fault plane in red; FM2: 3 September 2020 $M_L 2.5$ ($M_W 2.5$)). Blue dashed polygons: epicentral area of P18/19 seismicity. The blue arrow indicates migration direction of events within the Migratory-Cluster (see text). Gray dashed polygon: epicentral area of the deep earthquakes (center-point coordinates: 45.880°N/7.001°E). (b) Depth section along profile B–B' indicated in (a). All earthquakes of (a) were projected to a cross-section; symbols as in (a). Magenta lines denote inferred dip orientations of clusters. (FMs rotated to back hemisphere view; depth refers to sea level.) (c) Geological map of the Italian side of the MBM, covering the area from the Pyramides Calcaires in Val Veny to the head of Val Ferret. Cataclastic and mylonitic shear zones mapped on the surface are highlighted in magenta. Glacier areas from the 1960s are highlighted in light blue. The map outline in (a) and profile A–A' are indicated (hatching illustrates 2 km distances). The MBT is illustrated by a black line with a gray rectangle indicating its 600 m-wide primary inflow zone FZ367. This map is excerpted from Baggio (1964) and is taken from Dal Piaz and Argentieri (2021) with permission from Italian Journal of Geosciences.

each Period (Fig. 6e). Notably, the seismicity during the second half-year of P18/19 again shows the lowest value ($b_{1/2} = 1.08$). In most other half-years, $b_{1/2}$ is at least 0.2 units higher, usually between 1.3 and 1.6. The only other exception to this observation is the winter half-year of P17/18, with a lower $b_{1/2} = 1.21$. The consistency in observing low values across the high-resolution (Fig. 6c) and half-year-binned (Fig. 6e) *b*-value time series suggests that distinct driving mechanisms may underlie the GJES' rare, late-winter-peak.

To isolate this distinct activity, we applied DBSCAN clustering (Esler et al., 1996; Pedregosa et al., 2011) to all relocations, effectively grouping seismic events by proximity (Suppl. Material, Sec. 4). The primary cluster, comprising 375 events, mainly encapsulated migratory events from P17/18 and P18/19. To refine this cluster, we filtered out non-migratory events, particularly those occurring post-July 2019 and pre-2018. This refinement process identified a subset of the GJES characterized by a migratory pattern along a narrow, sub-vertical, planar structure, hosting the majority of the mainevents. The structure is well aligned with the active fault plane of the largest of these mainevents (Fig. S2). Coupled with a lower *b*-value, these observations suggest that the activity within this subset is primarily tectonically driven and potentially influenced by aseismic slip (Simon et al., 2021) or earthquake-earthquake interactions (Ellsworth and Bulut, 2018). We denote this subset as the "Migratory-Cluster," visually highlight it in Fig. S2, and

categorize its events as migratory, denoted by "m" in the GJES earthquake catalog (see Sec. Data availability).

## 5. Discussion

Given its distinct characteristics, we exclude the Migratory-Cluster from analyzing the seasonal component of the GJES. Two distinctive features characterize this remaining part. Firstly, a marked annual periodicity emerged in September 2015, dominating fall seismicity in all subsequent Periods. Secondly, this seismicity in fall exhibits relatively high *b*-values and is closely linked to fault systems that act as conduits for deep groundwater circulation in the nearby MBT. These features hint towards an influence of deep groundwater circulation on the seismic activity. We will discuss this hypothesis below.

### 5.1. Hydrological observations in the Mont Blanc Tunnel

Maréchal (2000) measured the flow from FZ367 – the primary inflow zone to the MBT – between February 1996 and January 1998 (Fig. 2d). The mean discharge over this period was 196 L/s and was postulated to closely correspond to a stable long-term equilibrium 35 years after tunneling (Maréchal and Perrochet, 2001). Nevertheless, the data show a noticeable yearly variation, with a rapid increase in flow rate occurring between August and October of one year, followed by a gradual
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Spatiotemporal evolution of the GJES. Periods and their durations are color-coded as in Figs. 4, 5, and 7. (a) Ten-day-binned histogram of TM-detections mostly indicating peak seismicity in fall of a Period. As an exception, P18/19 is dominated by a seismicity peak in early 2019, including most of the GJES' mainevents ($M_L \ge 2$). (b–d) Spatiotemporal development of seismicity: (b) With depth (deep earthquakes below 4.4 km (dashed line) appear after October 2017). (c) Along A–A' (as indicated in Fig. 5a and c; small distances are closer to MBT). The along-strike migration of Migratory-Cluster seismicity in P18/19 (see text) is indicated by a light blue arrow. (d) Along B–B' (as indicated in Fig. 5a; negative values indicate distances towards north-northwest, French side). (e) Evolution of half-Period *b*-values over the study period ($b_{1/2a}$) that separately capture the effect of the autumn and late-winter seismicity peaks. P17/18 and P18/19 stand out exhibiting the lowest $b_{1/2a}$ values of the study period during their high late-winter-peak seismicity.</figcaption></figure>

decrease until the following August. The amplitude of this fluctuation varied from year to year and reached its maximum of 30 L/s in the second year of the measurement. The water's electrical conductivity declined during the flow-rate increases (Fig. 2d), suggesting dilution by recent, low-mineralized surface waters (Maréchal, 2000). In 1996, the inflow temperature was 7–8 °C (Maréchal, 1998), about 5 °C lower than when piercing FZ367 in December 1960. These low and persistently dropping inflow temperatures also indicate a rapid discharge of cold snow and glacier meltwater towards the level of the MBT. This interpretation is further supported by isotope analyses indicating infiltration altitudes consistent with the topographic relief above the tunnel and short residence times (Maréchal and Etcheverry, 2003). Tracer tests in 1988 (Dubois, 1992) and 1997 (Maréchal, 1998) injected Uranin and Eosin into the bergschrund of Toule-Glacier, located at an elevation of 3300 m near Pointe-Helbronner, upstream of a major cataclastic fracture zone (Fig. 5c). These tests confirmed the water circulation between the surface zone and FZ367 in the tunnel. Due to technical issues, the transit times of the tracer through the MBG are only roughly known in both cases (less than 108 days and between 103 and 138 days, respectively).

