# Long-lasting seismic swarming induced from flooding of an abandoned coal mine at Gardanne, France

Jannes Lennart Kinscher, P Niemz, D Namjesnik, S Cesca, Isabelle Contrucci, Pascal Dominique, Alain Thoraval

► To cite this version:

Jannes Lennart Kinscher, P Niemz, D Namjesnik, S Cesca, Isabelle Contrucci, et al. Long-lasting seismic swarming induced from flooding of an abandoned coal mine at Gardanne, France. Geophysical Journal International, 2025, 243 (3), 10.1093/gji/ggaf388. ineris-05330575

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# Long-lasting seismic swarming induced from flooding of an abandoned coal mine at Gardanne, France

J. L. Kinscher<fnref n="1" />,<fnref n="1" /> P. Niemz,<fnref n="2" />,<fnref n="3" /> D. Namjesnik,<fnref n="1" /> S. Cesca,<fnref n="3" /> I. Contrucci,<fnref n="1" /> P. Dominique<fnref n="4" /> and A. Thoraval<fnref n="1" />

Accepted 2025 September 26. Received 2025 September 25; in original form 2025 May 5

## SUMMARY

Flooding of abandoned excavation mines implies significant changes in the hydromechanic rock behaviour often associated with instantaneous rock instabilities which cause underground and ground failure and collapses, sometimes (but not always) accompanied by induced seismicity. The permanent modification of the hydrogeological setting may, in certain cases, also induce long-term seismic activities persistent over several years. The governing hydromechanic triggering mechanisms are poorly understood in these cases what bares challenges in related seismic hazard and risk assessment. In this study, we provide new insights into this poorly explored field of fluid induced seismicity, by investigating the long-lasting (>10 yr) swarm activity induced by the flooding of an abandoned coal mine at Gardanne in Southern France. The strongest events of the activity have comparatively small magnitudes ($M_w$ < 2) but are felt by the local population due to their shallow source depth (<1 km). Thanks to full waveform based source analysis we show that the swarm is associated with the permanent activation of pre-existing faults situated below the flooded mining voids which act as a very high-capacity anthropogenic reservoir and aquifer. We further show that mine water level changes caused by either natural or anthropogenic driving forces cause seismic triggering which involves direct pore-pressure as well as poroelastic effects. These findings provide constraints for adequate guidelines for safe mine water level management and seismic risk mitigation.

**Key words:** Earthquake hazards; Earthquake source observations; Induced seismicity; Wave-form inversion.

## 1. INTRODUCTION

Ground subsidence or failure and sinkhole formation represent one of the principal hazards associated with the flooding of abandoned mine environments. Microseismic monitoring has been proven an important tool to study the underlying processes and mitigate the associated risks (e. g. Miller et al. 1988, 1989; Wetmiller et al. 2022; Srinivasan et al. 2000; Toon & Styles 2004; Senfaute et al. 2008; Goldbach 2009; Contrucci et al. 2010, 2023; Kinscher et al. 2018). These hazardous ground movements arise from the proceeding destruction and collapsing of the voids left by mine works due to the destabilization of the rock mass in the presence of water (e.g. due to efficient normal stress reduction, alteration, softening, fatigue and strength decrease; Fig. 1). Accompanying seismicity seems to be mostly associated with the underground collapsing process in the overburden and shows a typical progressive

signature in space and time (e.g. Miller et al. 1989; Kinscher et al. 2015, 2020), but can also be absent, particularly during the occurrence of slow ground movements (e.g. Contrucci et al. 2019).

Besides the hazards associated with such transitory underground adjustment processes, recent mine flooding cases have reported the frequent initiation and further persistence of seismicity, raising seismic hazard and risk assessment concerns (e.g. Ogasawara et al. 2002a; Goldbach 2009; Kinscher et al. 2018; Muntendam-Bos et al. 2022; Contrucci et al. 2023,b). Largest observed event magnitudes in this context reached local magnitudes of $M_L$ > 3. Hypocentres tend to be often comparatively shallow with respect to natural earthquakes, so that the associated shaking is often felt by local population, causing nuisance and eventually building damage. The triggering mechanisms of such flooding-triggered seismicity is not yet well understood, limiting the development of efficient seismic forecast (e.g. estimation of maximum expected magnitude)

<footnote n="1">¹Institut National de l'Environnement Industriel et des Risques Ineris, Ecole des Mines de Nancy Campus ARTEM 92 rue du Sergent Blandan, F-54042 Nancy, France. E-mail: Jannes-L.Kinscher@ineris.fr</footnote>

<footnote n="2">²University of Utah, Seismograph Stations, 115S 1460E RM 211 FASB, 84112 Salt Lake City, UT, USA</footnote>

<footnote n="3">³GFZ Helmholtz Centre for Geosciences, PotsdamSection 2.1 Physics of Earthquakes and Volcanoes, Telegrafenberg, 14473 Postdam, Germany</footnote>

<footnote n="4">⁴BRGM, Bureau de Recherches Géologiques et Minières, 3 Avenue Claude-Guillemin, BP 36009, F-45060 Orleans Cedex 2, France</footnote>
<figure><img src="image_2.png" /><figcaption>**Figure 1.** A conceptual model for the flooding of abandoned mines and the accompanying seismicity. (a) Natural and human factors controlling the water level changes in abandoned excavated mine voids. (b) Sources of induced and triggered seismicity (yellow stars; EQ) on pre-existing faults (black lines) resulting from mine flooding and changes in the mine water level and related to pore pressure (ΔP) and volume (ΔV) changes.</figcaption></figure>

and risk mitigation measures (e.g. optimized economical acceptable flooding strategy).