## 5.2. Hydraulic diffusion modeling

### 5.2.1. Modelling onset of MBT inflow by 1D hydraulic diffusion

We aim to improve the transit-time estimate by utilizing a recently updated meteorological and snow-cover model of the French-Alps (S2M; Vernay et al., 2022). The S2M model provides simulated meteorological, hydrological, and snow-pack parameters for various slope inclinations and exposure directions at 300-meter-altitude bins for the Alps (including the MBM). It covers the years 1958–2021, calibrated with 435 meteorological observation sites from the Western Alps and their surrounding areas. In Fig. 2d, we compare the sum of the S2M runoff and drainage (hereafter “S2M-Surface-Runoff”) to the FZ367 inflow measurements of 1996 and 1997. We select the 2700 m elevation bin, which covers the surface exposure of the N225°E-striking cataclastic fracture zone situated downstream of Toule-Glacier’s bergschrund. The two timelines show good agreement in peak amplitudes and trends, exhibiting higher S2M-Surface-Runoffs plus potential infiltration and tunnel inflows in 1997. In both years, the S2M-Surface-Runoff onset precedes the onset of increased tunnel inflow by 69.5±1.5 days. This value is lower than the tracer-test estimates mentioned above, however, we have higher confidence in our transit-time estimates because they rely on cal-
<figure><img src="image_10.png" /></figure>

**Fig. 7.** Comparison of the GJES seismicity depth development in a 1D hydraulic diffusion model. Time is relative to the onset of S2M-Surface-Runoff in a Period. Depth is relative to the mean elevation of the surface outcrop of the primary inflow zone (FZ367) at the surface of the MBT (2700 m). Pore-pressure fronts for three hydraulic diffusivities (*D*) are displayed as dashed gray curves. Earthquakes are denoted by dots, color-coded by their Period as in Figs. 4, 5, and 6, with error bars mainly indicating the uncertainty in infiltration-zone elevation and absolute location. The earthquakes of the Migratory-Cluster have been removed for this analysis. The gray square indicates the onset of inflow from FZ367 to the MBM. We find a remarkable agreement between hydraulic model and observation by assuming that shallow earthquakes (less than 4.4 km) are triggered by surface runoff from the previous year (approximately 1.5 years earlier), deep earthquakes (depth greater than 4.4 km) by surface runoff from two years prior (approximately 2.5 years earlier), and MBT inflow by surface runoff from the same year (approximately 69.5 days earlier).

ibrated S2M-Surface-Runoffs and the closeness of the obtained results in both observation years.

Following Roeloffs (1988), we model the vertical propagation of the pressure wave between the surface and the tunnel level as a 1D-diffusion process in a homogeneous, porous, isotropic half-space with hydraulic diffusivity, *D*. In this simplified model, the transit time (*t*[s]) corresponds to the time difference between a step increase in meltwater level at the surface and the onset of increased tunnel inflow. Neglecting the vadose zone thickness and considering the elevation difference between the surface inflow zone and the tunnel (Δ*h* = 1400 ± 200 m), we derive a hydraulic diffusivity value of *D* = Δ*h*²/(4π*t*) = 0.026⁺⁰.⁰⁰⁰⁸⁻⁰.⁰⁰⁰⁷ m²/s. This value matches previous studies modeling the rock-temperature anomaly and temporal decrease of flow from FZ367 after tunneling observed in the MBT (Maréchal, 2000).

### 5.2.2. Model seismicity distribution by simple hydraulic diffusion

Encouraged by these results, we use the same approach to test the hypothesis that the spatiotemporal evolution of the GJES seismicity (after removal of the Migratory-Cluster) is driven by meltwater. Following the argumentation of Shapiro et al. (1997), we assume that the source regions of the GJES are - at least locally - critically stressed, and that small pore-pressure increases can reduce effective stress on favorably oriented fractures to levels promoting rupture initiation. For a given hydraulic diffusivity, we can predict the depth development with time of a pore-pressure front initiating from a step-increase in surface meltwater, using: Δh = √(4πDi) (Roeloffs, 1988). Here, we define *t* as the time elapsed since the onset of S2M-Surface-Runoff in the 2700 m altitude bin of a given year. Further, we define *h* as the depth below the average surface elevation of the MBM over the GJES footprint (*h*₀ = 2980 m asl;

Suppl. Material) derived from the SRTM digital elevation model (SRTM, 2013).

In Fig. 7, we compare the development of the surface-melt-induced pore-pressure-increase front over time and depth for different hydraulic diffusivities to the spatiotemporal development of the GJES seismicity. To select an appropriate *D* range, we choose the value obtained from the MBT inflow analysis as a lower limit, recognizing that significant delays can occur between the onset of S2M-Surface-Runoff and its subsequent percolation into the relevant fault systems (Maréchal, 1998). We limit the range on the upper end by considering results for comparable environments from scientific literature (e.g., Costain and Bollinger, 2010).

Using the reference datums introduced above, we plot the MBT inflow observation as a square and GJES earthquakes as dots colored by Period, with error bars indicating their uncertainty. We determine *t* from the onset of S2M surface runoff in the *n*-th Period preceding the earthquake's Period. Here, *n* can be any positive integer, including 0. This means that the reference year for runoff can be the same as the earthquake's Period or any number of Periods before. We explore the fit of the observed seismicity to the predicted pore-pressure-front development for different values of *n*.