Generally, flooding of mining voids is assumed to significantly modify the local hydrogeological regime. This may permanently affect the stability of adjacent pre-existing faults and modify (accelerate) their seismic cycle. Fault activation itself is most likely related to pore-pressure increase and/or mine void and fluid volumetric mass changes (Fig. 1b), but depends on various factors, as, for example, their initial state of stress, location and hydromechanic (elastic) coupling with respect to the flooded mine voids, the voids' geometry and the dimension of the involved faults (e.g. Fowkes et al. 2015; Allgaier et al. 2024). In contrast to other fluid-induced seismicity cases, the main hydraulic driving force for mine flooding-induced seismicity, that is, the water level changes, is not exclusively human controlled (e.g. flooding, pumping) but also depends on natural changes in the hydrological regime of the subsurface (e.g. changing meteorological condition associated with climate change) (Fig. 1a). As an example for the complex interplay between these factors,

Ogasawara et al. (2002a,b) discussed the cause of microseismicity (ML < 3) in a former flooded Japanese Silver mine in the context of heavy rainfall events and a distant major natural earthquake (Fig. 1).

In this study, we provide new insights into the causes of triggering of a long-lasting fault activation and swarm seismicity observed during the flooding of the abandoned Gardanne coal mine, in Southern France. While the initial microseismic monitoring attempts at Gardanne, back in 2008, focused on ground failure prevention (i.e. mine collapse detection), seismic hazard and risk has become a concern since the initiation of the long-term swarm activity in 2010, referred as the Fuveau swarm, which correlated with the arrival of the flooding front (Fig. 2; Kinscher et al. 2018; Namjesnik 2021; Namjesnik et al. 2021, 2022; Dominique et al. 2022; Contrucci et al. 2023). The swarm activity seems to be controlled by the changes in the water level within the abandoned mine voids and is characterized by the regular recurrence of seismic events (M<sub>w</sub> ~ 1) significantly felt by local population, which persisted until today despite the stabilization of the mine water level at a low stand.
<figure><img src="image_3.png" /><figcaption>**Figure 2.** Seismicity in the flooded abandoned Gardanne coal mine. (a) Seismicity over time recorded by the permanent seismic network (Section 3.1). Topmap view (b), inlet of location of Gardanne in France (c) and cross-section A–B (d) showing seismicity and flooding situation between 2010 and 2020. Size of seismic events (yellow points) is proportional to the event moment magnitudes ranging between $0 \le M_w \le 1.8$ (see Section 3.1 and Appendix A.1 for the seismic catalogue generation workflow). For information on hydrological monitoring stations and temporary seismic networks and catalogues, see Section 2.2 and 3.2. Spatial coordinates are given in Lambert Sud. Geological and mining layers are taken from the digital elevation model by Roger (2018). The flooding front is constraint from the water level measured at the Gerard well (see Section 2.2) and projected to the contour of the ‘Grande mine’ mine layer. Fault pattern are taken from Gaviglio (1985).</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Figure 3.** Schematic geohydrological model. (a) Water infiltration following rainfall (blue arrows) in the Begudian and Fuvelian aquifers following Dheilly *et al.* (2013) and Chalumeau (2000). (b) Water circulation in the abandoned mine works following Autran *et al.* (2014), Chalumeau (2000) and Dheilly *et al.* (2013) (see Section 2.2). Mine layer contours (grey lines) are shown in 15 m interval in metres a.s.l. Massive inflow at the source 90 (blue arrows) from the deeper and shallower aquifer and the main drainage pathways (white shaded blue arrows) towards the Gerard well (for further references see caption of Fig. 2).</figcaption></figure>

In the following, we first provide a short historical overview on mine flooding and seismicity (Section 2.1) and introduce the geotechnical and geohydrological settings (Section 2.2), as well as available data sets provided from seismic monitoring (Sections 3.1 and 3.2). Our study focuses then on the analysis of the Fuveau seismic swarm for the period 2010–2020. At first, based on a high-quality seismic data set associated with a dense temporal network in 2019–2020, we investigate the seismic source characteristics by applying advanced full-waveform based tools (waveform similarity based clustering analysis, source mechanisms and source parameters) (Section 3.3) and derive a conceptual fault model associated with the Fuveau swarm (Section 4). Furthermore, we classify seismicity episodes associated with specific water level stages (Section 5). Finally, we jointly interpret the seismological results and constrain the main triggering mechanisms controlling the long-term fault activation considering the available geohydrologic data sets (Section 2.2) and discuss the implications of those mechanisms for seismic hazard and risk (Section 6).

## 2. BACKGROUND ON GARDANNE MINE FLOODING AND INDUCED SEISMICITY

### 2.1. Brief historical overview of mine flooding and seismicity (Fuveau swarm)

The Gardanne coalfield, part of the active tectonic Provençal Arc Unit, forms an asymmetric synclinal limestone complex composed of Cretaceous (Begudian and Fuvelian) and Jurassic formations

and is generally associated with a low to moderate natural seismicity level (e.g. Gaviglio 1985; Nicolas et al. 1990; Senfaute et al. 2001; Abdul-Wahed et al. 2006; Dominique et al. 2022). The mining area extends over more than 10 km² which dips from the East at around 50–100 m depth westwards down to 1.4 km depth following the ‘Grande-mine’ coal layer (Fig. 2). Coal production and excavation took place over more than three centuries, which was accompanied by induced seismicity, rock bursts, flooding and mine collapsing events and land subsidence (e.g. Josien et al. 1989; Carnec & Delacourt 2000; Namjesnik 2021; Dominique et al. 2022; Contrucci et al. 2023; Raucoules & Foumelis 2024).

The mine closure in the early 2000s was succeeded by its continuous flooding since 2003. The flooding was microseismically monitored since 2008, focusing on designated zones in the eastern part of the region associated with a higher risk of ground failure (Fig. 2b; e.g. Namjesnik et al. 2021; Dominique et al. 2022; Contrucci et al. 2023). The arrival of the flooding front towards the eastern part of the mining region in 2010 coincided with the initiation of a long-lasting seismic swarm activity close to the town of Fuveau (the Fuveau swarm) which persists until today (Fig. 2). The swarm activity mostly occurs in form of distinct spatial clusters (Fig. 3), involving repetitive events associated with similar waveforms, so-called multiplets (Kinscher et al. 2018; Namjesnik 2021; Namjesnik et al. 2021). The swarm is characterized by a constant background seismicity and short-term bursts (usually lasting several months), as for example, in 2012, 2014, 2016 and 2017, which seems to be partially linked to changes in the mine water level (e.g. Kinscher et al. 2018; Namjesnik et al. 2021; Dominique et al. 2022) (Fig. 2a; Section 5). Most of the seismic events were located some hundred metres below the mine works, and source
mechanisms of large events revealed a predominant normal faulting style. From these observations, it was suggested that seismicity was related to the reactivation of pre-existing faults, situated below the mine works. The largest events were close to magnitude $M_w = 2$ and caused peak ground velocities of several mm s⁻¹ (Dominique et al. 2022; Contrucci et al. 2023).

## 2.2. Geotechnical mining and geohydrological context of the Fuveau swarm sector

The Fuveau swam is located in the Regagnas and Greasque mining sector, where the Grande-mine coal layer was extracted at around 300–350 m depth (Fig. 3). The sector was mainly exploited between 1960 and 1975, using the room and pillar excavation technique (Chalumeau 2000; Autran et al. 2014). Historical documents report that the area was affected by multiple catastrophic flooding events during exploitation and the presence of felt seismicity and partial ground surface movements (>20 cm) during its final excavation stage, possibly linked with pre-existing fault structures oriented NW–SE, parallel to the mining shafts (Didier et al. 2003). The last *in situ* inspections of the mine before the flooding documented that the pillars were mostly in a post-collapsed state and partially converged from ~2.4 to ~1 m. Given the partially collapsed state and thus relatively low collapsing risk, no microseismic monitoring was initially planned in this area during the mine flooding.

The local geological setting in the Fuveau swarm area is characterized by subhorizontal westwards dipping Cretaceous limestone layers (Figs 2d and 3b). Following fault structural, geomorphological analyses and *in situ* stress measurements, the area was interpreted as a transition zone between an extensive zone in the east and a compressive zone in the west of the mine area (Gaviglio 1985; Gaviglio et al. 1996; Chalumeau 2000; Dominique et al. 2022). Multiple flooding events reported during the exploitation were mainly attributed to the presence of ‘karstic faults’, also called the ‘source 90”, connecting the mine works with the over (Begudian) and underlying (Fuvelian and Jurassic) aquifers, supplying ~2/3 of the water inflow volume of the mine (Figs 2d and 3b) (Chalumeau 2000; Dheilly et al. 2013). Flooding events also partially occurred during periods of intense rainfall and fast infiltration into the karstic rocks to the east of the Fuveau swarm area (Rocher Bleu mining sector, Fig. 3a).

Today’s hydrological setting in the Fuveau swarm area, following its flooding, is likely still controlled by the two water inflow sources, source 90 and rainfall, as well as by the pumping rate of the Gerard well which regulates the global water level in the mine by draining it into the Mediterranean Sea via the tunnels of the ‘Galerie de la Mer’ (Figs 2b and d). Due to the long excavation history, the exact drainage configuration within the study area is unfortunately uncertain (Fig. 3b). Globally, the Fuveau area can be considered as a catchment basin situated below the eastern and southern mine voids and the backfilled less permeable deeper mine sections to the west and north, which likely have a damming effect on the infiltration in east-west direction (Fig. 3b). Given the particular reservoir configuration in the Fuveau area and the proximity to the main inflow sources (Fig. 3b), local mine water level changes are potentially more pronounced compared to any other flooded neighbouring mining districts. Unfortunately, until today there is no direct *in situ* water level measurement in the abandoned mine works. The only available hydrological data is provided by the piezometric record of the ~700 m deep Gerard well to the west and the shallow (7.5 m deep) Fuveau well at several kilometre distance (Fig. 2b).

We use both wells as a proxy for the regional mine water level and for the mapping of effective rainfall infiltration via the shallow Begudian aquifer (Fig. 2).

# 3. DATA AND METHODS

## 3.1. Permanent seismic network

Seismicity at the Gardanne mine has been monitored since 2008 by a permanent microseismic network (Permnet). The initial network was composed of five borehole stations, equipped with surface and borehole sensors at a depth of 100–200 m (Contrucci et al. 2023). The network provides near-real time monitoring focusing on ground failure risk zones east of the area studied here (Fig. 2b). The closest and thus key station for tracing historic seismicity of the Fuveau Swarm is PLA located at ~2 km to the east (Fig. 2b). The station is equipped with a 40 Hz borehole sensor at 109 m depth. Event detection is based on a trigger mode with fix threshold. Event locations are constrained using manually picked *P* and *S* wave arrival times and polarization angles (Contrucci et al. 2010). Local magnitude $M_L$ are constrained based on event durations (Contrucci et al. 2023). In addition, moment magnitudes $M_W$ are routinely determined by means of source spectrum inversion since 2017. As detailed in Appendix A.1, for this study, we refined locations and magnitudes by including additional data from the temporary network, which is described here below.

## 3.2. Temporary seismic networks

Following increased seismicity in 2010 and 2012 (Figs 2a and 6a), a first temporary network equipped with four three-component accelerometric surface stations (GeoSig) was installed in the Fuveau swarm area (Fig. 2b) (Tempnet2013–2018. Network; Dominique et al. 2022). After further intense swarm episodes in 2014, 2016 and 2017 (Figs 2a and 6a), the temporary network was densified in 2018 by the additional installation of five surface accelerometers and four velocimetric stations (30 s broad-band seismometers and 2 Hz geophones) (Fig. 2b) (Tempnet2019–2020 network; Dominique et al. 2022). Both Tempnet catalogues were created via a full-waveform-based back projection detection and location workflow (Namjesnik et al. 2021) involving the BacktrackBB algorithm (Poiata et al. 2016). Here, we consider only events associated with the best quality classes, namely A and B (Namjesnik et al. 2021). A and B classes imply that events are associated with local magnitudes $ML > -1$ and $M > 0$, respectively and are detected by at least three stations which is a pre-condition of reliable location approximation and data quality in terms of signal-to-noise ratio. Waveforms and locations of all these events have been revised by means of manually picked *P* and *S* waves and a calibrated velocity (see Appendix A.2). The final catalogue of Tempnet2013–2018 and Tempnet2019–2020 was composed of a total 2143 events which represented around 50 per cent of the total number of detected events. Based on the 95 per cent confidence interval of the location probability density function we estimate that the average epicentre uncertainty is below 200 m for Tempnet2013–2018 and below 100 m for Tempnet2019–2020, with depth uncertainties below 600 and 300 m, respectively. Moment magnitudes were calculated using empirical relationships based on the maximum waveform amplitudes and seismic moment estimates derived from the inversion of source spectra (Appendix A.2; see also Namjesnik et al. 2021). Due to the variation in station availability during the installation setup, we do not consider the data from 2018 (Section 3.3).
## 3.3. Advanced seismic source analysis