We obtain a remarkable alignment, with most earthquakes falling below the pore-pressure fronts with $D = 0.08 - 0.1$ m²/s, using $n = 1$ for shallow earthquakes (source depths <4.4 km) and $n = 2$ for deep earthquakes. The larger delay used for the deep earthquakes is a reasonable assumption from a physical point of view. Yet, it is also supported by the observation that the onset of the GJES' deep seismicity lags behind that of the shallow seismicity by two years (Fig. 6c). However, Fig. 8 suggests that P15/16 is best explained with $n = 0$, and hydraulic modeling using $D \approx 0.2$ m²/s supports this value (Fig. S11a). These differences in
the inversion results are likely due to hydraulic property heterogeneities in fault zones (see Suppl. Material, Sec. 9), as observed elsewhere (e.g., Ross et al., 2020).

The massive initial flow from FZ367 and its rapid decline after MBT-excavation suggest that the tunnel serves as a drainage channel, expanding a hydraulic-head depression zone along FZ367 (Maréchal and Perrochet, 2001). Following Maréchal et al. (2014), the depression-zone width (measured from the tunnel) can be estimated by assuming a vertical planar aquifer with homogeneous hydraulic properties and a constant surface infiltration rate and should have reached a close-to-equilibrium state today, more than 60 years after MBT excavation. This quasi-static hydraulic head profile impacts effective normal stress and, consequently, fault stability along FZ367. Faults near the MBT, where hydraulic heads are low – reaching the tunnel elevation - should exhibit greater stability. Those at greater distances, in areas with larger hydraulic heads, should be closer to instability and can, therefore, be more easily triggered by small seasonal head changes. To conservatively estimate the surface infiltration rate, we use the S2M-Surface-Runoff, considering the fractional contribution of each altitude bin to a three-kilometer-wide corridor around the MBT (Suppl. Material, Fig. S4). Averageing the S2M-Surface-Runoff between 1961 (piercing of FZ367) and 2021, we finally arrive at $1457 \pm 234$ mm/year (Suppl. Material, Sec. 6). Assuming this infiltration rate and the geometrical parameters from above, the model of Maréchal et al. (2014) provides a depression-zone-half-width estimate of $4457^{+1103}_{-1111}$ m. Notably, the observed minimum horizontal distance of GJES earthquakes to the MBT ($\approx$ 3300 m) agrees reasonably well with this estimate (Fig. 1a), adding to the hypothesis that hydraulic effects strongly influence GJES seismicity.

### 5.3. Comparison of seismicity rates and Heat Wave Magnitude Index

To further investigate the potential delay between the onset of S2M-Surface-Runoff and seismicity, we have examined various meteorological (Station-Ferrachet, 2022; Hall and Riggs, 2021; Swiss Permafrost Monitoring Network (PERMOS), 2023; Weber et al., 2019; Cornes et al., 2018), hydrological (Station-Ferrachet, 2022), and cryospheric (Swiss Permafrost Monitoring Network (PERMOS), 2023; Mondardini et al., 2021; GLAMOS, 2023; GLIMS and NSIDC, 2005, updated 2018; Pfefer et al., 2014) datasets. These datasets collectively reveal a sustained long-term trend spanning several decades, indicative of rising average temperatures. Moreover, datasets with adequate temporal resolution usually exhibit a solid cyclic annual pattern. However, no discernible deviations from these long-term trends that could explain the sudden onset of the annual periodicity in GJES seismicity in September 2015 could be found. On the other hand, the MBM has experienced increasingly frequent and severe heat waves over recent decades (Cremonese et al., 2019). Notably, those in 2003 and 2015 profoundly affected the MBM's high-altitude areas and triggered numerous rockfalls and rock pillar collapses, primarily due to the retreat of permafrost (Ravanel et al., 2017; Viani et al., 2018). Such events may significantly alter the hydrological conditions in the shallow subsurface and change meltwater percolation pathways into and through the underground. Yet, the available datasets we examined seem not to adequately capture these events.

To improve this situation, we build on Corona-Lozada et al. (2019) and utilize the Heat Wave Magnitude Index (HWI) proposed by Russo et al. (2014) as an indicator for the climate-driven stress on high-altitude areas of the MBM. Modifying the original HWI calculation to suit our requirements, we define a heat wave as a period lasting a minimum of three consecutive days during which the daily maximum temperatures exceed the 90th-percentile of the expected values derived from the reference period 1981 to 2010. We quantify the magnitude of a heat wave by summing its daily maximum air temperatures exceeding 0°C. Eventually, the HWI for a given year is determined as the highest magnitude observed within that year.

For 1959 to 2021, HWIs are computed for each S2M altitude bin exclusively using S2M data. These values are then aggregated, considering the fractional contribution of each altitude bin to the GJES footprint (Fig. S5 and S6) to compute a HWI representative of melt-induced forcing on the GJES. Post-2021, in the absence of S2M data, we used daily maximum air temperature recordings from the Ferrachet meteorological station (Station-Ferrachet, 2022), located at 2290 m elevation in Val-Ferret, Italy, within the GJES footprint. During this period, we focus solely on the S2M's 2100 m elevation bin to estimate expected temperatures from the reference period. The resulting increase in HWI during this period aligns well with reports of extreme retreats of MBM and Swiss glaciers in 2022 and 2023 (Swiss Commission for Cryosphere observation (SCC), 2023; Berthier et al., 2023).

In Fig. 8, we juxtapose the yearly HWI values with indicators for the GJES seismic activity in a Period. These indicators are the seismic energy release (Benioff-strain; Mignan, 2011) and the earthquake count in a Period, both derived from our TM-detections above the completeness magnitude ($M_C = M_W0.7$). Further, we exclusively focus on shallow events to avoid analyzing superimposed effects of melt events from different years (Sec. 5.2.2). We included estimated indicators for P22/23 and P23/24, as they were not yet TM-analyzed at the time of writing. For this, we scaled up the numbers of their routine catalog events by the ratio between catalog events and TM-detections observed in P19/20 and P20/21, which had similarly high catalog event rates as P22/23 and P23/24.

The time series of the HWI and seismicity indicators are illustrated in Fig. 8c, where they have been normalized to facilitate comparison. In this normalized view, the two seismicity indicators demonstrate almost identical values. Comparing HWI with the seismicity indicators post-July 2014, we observe striking similarities, including peaks in 2015, 2019, and 2023 and lows in 2017 and 2021. However, before July 2014, the HWI exhibited a significant peak in 2011 that is not reflected in the seismicity indicators. The onset of the seismic response after that date could be related to a substantial increase in high-altitude glacier ablation in the MBM since 2015, which is documented by the positive degree day timeline in Fig. 8b.

To delve deeper into this relationship, we conduct a cross-correlation analysis between the HWI and the seismicity indicators, focusing exclusively on the period after July 2014. We set pre-2014 HWI values to a constant value. The analysis reveals significant cross-correlation at 0.5 and 1.5 years lag times, while lag times shorter or longer than that exhibit minimal correlation (Fig. 8d). These findings imply that seismic activities in the shallow part of the GJES tend to respond to changes in the HWI within the same or the following year after July 2014. Note that snow accumulation in winter and snow/ice melt in summer can respectively increase or decrease the likelihood of failure in a normal faulting regime due to instantaneous Coulomb Failure Stress change (Heki, 2003; Roeloffs, 1988). Since we observe this faulting style in our mainevents, this phenomenon might influence the overall scenario. However, the peak snow/ice load in the MBM happens in spring and cannot account for the rise in seismic activity in fall. Furthermore, thermoelastic effects from cold melt-water infiltration may align with the pore-pressure effects and contribute to the observed seismic response (e.g., De Simone et al., 2013). Given the low-temperature anomaly around the GJES-hosting fault zone, documented in the MBT, only small temperature variations are expected during this process.

## 6. Conclusions

Our high-resolution microseismic analysis unveiled the spatiotemporal seismicity pattern of the GJES from November 2006 to October 2022 in great detail. Before September 2015, the sequence exhibited low activity. From September 2015, a significant annual periodicity emerged, showing pronounced fall-seismicity peaks in each Period. Our high-precision relocations reveal that the seismicity is hosted on a dominant fault zone in the MBG outcropping in the nearby MBT, where
<figure><img src="image_11.png" /><figcaption>**Fig. 8.** Comparison of GJES seismicity and the annual Heat Wave Magnitude Index (HWI): (a) 30-day-binned histogram of shallow TM-detections without Migratory-Cluster (blue) and corresponding cumulative event rate (black line). (b) Yearly sum of positive degree days (PDD; Huber et al. e.g., 2024), calculated for each S2M altitude bin above an average glacier altitude of 3000 m using S2M data. PDDs serve as a proxy for glacier ablation (Braithwaite, 1985). The PDD timeline highlights a notable increase in high-altitude glacier melt starting in 2015 in the MBM region, supporting the hypothesis that the onset of the GJES in 2015 is linked to climate change-induced increases in meltwater volumes and alterations in flow paths. (c) Seismic energy release (Benioff-strain; green) and number of events (blue) per Period, plotted in the middle of each Period. Both seismicity indicators were extrapolated for P22/23 and P23/24 (see text). Heat Wave Magnitude Index (HWI; red and orange) calculated from the S2M dataset (see text), plotted in the middle of each year. HWI of the last three years calculated using data of Station-Ferrachet (2022) (orange). The gray vertical bar indicates the time range of a dramatic rearrangement of subsurface melt-water flow paths in P14/15. Before 2014, the HWI calculated from S2M data was muted to a constant value (see text). (d) Normalized cross-correlation between the seismicity indicators and HWI. Strong correlations at lag times of 0.5 or 1.5 years suggest that elevated temperatures occurring half or one-and-a-half years earlier promote GJES seismicity.</figcaption></figure>

water temperature, electrical conductivity and isotope data suggest that groundwater inflow is dominated by young surface meltwater. Modeling meltwater infiltration by a simple 1D hydraulic diffusion equation using high-altitude runoff onset times from the S2M meteorological snow-pack model, we confirm that most of the GJES seismicity can be considered meltwater-induced. A second minor component of the seismicity shows a migratory pattern along a narrow, sub-vertical, planar structure, hosting the majority of the mainevents. It aligns with the active fault plane of the largest of these mainevents. Coupled with a low *b*-value, these observations suggest that the activity within this subset is driven by a different mechanism and potentially influenced by aseismic slip or earthquake-earthquake interactions.

Our analysis suggests that the onset of increased and periodic seismicity in fall 2015 can be attributed to the rising impact of climate-change-driven heat waves on the high-altitude cryosphere of the MBM. Permafrost and glacier retreat trigger numerous rockfalls and rock pillar collapses that can modify near-surface meltwater infiltration pathways, allowing these waters to induce pore-pressure changes and trigger seismicity in new source regions. The 2015 heat wave profoundly affected the MBM's high-altitude areas in this way (Ravanel et al., 2017; Viani et al., 2018). Our hydraulic modeling and HWI analysis indicate that the melt events from the same or previous year can explain seismicity surges in the GJES source region. Therefore, the 2015 heat wave likely triggered the change in the GJES activity in September 2015.