Advanced seismic source analysis was applied in order to constrain the main seismogenic fault pattern associated with the Foveau swarm, in order to unravel faulting style, fault dimension and its progressive activation over time. The analysis was applied to the *Tempnet* data, following the workflow introduced by Petersen et al. (2021) and tools described therein, aiming the following strategy. First, high-quality seismic data of the dense temporary network *Tempnet2019–2020* was used to constrain full moment tensors for the 26 strongest events (widely in 2019), using a full waveform based probabilistic approach (Appendix B.1). Next, a waveform similarity-based clustering analysis was performed to identify most important multiplet families between 2014–2020 (Appendix B.2). This allowed to bridge the high-quality source properties (locations and mechanisms) derived for events recorded with the *Tempnet2019–2020* data with smaller magnitude and poor-quality events associated with the *Tempnet2013–2018 data set*. Finally, source parameters as seismic moment, corner frequency and source radii have been estimated from both *Tempnet* data sets in order to constrain rupture sizes and to assess the overlap of rupture areas for each multiplet family (Appendix B.3). The results of this analysis are summarized and discussed in the context of the fault model construction in Section 4.

The double couple (DC) components of the derived moment tensors (MTs) predominantly show normal faulting with variable strike including NW–SE, E–W and NE–SW (Appendix B.1). Results are consistent to DC solutions previously obtained by Dominique et al. (2022). The MTs show predominantly negative vertically oriented CLVD (compensated linear vector dipole) components and smaller negative isotropic components, suggesting a vertical compaction component (Appendix B.1). While this observation is consistent for most of our solutions, we only interpret it in a qualitative way (Section 6.3), since we do not take into account the strong wave field perturbation and scattering effects likely arising from passage of voids of the abandoned mine works. Indeed, potential scattering and wave field diffraction is not accounted by our simplified velocity model. Results of the clustering approach highlight the presence of 23 multiplet families which account for around 40 per cent of the released seismic moment related to *Tempnet2013–2018* and *Tempnet2019–2020* (Fig. 4a). Several examples of long-term multiplet families exist (e.g. family 0, 5, 6 and 9) showing partially high-waveform similarity (cross-correlation coefficients $CC > 0.8$) for events associated with source time origins differing in the order of several years (Appendix B.2). Results of source parameter analysis demonstrate consistently for *Tempnet2013–2018* and more high-quality *Tempnet2019–2020* events that source radii range between 5 and 60 m and $M_w$ between –0.5 and 1.7 (Fig. B4; Appendix B3). Stress drops seem to range between 0.5 and 1 MPa. We remind here that due to the above mentioned simplification of the velocity and attenuation model, these results should be seen as first order approximations only.

# 4. CONCEPTUAL FAULT MODEL

## 4.1. Evidence for pre-existing fault structure activation

Previous investigations (Namjesnik et al. 2021; Dominique et al. 2022) suggested that seismicity can be attributed to the reactivation of pre-existing normal faults below the mine works. This hypothesis is supported by our results from event locations (Section 3.2; Appendix A2) and moment tensor inversions (Section 3.3;

Appendix B.1) using the high-quality *Tempnet2019–2020* data set (Fig. 4). In addition, seismicity shows a distinct clustering in space and is persistent over time (Figs 2 and 3; Section 5) several long-lasting seismic multiplets are observed persisting over several years (Fig. 4a; Section 3.3 and Appendix B.2). This repetitive character is compatible with the repeated activation of distinct fault zones rather than with instabilities and/or collapses within the mine works. Pure collapsing mechanisms are expected to show a higher variability in waveforms and a progressive spatiotemporal migration without local reactivations resulting from the continuous changes in the stress field arising from the volumetric changes associated with crushed pillars in the mine works (Fig. 1; e.g. Kinscher et al. 2015). Secondly, most of the mine works were already in a post-collapsed state before flooding (Section 2.2) and the size of the pillars (<10–15 m) is smaller than the source radii of ~40 m obtained for the larger events (Appendix B.3). On the other hand, compressional non-DC components found in the MT inversions (Appendix B1) and observations of surface subsidence (Section 2.2 and see later Section 5.3) seem rather in agreement with a mine collapses. Nonetheless, as later discussed in Section 6.3, we attribute the compressional components rather to the volumetric strains associated with the fluid mass reduction during the water level lowering process.

We developed a conceptual fault model (Fig. 5) based on existing information on main fault structures and our insights from MT inversions and the multiplet family classification (Fig. 4; Sections 3.2, 3.3 and Appendix B). In the model, we interpret the multiplet families as fault complexes (incorporating one or several neighbouring faults) who are explained in detail in the following sections. As previously suggested, the fault complexes are seen to be part of a pre-existing regional extensive tectonic regime spreading NE–SW (Fig. 5b), in agreement with *in situ* stress measurements in the mine works before the mine closure (Gaviglio et al. 1996; Dominique et al. 2022). A detailed documentation of pre-existing faults from direct *in situ* observations during excavation in the mine excavation level was given by Gaviglio (1985) and is shown in Fig. 2(a). In fact, most of the borders of the mining districts are related to fault structures which have been bypassed during excavation for stability and security reasons (Didier et al. 2003; Section 2.2). In the following fault model description, we provide an overview on the activated pre-existing faults segments constrained from seismic observations. The mechanism of pre-existing fault activation itself is then further discussed from the perspective of a simplified hydromechanical model in Section 6, considering the fault’s criticality with respect to the local stress field and the hydrostatic pressure level changes.