We find that during Periods of high meltwater-driven seismicity, the seismic hazard can increase by up to nearly four orders of magnitude compared to the pre-2015 level, indicating high daily occurrence prob-

abilities for $M_W \ge 3$ earthquakes ($P_{M_W \ge 3} > 0.1$). Daily probabilities for earthquakes with higher target magnitudes exhibit similar behavior, albeit generally decreasing by a factor of ten with every unit increase in target magnitude. However, these probabilities are expected to diminish as they approach an unknown maximum possible magnitude ($M_{max}$) for the GJES. In the Swiss Seismic Hazard Model (SED, 2014), $M_{max}$ is modeled as a distribution with a mean of $M_W$ 6.4 and a standard deviation of 0.84. The observed Frequency-Magnitude Distribution tapering above $M_W = 1.5$ in post-2015 seismicity (Suppl. Material, Sec. 8) could suggest a reduced probability of larger-magnitude earthquakes when fitted by a tapered Gutenberg-Richter law with a sufficiently small $M_{max}$. However, neither the seismic hazard model nor the region's past seismicity support this conclusion. Consequently, the Frequency-Magnitude Distribution tapering is more likely to reflect the short observation period (since 2015) and indicates a current absence of still-expected larger-magnitude earthquakes. For the MBM, which is part of Zone 4 in the Swiss Hazard model, a decision tree with three equally weighted $M_{max}$ values (6.54, 6.73, 7.18) is employed. These values indicate that $P_{M_W \ge 5}$ should still be a reasonable indicator for STSH. That these observed high $P_{M_W \ge 3}$ values are not unrealistic for the GJES, is documented by two $M_L$ 3.1 ($M_W$ 2.9)/$M_L$ 2.9 ($M_W$ 2.8) earthquakes in P18/19, which were likely triggered by the preceding seasonal activity. Additionally, $P_{M_W \ge 4}$ earthquakes have occurred in close vicinity of the MBM in the past (Fig. 1, Suppl. Material, Sec. 2).

Consequently, our research indicates climate change can significantly increase the local seismic hazard in alpine regions. This phenomenon may also occur in other glaciated areas worldwide. As a result,
there is a need to better quantify the associated seismic risk for affected
alpine communities.

CRediT authorship contribution statement

**Verena Simon:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Toni Kraft:** Writing – review & editing, Writing – original draft, Software, Validation, Formal analysis, Methodology, Supervision, Project administration, Investigation, Funding acquisition, Conceptualization.
**Jean-Christophe Maréchal:** Writing – review & editing, Formal analysis, Validation, Investigation. **Agnès Helmstetter:** Writing – review & editing, Investigation, Validation. **Tobias Diehl:** Writing – review & editing, Investigation, Formal analysis.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

We express our sincere appreciation to Jean-Pierre Salamin, President of L'association Pro Forteresse; Eric Thioliere, President of the Consorts de la Montagne de Blatière; and Luca Glarey, owner of Hotel Lavachey, for generously permitting the installation of seismic stations on their properties. Additionally, we are grateful for the support of the Companie du Mont-Blanc, Marco Vagliasindi, Christian Scherrer, Robert Tanner, and Verónica Antunes for their assistance during the installation and maintenance processes. We would like to sincerely thank Anne Paul and Mickaël Langlais from the Institut des Sciences de la Terre (ISTerre) and Florent Gimbert and Ugo Nanni from the Institut des Géosciences de l'Environnement (IGE) Grenoble, France, for generously sharing seismic logical data. Special thanks to Roman Racine and the SED network group for their support with data handling and archiving. Additionally, we are grateful to Peter Molnar for his assistance in acquiring MeteoSwiss gridded meteorological datasets. We extend our gratitude to Philippe Roth and Stefan Wiemer for their insightful input and discussions regarding the study. Funding for Verena Simon was provided by the Swiss National Science Foundation (SNF), Grant 200021_188615, as well as the projects SIAMIS and GEOBEST2020+ funded by the Swiss Federal Office of Energy.

Appendix A. Supplementary material

Supplementary material related to this article can be found online at
https://doi.org/10.1016/j.epsl.2025.119372.

Data availability

The data supporting the findings of this study is openly accessi-
ble and can be retrieved from the ETH Zürich Research Collection via
the following DOI: https://doi.org/10.3929/ethz-b-000663486. Seis-
mological data used in this study were obtained from the SED data
archive (https://doi.org/10.12686/SED/NETWORKS/CH, https://doi.
org/10.12686/SED/NETWORKS/8D), the INGV data archive (https://
doi.org/10.13127/SD/X0FXNH7QFY), and the RESIF data archive
(https://doi.org/10.15778/RESIF.8C2019, https://doi.org/10.15778/
resif.zo2018). Meteorological, glaciological, and hydrological datasets
include the S2M reanalysis for the French Alps (https://essd.copernicus.
org/articles/14/1707/2022/) and air temperature recordings from the
Valle d'Aosta monitoring network (https://presidi2.regione.vda.it/str-
dataview station/1220). Detailed links to all networks, stations, and
datasets are provided in the Suppl. Material. Software written for the
analysis can be requested from the authors.

References

Baggio, P., 1964. Contributo alla conoscenza geologica del versante italiano del Monte Bianco. Atti Ist. Veneto Sci. Lett. Arti 122, 293-325.

Ben-Zion, Y., Allam, A.A., 2013. Seasonal thermoelastic strain and postseismic effects in Parkfield borehole dilatometers. Earth Planet. Sci. Lett. 379, 120–126. https://doi.org/10.1016/j.epsl.2013.08.024.

Bergemann, C.A., Gnos, E., Whitehouse, M.J., 2019. Insights into the tectonic history of the Western Alps through dating of fissure monazite in the Mont Blanc and Aiguilles Rouges Massifs. Tectonophysics 750, 203–212. https://doi.org/10.1016/j.tecto.2018.11.013.

Berthier, E., Vincent, C., Six, D., 2023. Exceptional thinning through the entire altitudinal range of Mont-Blanc glaciers during the 2021/22 mass balance year. J. Glaciol., 1–6. https://doi.org/10.1017/jog.2023.100.

Bertini, G., Marcucci, M., Nevini, R., Passerini, P., Sguazzoni, G., 1985. Patterns of faulting in the Mont Blanc granite. Tectonophysics 111, 65–106. https://doi.org/10.1016/0040-1951(85)90066-6.

Bettinelli, P., Avouac, J.P., Flouzat, M., Bollinger, L., Ramillien, G., Rajaure, S., Sapkota, S., 2008. Seasonal variations of seismicity and geodetic strain in the Himalaya induced by surface hydrology. Earth Planet. Sci. Lett. 266, 332–344. https://doi.org/10.1016/j.epsl.2007.11.021.

Braithwaite, R.J., 1985. Calculation of degree-days for glacier-climate research. Z. Gletsch.kd. Glazialgeol. 20, 1–8.

Christiansen, L.B., Hurwitz, S., Ingebritsen, S.E., 2007. Annual modulation of seismicity along the San Andreas Fault near Parkfield, CA. Geophys. Res. Lett. 34, L04306. https://doi.org/10.1029/2006GL028634.

Cornes, R.C., van der Schrier, G., van den Besselaar, E.J.M., Jones, P.D., 2018. An ensemble version of the E-OBS temperature and precipitation data sets. J. Geophys. Res., Atmos. 123, 9391–9409. https://doi.org/10.1029/2017JD028200.

Corona-Lozada, M., Morin, S., Choler, P., 2019. Drought offsets the positive effect of summer heat waves on the canopy greenness of mountain grasslands. Agric. For. Meteorol. 276–277, 107617. https://doi.org/10.1016/j.agrformet.2019.107617.

Costain, J.K., Bollinger, G.A., 2010. Review: research results in hydroseismicity from 1987 to 2009. Bull. Seismol. Soc. Am. 100, 1841–1858. https://doi.org/10.1785/0120090288.

Cremonese, E., Carlson, B., Filippa, G., Pogliotti, P., Alvarez, I., Fosson, J.P., Ravanel, L., Delestrade, A., 2019. Changements climatiques dans le massif du Mont-Blanc et impacts sur les activités humaines. Technical Report November. Rédigé dans le cadre du projet AdaPT Mont-Blanc financé par le Programme européen de coopération territoriale Alcotra Italie-France 2014-2020. Online Report.

Dal Piaz, G.V., Argentieri, A., 2021. 150 years of plans, geological survey and drilling for the Fréjus to Mont Blanc tunnels across the Alpine chain: an historical review. Ital. J. Geosci. 140, 169–204. https://doi.org/10.3301/IGJ.2020.29.

De Simone, S., Vilarrasa, V., Carrera, J., Alcolea, A., Meier, P., 2013. Thermal coupling may control mechanical stability of geothermal reservoirs during cold water injection. Phys. Chem. Earth Parts A/B/C 64, 117–126. https://doi.org/10.1016/j.pce.2013.01.001.

Diehl, T., Clinton, J., Cauzzi, C., Kraft, T., Kästli, P., Deichmann, N., Massin, F., Grigoli, F., Molinari, I., Böse, M., Hobiger, M., Haslinger, F., Fäh, D., Wiemer, S., 2021. Earthquakes in Switzerland and surrounding regions during 2017 and 2018. Swiss J. Geosci. 114, 4. https://doi.org/10.1186/s00015-020-00382-2.

Diehl, T., Kraft, T., Kissling, E., Wiemer, S., 2017. The induced earthquake sequence related to the St. Gallen deep geothermal project (Switzerland): fault reactivation and fluid interactions imaged by microseismicity. J. Geophys. Res., Solid Earth 122, 7272–7290. https://doi.org/10.1002/2017JB014473.

Dubois, J.D., 1992. Typologie des aquifères du cristallin. EPFL.

Edwards, B., Kraft, T., Cauzzi, C., Kästli, P., Wiemer, S., 2015. Seismic monitoring and analysis of deep geothermal projects in St Gallen and Basel, Switzerland. Geophys. J. Int. 201, 1022–1039. https://doi.org/10.1093/gji/ggv059.

Egli, D., Mancktelow, N., 2013. The structural history of the Mont Blanc massif with regard to models for its recent exhumation. Swiss J. Geosci. 106, 469–489. https://doi.org/10.1007/s00015-013-0153-5.

Ellsworth, W.L., Bulut, F., 2018. Nucleation of the 1999 Izmit earthquake by a triggered cascade of foreshocks. Nat. Geosci. 11, 531–535. https://doi.org/10.1038/s41561-018-0145-1.

Ester, M., Kriegel, H.P., Sander, J., Xu, X., 1996. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. AAAI Press. https://aaai.org/papers/kdd96-037-a-density-based-algorithm-for-discovering-clusters-in-large-spatial-databases-with-noise/.

Gao, S.S., Silver, P.G., Linde, A.T., Sacks, I.S., 2000. Annual modulation of triggered seismicity following the 1992 Landers earthquake in California. Nature 406, 500–504. https://doi.org/10.1038/35020045.

GLAMOS, 2023. Swiss Glacier Volume Change, Release 2023, Glacier Monitoring Switzerland. https://doi.org/10.18750/volumechange.2023.r2023.

GLIMS and NSIDC, 2005. Global Land Ice Measurements from Space Glacier Database. Updated 2018. https://nsidc.org/data/nsidc-0272/versions/1.

Guermani, A., Pennacchioni, G., 1998. Brittle precursors of plastic deformation in a granite: an example from the Mont Blanc massif (Helvetic, western Alps). J. Struct. Geol. 20, 135–148. https://doi.org/10.1016/S0191-8141(97)00080-1.
Gulia, L., Tormann, T., Wiemer, S., Herrmann, M., Seif, S., 2016. Short-term probabilistic earthquake risk assessment considering time-dependent b values. Geophys. Res. Lett. 43, 1100–1108. https://doi.org/10.1002/2015GL066686.

Gutenberg, B., Richter, C.F., 1944. Frequency of earthquakes in California. Bull. Seismol. Soc. Am. 34, 185–188. https://doi.org/10.1785/bssa0340040185.

Hainzl, S., Kraft, T., Wassermann, J., Igel, H., Schmedes, E., 2006. Evidence for rainfall-triggered earthquake activity. Geophys. Res. Lett. 33. https://doi.org/10.1029/2006GL027642.