## 4.2. Southern fault complex (SFC)

The southern fault complex (SFC) is assumed to accommodate significant southwards extension along two main E–W trending normal fault zones within the NE–SW extensive regime (Fig. 5). The suggested fault pattern is consistent with the strike of the elongated cloud of seismic multiplets (families 0 and 9) and the respective source mechanisms (Figs 4e and f). Additionally, the orientation of the fault zones matches the orientation of the residual intact rock mass to the northwards mining sector suggesting that the fault zones are connected to the mine works. Hypocentre locations for multiplet families 0 and 9 indicate that the seismogenic part of the fault zones is located up to some hundred metres below the mine works (Fig. 4g).
<figure><img src="image_5.png" /></figure>

**Figure 4.** Results from full-waveform based MT inversion using the *Tempnet* data set. Colours correspond to the 21 main multiplet families identified from waveform similarity clustering (Appendix B.2). Coloured circles show clustered events, white circles refer to unassociated events; size scaled by magnitude ($0 < M_w < 1.8$); (a) cumulative seismic moment $M_0$ of multiplet families (coloured lines) and moment magnitude of multiplet events (coloured points) over time each; (b) source mechanisms for multiplets (coloured beachballs) and unassociated events (black beachballs); (c–f) zoom into the main long-term multiplet families showing source mechanisms and source radii (Appendix B.3); (g) source depths for events recorded by *Tempnet2013–2018* (transparent) and the high resolution data base *Tempnet2019–2020* (opaque) along profile A–B (see dashed line in panel b). See Fig. 2 for further references.

The SFC is likely constituted of a system of neighbouring fault segments or patches instead of representing a single fault or asperity (i.e. characterized by seismic repeaters). This is supported by the source radii of the multiplets that only partially overlap (Figs 4e and

f; Appendix B.3). Although the fault zones illuminated by families 0 and 9 are well-separated in space (>200 m), we cannot rule out a geomechanical interaction between both zones (i.e. stress transfer). Both zones show a high similarity in fault patterns in agreement with
<figure><img src="image_6.png" /><figcaption>**Figure 5.** Conceptual fault model of the Fuveau swarm. (a) Fault pattern within the fault complexes in comparison with *in situ* stress measurements (Gaviglio *et al.* 1996) and main faults observed during mining (Gaviglio 1985); (b) interpreted extensive strain model modified from Gaviglio *et al.* (1996), Chalumeau (2000) and Dominique *et al.* (2022); (c) P, t and B axis of DC fault mechanisms from Fig. 4.</figcaption></figure>

the main mine works boundary trends, suggesting the presence of a major, ~1 km long E–W trending fault zone (Figs 4b and 5), which has strong implication towards seismic hazard (Section 6.4). Also, compared to the other fault complexes, the SFC is associated with frequent stronger $M_w > 1$ events (see families 0 and 9 in Fig. 4a).

### 4.3. Eastern fault complex (EFC)

The eastern fault complex (EFC) is assumed to accommodate important extensive strain at the NE boundary of the Fuveau swarm (Fig. 5). Similar to the SFC, it hosts recurrently (at least yearly) strong event occurrences of $M_w > 1$ (see family 0 in Figs 4a and d).
On the other hand, the detailed fault pattern of the EFC are not as well constrained from MT source analysis. Unfortunately, we could only resolve a single MT for multiplet family 6 and the microseismic cloud of this family shows no clear alignment. Nonetheless, the orientation of the mine working boundary is in a first order consistent with the NNW-SSE trending normal fault from the source mechanism (Figs 4b and d). Similar to the SFC, source radii suggest the presence of several interacting fault segments or patches (Fig. 4d).

## 4.4. Central fault complex

The central fault complex (CFC) is interpreted as a structurally complicated fault depression zone accommodating the global horizontal and vertical strain produced by traction exerted by the extensive faulting at the SFC and EFC (Fig. 5). The CFC is associated with the broadest seismic cluster and the largest cumulative seismic moment release (see family 5 in Fig. 4). We attribute the faulting at the CFC to a highly complex network of small-scale, neighbouring and potentially interacting fault zones dominated by normal faulting with some obliqueness and variable orientations (Fig. 5). In a first order, source mechanisms tend to show a rough predominance of NW-SE and E-W normal faulting in the SE part of the CFC and NE-SW normal faulting in the NW part of the CFC, consistent with the orientations of the adjacent mine working borders (Figs 4b and c and 5).

We must emphasize here that our clustering approach does not allow to resolve the detailed fault pattern, that is, the number of individually involved fault zones and their potential interactions. The approach applied in this study was tuned for the mapping of large-scale structures, using relatively low frequencies (3–7 Hz) and a chain-like clustering (Section 3.3). As a result, deviating faulting mechanisms are observed within the multiplet family 5 (Fig. 4c). Clustering results based on higher frequencies (1–20 Hz) showed indeed that several multiplet families of the CFC can be subdivided into several consecutive rather transient-like short-term multiplet families (Namjesnik et al. 2022). Nonetheless, in the same approach the limited location accuracy with depth (in particular for Tempnet2013–2018) did not allow to resolve the associated fault pattern and structures in space (where different multiplet families often share very similar source hypocentre locations). The transient like character of the multiplet occurrences and the variety of involved source mechanisms in a spatially rather constrained seismic volume provide some indications that the CFC is undergoing significant changes in the constitution of the local stress field (see discussion in Section 6.3).

## 4.5. Northern (NFC) and Western fault (WFC) complex

The Northern and Western fault complexes (NFC and WFC) accommodate minor extensional strain at the northern and western margin of the Fuveau swarm (Fig. 5). Multiplet families in these areas occur in form of short transients (episodes of several months) and no long-term families were found (Fig. 4a). Few available source mechanisms suggest NE-SW and NW-SE trending normal faulting at the margins of the NFC and the WFC, respectively, constituting the outer borders of the suggested extensional domain (Figs 4b and 5). The largest event occurrences appear smaller compared to the other faults complexes (Figs 4a and b) what might suggest that the involved fault pattern are smaller dimensioned.

# 5. QUALITATIVE RELATIONSHIP BETWEEN FAULTING AND SEISMIC ACTIVITY AND COINCIDING MINE WATER LEVEL CHANGES

## 5.1. Arrival of flooding front (2010)

Following the rise of the ground water table since 2003, the arrival of the flooding front in the study area (at ~ -24 m a.s.l.) coincided with the initiation of the seismic activity in the Fuveau swarm area in April 2010 (Figs 2a and 6) (Namjesnik et al. 2021; Dominique et al. 2022; Contrucci et al. 2023). An increase in seismicity rate is observed in June 2010 which is seemingly located in the area of the EFC (dark blue coloured seismicity in Fig. 7a; see Appendix A1 for location approximation). In the following, pumping at the Gerard well was initiated in August 2010 to maintain the water level between -30 m and +18 m a.s.l. and avoid the uncontrolled overflow of ferruginous waters to the Mediterranean Sea (Dheilly et al. 2013). This was followed by a period associated with a rather constant seismicity rate and stable mine water level around -10 m a.s.l. (dark violet coloured seismicity in Figs 6 and 7a)

## 5.2. Rainfall controlled water level changes (2012–2014)

A major seismic crisis was recorded in November 2012, with repeated reports from the local population upon the occurrence of felt, stronger events ($M_w$ ~1.5, Fig. 6) which partially seemed to cause very slight damage (cracking of walls) on a few houses (Dominique et al. 2022). This activity marks the beginning of a period of large and comparatively fast water level changes (±20 m), which would last until 2015. In this period, the gradient of Gerard well water level, that is, the change of the global water level in the mine works, correlates particularly well (in phase) with the water table measured at the Fuveau well [shallow Begudian aquifer (Fig. 6d) which is a good proxy for effective rain fall infiltration (see Section 2.2)]. Such a correlation represents good evidence that the global mine water level changes were mainly controlled by the rainfall during this period. Previous hydrological tracer tests have shown that infiltration of rainfall into mine works is concentrated in zones of the outcropping Fuvelian formation to the East of the Fuveau swarm (Fig. 3a) and in the thrust fault belt in the south of the Gerard well (Chalumeau 2000; Dheilly et al. 2013). The observed infiltration speed into the mine layer was in the order of hours to days (depending on the amount of rainfall) what is consistent with delay times between the effective rainfall proxy (Fuveau well) and the global mine water level change (gradient of the Gerard water level) observable in Fig. 6(d). Some peaks in seismicity in 2013 and particularly in 2014 coincide with periods of fast water level rises and can thus be linked to intensified rainfall events (Fig. 6), as previously observed by Contrucci et al. (2023), Dominique et al. (2022) and Namjesnik et al. (2021). During this period, seismicity is mainly located in the SE part of the CFC (light blue coloured seismicity in Fig. 7a).

## 5.3. Mine water level lowering by intensified pumping (2015–2017)

Following the major water level changes in 2012–2014 (±20 m) it was concluded that the initial maximum pumping capacity (1000 m³ hr⁻¹) at the Gerard well was insufficient to stabilize the water level (Dheilly et al. 2013; Dominique et al. 2022). The installation of
<figure><img src="image_7.png" /><figcaption>**Figure 6.** Seismicity and mine water level changes between 2009 and 2020. (a, b) Seismicity of the Fuveau swarm recorded by Permet colour-coded corresponding to different phases of mine water level stands; the magnitude of completeness Mc is set to the minimum detection level in 2010 when comparatively high trigger thresholds were used; (c) global mine water level represented by the Gerard well level (dark blue curve) and the pumping rate at the Gerard well towards the Galerie de la mer (red curve); (d) the gradient (time derivative using 14 d windows) of the Gerard well level (dark blue curve) and the water level in the shallow Begudian aquifer recorded by the Fuveau well (light blue curve) as a proxy for effective rainfall; (e) INSAR data from Copernicus https://www.copernicus.eu/ for three reflector points located in the Fuveau swarm area.</figcaption></figure>

additional pumps in 2015 at the Gerard well increased the pumping capacity to 1200 m³ hr⁻¹ (Fig. 6c). In this way, the global mine water level was lowered by approx. 20 m in the last quarter of 2015. The pumping operations were then interrupted due to technical

issues from early to mid 2016. Resumed pumping from mid to end 2016 allowed to further lower the mine water level of ~20 m, with the aim of reaching a stable level at ~30 m a.s.l. This second pumping phase caused the fastest and strongest decrease in the mine
<figure><img src="image_8.png" /><figcaption>**Figure 7.** Seismic event locations and seismic moment release between 2010 and 2020 (Permnet). (a) The seismic events [points sized accordingly to the moment magnitude range (0 < *M*<sub>w</sub> < 1.8)] and flooding front levels (lines) are coloured by phases of specific water level stages (Fig. 6c and Section 5.). The direction of the flooding front is shown by white arrows. (b) Heatmap of the total cumulative seismic moment release (*M*<sub>0cum</sub>) and the main fault complexes as defined in Section 4. Clusters as previously identified by Namjesnik *et al.* (2021) and INSAR reflector points whose data are shown in Fig. 6(e).</figcaption></figure>

water level (global minimum of gradient of Gerard well level) since
the flooding front arrival 2010 (Figs 6c and d), which coincided
with the ‘drying’ or depletion of the mine sectors associated with
EFC and SFC (Fig. 7a). Comparing the water table gradient at

the Gerard well and the shallow Begudian aquifer level (Fuveau
well) shows a partial correlation during these pumping phases (mid
2015 to end 2016), suggesting a partial control of rainfall onto
water level changes (Fig. 6d). In turn, the lack of correlation from
early 2017 suggests that the reinforced pumping aperture finally allowed to stabilize the water level during the period of intensive rainfall.