Hall, D.K., Riggs, G.A., 2021. MODIS/Terra Snow Cover Monthly L3 Global 0.05Deg CMG. Version 61. https://doi.org/10.5067/MODIS/MOD10CM.061.

Heki, K., 2003. Snow load and seasonal variation of earthquake occurrence in Japan. Earth Planet. Sci. Lett. 207, 159–164. https://doi.org/10.1016/S0012-821X(02)01148-2.

Herrmann, M., Kraft, T., Tormann, T., Scarabello, L., Wiemer, S., 2019. A consistent high-resolution catalog of induced seismicity in Basel based on matched filter detection and tailored post-processing. J. Geophys. Res., Solid Earth 124, 8449–8477. https://doi.org/10.1029/2019JB017468.

Huber, C.J., Eichler, A., Mattea, E., Brütsch, S., Jenk, T.M., Gabrieli, J., Barbante, C., Schwikowski, M., 2024. High-altitude glacier archives lost due to climate change-related melting. Nat. Geosci. 17, 110–113. https://doi.org/10.1038/s41561-023-01366-1.

Husen, S., Bachmann, C., Giardini, D., 2007. Locally triggered seismicity in the central Swiss Alps following the large rainfall event of August 2005. Geophys. J. Int. 171, 1126–1134. https://doi.org/10.1111/j.1365-246X.2007.03561.x.

Huss, M., Hock, R., 2018. Global-scale hydrological response to future glacier mass loss. Nat. Clim. Change 8, 135–140. https://doi.org/10.1038/s41558-017-0049-x.

Istituto Nazionale di Geofisica e Vulcanologia (INGV), 2005. Rete Sismica Nazionale (RSN) [Data set]. https://doi.org/10.13127/SD/X0FXNH7QFY.

Johnson, C.W., Fu, Y., Bürgmann, R., 2020. Hydrothermal modulation of stress and seismicity on shallow faults in southern Alaska. Earth Planet. Sci. Lett. 530, 115904. https://doi.org/10.1016/j.epsl.2019.115904.

Johnson, C.W., Totten, E.J., Bürgmann, R., 2016. Depth migration of seasonally induced seismicity at The Geysers geothermal field. Geophys. Res. Lett. 43, 6196–6204. https://doi.org/10.1002/2016GL069546.

Maréchal, J.C., 1998. Les circulations d'eau dans les massifs cristallins alpins et leurs relations avec les ouvrages souterrains. Ph.D. thesis. Ecole Polytechnique Fédérale de Lausanne (EPFL). https://theses.hal.science/tel-00454009.

Maréchal, J.C., 2000. Massif du Mont-Blanc: identification d'une structure aquifère ma- jeure. Houille Blanche, 78–82. https://doi.org/10.1051/lhb/2000064.

Maréchal, J.C., Etcheverry, D., 2003. The use of ³H and ¹⁸O tracers to characterize water inflows in Alpine tunnels. Appl. Geochem. 18, 339–351. https://doi.org/10.1016/S0883-2927(02)00101-4.

Maréchal, J.C., Perrochet, P., Tacher, L., 1999. Long-term simulations of thermal and hydraulic characteristics in a mountain massif: the Mont Blanc case study, French and Italian Alps. Hydrogeol. J. 7, 341–354. https://doi.org/10.1007/s100400050207.

Maréchal, J.C., Lanini, S., Aunay, B., Perrochet, P., 2014. Analytical solution for modeling discharge into a tunnel drilled in a heterogeneous unconfined aquifer. Groundwater 52, 597–605. https://doi.org/10.1111/gwat.12087.

Maréchal, J.C., Perrochet, P., 2001. Theoretical relation between water flow rate in a vertical fracture and rock temperature in the surrounding massif. Earth Planet. Sci. Lett. 194, 213–219. https://doi.org/10.1016/S0012-821X(01)00551-9.

McGuire, B., 2022. Earthquakes and volcanism in a changing climate. In: Shroder, J.F. (Ed.), Treatise on Geomorphology, vol. 2. Academic Press, pp. 645–670. https://doi.org/10.1016/B978-0-12-818234-5.00124-3.

Mignan, A., 2011. Retrospective on the Accelerating Seismic Release (ASR) hypothesis: Controversy and new horizons. Tectonophysics 505, 1–16. https://doi.org/10.1016/j.tecto.2011.03.010.

Mondardini, L., Perret, P., Frasca, M., Gottardelli, S., Troilo, F., 2021. Local variability of small Alpine glaciers: Thoula Glacier geodetic mass balance reconstruction (1991-2020) and analysis of volumetric variations. Geogr. Fis. Din. Quat. 44, 29–38. https://doi.org/10.4461/GFDQ.2021.44.3.

Montgomery-Brown, E.K., Shelly, D.R., Hsieh, P.A., 2019. Snowmelt-triggered earthquake swarms at the margin of Long Valley Caldera, California. Geophys. Res. Lett. 46, 3698–3705. https://doi.org/10.1029/2019GL082254.

Omori, F., 1902. Annual and diurnal variations of seismic frequency in Japan. In: Publications of the Earthquake Investigation Committee in Foreign Languages, vol. 8, pp. 1–94.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Courneaupeau, D., Brucher, M., Perrot, M., Duchesnay, E., 2011. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830. https://www.jmlr.org/papers/v12/pedregosa11a.html.

Pfeffer, W.T., Arendt, A.A., Bliss, A., Bolch, T., Cogley, J.G., Gardner, A.S., Hagen, J.O., Hock, R., Kaser, G., Kienholz, C., et al., 2014. The Randolph Glacier Inventory: a globally complete inventory of glaciers. J. Glaciol. 60, 537–552. https://doi.org/10.3189/2014JoG13J176.

von Raumer, J.F., Bussy, F., 2004. Mont Blanc and Aiguilles Rouges: Geology of Their Polymetamorphic Basement (External Massifs, Western Alps, France-Switzerland), vol. 42. Université de Lausanne.

Ravanel, L., Magnin, F., Deline, P., 2017. Impacts of the 2003 and 2015 summer heatwaves on permafrost-affected rock-walls in the Mont Blanc massif. Sci. Total Environ. 609, 132–143. https://doi.org/10.1016/j.scitotenv.2017.07.055.

Roeloffs, E.A., 1988. Fault stability changes induced beneath a reservoir with cyclic variations in water level. J. Geophys. Res., Solid Earth 93, 2107–2124. https://doi.org/10.1029/JB093i03p02107.

Ross, Z.E., Cochran, E.S., Trugman, D.T., Smith, J.D., 2020. 3D fault architecture controls the dynamism of earthquake swarms. Science 368, 1357–1361. https://doi.org/10.1126/science.abb0779.

Russo, S., Dosio, A., Graversen, R.G., Sillmann, J., Carrao, H., Dunbar, M.B., Singleton, A., Montagna, P., Barbola, P., Vogt, J.V., 2014. Magnitude of extreme heat waves in present climate and their projection in a warming world. J. Geophys. Res., Atmos. 119, 12,500–12,512. https://doi.org/10.1002/2014JD022098.

Shapiro, S.A., Huenges, E., Borm, G., 1997. Estimating the crust permeability from fluid-injection-induced seismic emission at the KTB site. Geophys. J. Int. 131, F15–F18. https://doi.org/10.1111/j.1365-246X.1997.tb01215.x.

Simon, V., Kraft, T., Diehl, T., Tormann, T., 2021. Possible precursory slow-slip to two M₃ mainvents of the Diemtigen microearthquake sequence, Switzerland. Geophys. Res. Lett. 48. https://doi.org/10.1029/2021GL093783.

Smirnov, V.B., Srinagesh, D., Ponomarev, A.V., Chadha, R., Mikhailov, V.O., Potanina, M.G., Kartashov, I.M., Stroganova, S.M., 2017. The behavior of seasonal variations in induced seismicity in the Koyna-Warna region, Western Asia. Izv. Phys. Solid Earth 53, 530–539. https://doi.org/10.1134/S1069351317030119.

SRTM, 2013. NASA shuttle radar topography mission (SRTM) (2013). Shuttle radar topography mission (SRTM) global. Distributed by OpenTopography. https://doi.org/10.5069/G9445JDF. (Accessed 4 March 2024).

Station-Ferrachet, 2022. Dati osservati del Centro Funzionale Regione Autonoma Valle d'Aosta: temperature, snow height and rain measured at station Courmayeur Ferrachet. presidi2.regione.vda.it/strdataview station/1220.

Swiss Commission for Cryosphere observation (SCC), 2023. Two catastrophic years obliterated 10% of Swiss glacier volume. https://cryosphere.scnat.ch/en/uuid/i-b8d5798e-a75e-5a7d-a855-f7a6613524ed-Two-catastrophic years obliterate 10_of_Swiss-glacier volume. (Accessed 23 March 2024).

Swiss Permafrost Monitoring Network (PERMOS), 2023. Swiss permafrost monitoring network database (PERMOS database). https://www.permos.ch/doi/permos-dataset-2023-1.

Swiss Seismological Service (SED) At ETH Zurich, 2005. Temporary deployments in Switzerland associated with aftershocks and other seismic sequences; ETH Zurich. Other. https://doi.org/10.12686/SED/NETWORKS/8D.

Swiss Seismological Service (SED) At ETH Zurich, 1983. National Seismic Networks of Switzerland; ETH Zürich. Other/Seismic Network. https://doi.org/10.12686/SED/NETWORKS/CH.

Tormann, T., Enescu, B., Woessner, J., Wiemer, S., 2015. Randomness of megathrust earthquakes implied by rapid stress recovery after the Japan earthquake. Nat. Geosci. 8, 152–158. https://doi.org/10.1038/ngeo2343.

Vernay, M., Lafaysse, M., Monteiro, D., Hagenmuller, P., Nheili, R., Samacoits, R., Verfallie, D., Morin, S., 2022. The S2M meteorological and snow cover reanalysis over the French mountainous areas: description and evaluation (1958–2021). Earth system. Sci. Data 14, 1707–1733. https://doi.org/10.25326/37#v2020.2.

Viani, A., Condom, T., Vincent, C., Rabatel, A., Bacchi, B., Sicart, J.E., Revuelto, J., Six, D., Zin, I., 2018. Glacier-wide summer surface mass-balance calculation: hydrological balance applied to the Argentière and Mer de Glace drainage basins (Mont Blanc). J. Glaciol. 64, 119–131. https://doi.org/10.1017/jog.2018.7.

Waldhauser, F., Ellsworth, W.L., 2000. A double-difference earthquake location algorithm: method and application to the northern Hayward fault, California. Bull. Seismol. Soc. Am. 90, 1353–1368. https://doi.org/10.1785/0120000006.

Wang, C.y., Manga, M., 2021. Earthquakes influenced by water. In: Water and Earthquakes. Springer. https://doi.org/10.1007/978-3-030-64308-9.

Weber, S., Beutel, J., Da Forno, R., Geiger, A., Gruber, S., Gsell, T., Hasler, A., Keller, M., Lim, R., Limpach, P., Meyer, M., Talzi, I., Thiele, L., Tschudin, C., Vieli, A., Vonder Mühll, D., Yücel, M., 2019. A decade of detailed observations (2008–2018) in steep bedrock permafrost at the Matterhorn Hörlniglat (Zermatt, CH). Earth Syst. Sci. Data 11, 1203–1237. https://doi.org/10.5194/essd-11-1203-2019.

Westaway, R., 2002. Seasonal seismicity of northern California before the great 1906 earthquake. In: The Mechanism of Induced Seismicity. Birkhäuser Basel, Basel, pp. 7–62. https://doi.org/10.1007/978-3-0348-8179-1_2.