Following some apparent slight increases in the seismic rate in the beginning of 2016 (2016a), a major episode of intensified seismicity occurred at the end and after the second pumping stage, involving several peaks of activity at the end of 2016 (phase 2016b) and from January to April in 2017 (phase 2017a) (Fig. 6). This period is associated with the highest seismic event rate and the largest cumulative moment release over the entire monitoring phase. During the phases 2016a and 2016b, microseismicity reaches levels close to the 2012–2014 activity and seem to be rather related to intensive rainfall events (magenta colored seismicity in Figs 6 and 7). In contrast, the seismic activity in phase 2017a migrated towards the NW mostly within the CFC (red coloured seismicity in Figs 6 and 7a; see also Namjesnik et al. 2021). The NW migration seem to coincide with the mine water level lowering and stabilization process controlled by the pumping rate (Fig. 6; Dominique et al. 2022).

The lowering of the water table and the seismic activity in phase 2017a further coincide with a sharp acceleration in the subsidence rate of ~20 mm within ~4 months (Fig. 6e). The subsidence was derived from INSAR (Interferometric synthetic-aperture radar) measurements using a reflector (ID: 4cHCV0r8Pc) located directly above the 2017a activity in the NW of the CFC (Figs 6e and 7b; Papageorgiou et al. 2025). A more moderate subsidence acceleration was observed at other reflectors (ID: 40Rgv70odR and 4cHCV0z6pv) located further to the North of the CFC (Figs 6e and 7b).

## 5.4. Drought period (August–October 2017)

After a decay of seismic activity until mid-2017, the activity resumed with a relatively sharp increase in August to October 2017 (phase 2017b, orange coloured seismicity in Figs 6 and 7; see also Namjesnik et al. 2021; Dominique et al. 2022). The associated seismicity was located at the NFC, where seismic events were rarely observed before neither occurred later (Figs 4a and 6). The Gerard well data shows no significant water level changes during this period, even though this might have been masked by the superimposed effect of the local pumping activity. Instead, we hypothesize a link between the seismicity peak and the water table minimum at the shallow Begudian aquifer, which mark the occurrence of a drought period associated with wide absence of effective rainfall (Fig. 6d). As a consequence, the aquifer inflow to the Fuveau swarm area might have been interrupted, causing an unusually low-water level stand.

## 5.5. Stable lowered mine water level (2018–2020)

Since the stabilization of the global mine water level at –30 m a.s.l. in 2017, seismicity also stabilized at relatively constant rates without significant bursts of seismicity (Figs 6 and 7). Seismic events are mostly located at the SW of the CFC, the EFC and SFC, that is, in fault areas widely activated during flooding and rainfall driven water level changes (2010–2016). In contrast, very few seismic events were recorded at the NE of the CFC and the NFC which were widely activated during low-water levels stands (2017a,b) (Fig. 7a). Smaller scale water level changes, for example, those caused by a pump maintenance period in early 2020 (~10 m), do not coincide with clear seismicity increases. Despite the significant decrease

in the seismic rate compared to the period before 2017, stronger seismic events $M_w > 1$ still occur at least twice a year and are felt by local population.

# 6. FAULT REACTIVATION AND SEISMIC TRIGGERING LINKED TO WATER LEVEL CHANGES

## 6.1. Fault reactivation caused by an efficient hydraulic connection to the mine aquifer

Water level changes appear as a first-order driver for seismicity during the Fuveau swarm (Section 5). Indeed, the temporal and spatial distribution of seismicity correlates very well with the arrival of the flooding front and its spatial contour, respectively (Figs 2 and 6). Spatially seismicity is exclusively concentrated in the mining districts exploited by room and pillar, containing voids (Fig. 2). Conversely, no significant seismicity is observed in the backfilled mining areas, exploited by longwall mining. The room and pillar mine voids in the Fuveau swarm area represent a very high-capacity anthropogenic reservoirs and/or aquifer, which enabled a hydraulic connection between the deeper (Jurassic) and shallower (Begudian and Fuvelian) aquifer (Sources 90 in Fig. 3b) and thus altered significantly the initial regional hydrological setting (Chalumeau 2000). Dheilly et al. (2013) estimated that the mine voids in the Eastern part of the Gardanne mine represent a reservoir volume of around 18 million m³ of water and act as a very efficient conduct associated with total discharge in the order of at least 800–1200 m³ hr⁻¹. Together with the karstic environment, mining voids support dynamic flooding and constitute a drainage system prone to produce fast and strong changes in the water level; hydrostatic conditions in its surrounding seem to affect the criticality of adjacent, pre-existing faults.

The identified fault complex of the Fuveau swarms, even though activated at greater depth, seem to be somehow structurally connected to the mine working and thus to its hydrological evolution. Indeed, the activated Fuveau fault network presents similar geometries, compared to the alignment of the seismicity cloud, the fault plane strikes derived from source mechanisms and the orientation of undermined galleries (Section 4). The testimony of a former mine geologist confirmed that the borders of mining districts often involved subvertical karstic fault structures, where waters frequently intruded from the over and underlying aquifers during exploitation (Didier et al. 2003; Section 2.2). These features form efficient hydraulic connections to the underlying reactivated faults or might be even directly involved in the faulting process. In the following, we further discuss the response of the fault complexes to the mine water level evolution in the context of two dominating effects: (i) a direct pore pressure effects during water level increases and (ii) a poroelastic effect during mine water level lowering (Figs 1 and 8).

## 6.2. Evidence for direct pore pressure effect (flooding and rainfall triggering 2010–2015)

Following Mohr–Coulomb frictional faulting theory, pore pressure increase on a fault lead to a reduction of the effective normal stress, bringing the fault closer to failure (Figs 1 and 8a). Such direct pore pressure effect is a well-known mechanism frequently involved in the generation of fluid induced seismicity (e.g. Kisslinger 1976; Shapiro 2015; Keranen & Weingarten 2018; Ge & Saar 2022). Following the evidence that the identified faults are
<figure><img src="image_9.png" /><figcaption>**Figure 8.** Schematic presentation of the governing hydromechanic trigger mechanism of normal faulting in the Fuveau swarm. (a) Direct pore pressure effect associated with periods of flooding and intensive rainfall and (b) a poroelastic effect during periods of intense mine water level lowering/pumping (see Sections 6.2 and 6.3; Appendix C); illustration of resulting dilatational and unloading stresses in panel (b) were adapted from Buijze *et al.* (2017) and Ge & Saar (2022).</figcaption></figure>

hydraulically efficiently connected to the mine works, this mechanism plays likely a fundamental role in their reactivation. A direct pressure effect is generally in agreement with the temporal increase in seismicity during periods when the mine water level was controlled by the flooding front and intensified rainfall between 2010 and 2014 and reached its maximum over the monitoring period (Fig. 6).

In order to quantify the role of a pore pressure effect, we present a simple (2-D) hydromechanic coupled model as described in Appendix C which considers a hydrological connected (fully saturated) medium between pre-existing (cohesion free) faults and the overlying mine works, which is in agreement with observations of karstic faults in Section 6.1. The effective stresses acting on the faults were simulated by assuming different stress states and pore pressure changes at the fault resulting from the difference between the flooding level (Gerard well water level) and fault depth. A Mohr-Coulomb failure criterium was then used to discuss the fault failure conditions for the minimum and maximum water level stands after

the flooding front arrival in 2010, that is, -30 m and 10 m a.s.l., respectively, and considered different fault depths, dips and frictional coefficients. The modelling results show that a failure can be expected for normal faults located at least 100 m below the mine works (i.e. at pore pressure change 0.7 and 1.1 MPa) when assuming the following conditions: the ratio between the minimum horizontal and vertical stress $\sigma_h/\sigma_v$ is below 0.6 ($\sigma_h = \sigma_3$ and $\sigma_v = \sigma_1$), the fault dip is smaller than 80° and the angle of internal friction $\psi$ is below 30° (see Appendix C). These conditions are widely in agreement with constraints found from *in situ* stress (Fig. 5), source mechanisms (Appendix B.1; Figs 4b–f) and hypocentral depths (Fig. 4g). Overall, this supports the scenario of pore pressure induced fault activation.

Pore pressure induced triggering seems to play a particular role in the reactivation of the EFC and may explain the comparatively high-seismic moment release in the area (Fig. 7). Indeed, seismicity associated with the EFC has been initiated with the flooding front arrival in 2010 and is regularly intensified during periods of rainfall
caused water level increases (2012, 2013, 2014) (Figs 6 and 7). The EFC is furthermore located closest to the main water inflow sources associated with the karstic over- and underlying aquifers (Figs 3 and 7; Section 2.2). Eventually, hydrostatic pressure rise particular high in this area during intense rainfall periods when the incoming water volume exceeded the limited storage capacity of the local mine voids. Hydrodynamic effects, favoured by the comparatively fast fluid flow expected in karstic environment, might have also played a role in this context, causing additional pressure transient at the underlying fault segments.

Next to the particular location within the hydrological setting, also difference in fault orientations and the faults stress state may explain the particular sensitivity of the EFC to pore pressure increase. Indeed, *in situ* stress measurements indicate that the minimum horizontal stress tends to be oriented NE–SW which is perpendicular to the NNW–SSE oriented fault plane of the EFC (Figs 5a and c) and thus more in favour for slip, when following Andersonian faulting principle (Anderson 1905), than normal faults of other fault complexes oriented E–W and NE–SW (e.g. NFC and SFC) (Figs 5a and c). Similarly, we suggest that differences in fault orientation explain dominance of seismicity in the SE of the CFC during periods of intensified rainfall where fault orientations tend (in a first order) towards a slight predominance of NW–SE oriented normal faults.

### 6.3. Evidence for poroelastic effect due to mine water level lowering (2017)

As introduced in Sections 5.3 and 5.4, some episodes of increased seismicity in 2017 coincide with the fastest and strongest decrease in the mine water level as a consequence of intensified pumping (2017a) and absence of rainfall (2017b) (Fig. 6). Since pore pressure is expected to be reduced during these phases, a pore pressure model alone is not able to explain the seismicity increase. Also pore pressure diffusion appears to be rather compatible with a East–Southeast migration of seismicity in the direction of the flooding front (e.g. Figs 2 and 3b) and not North–Northwest as observed during the activities in 2017a and 2017b (Fig. 7a). Indeed, since the flooding in 2010, the flooding level (water column) and resulting pore-pressure increase are in fact expected to be highest in the area of activities 2017a and 2017b where no seismicity has been observed before 2017 (Figs 7a and 8a). It must be stressed that the delay time between the water level high stands and rainfall peaks before the seismic activities in 2017a and 2017b are in the order of several months to years which are theoretically in agreement with the expected timescales of a pore pressure diffusion processes in karstic/carbonatic ($10^{-5}$ to $10^{-2}$ m$^2$ s$^{-1}$) and crustal rocks ($10^{-2}$ to $10^1$ m$^2$ s$^{-1}$) (e.g. Scholz 2019) assuming distances in the order of several hundred metres (intercould distances). Consequently, when assuming heterogeneities in the lateral and vertical hydraulic coupling between the flooded mine layer, the overburden and the underlying faults, the role of pressure diffusion, cannot be completely neglected. Nonetheless, considering the similarity in the geological environment and the excavation method such process appears rather unlikely in explaining the 2017a and 2017b activities and more relevant for East–Southeast trending spatiotemporal migration trends.

Regarding the above discussed shortcomings of a pore pressure diffusion model, we suggest that the origin of the 2017a and 2017b seismic activities is more likely linked to poroelastic effects following ground water lowering. Seismic source origin in relation with groundwater level lowering and poroelastic effects can be associated with volumetric compaction strains arising from the pore pressure

reduction within and around a finite ground water reservoir (e.g. González *et al.* 2012; Amos *et al.* 2014; Keranen & Weingarten 2018; Wetzler *et al.* 2019; Tiwari *et al.* 2021; Shalev *et al.* 2023). The discussed models in these cases are widely based on the pioneering work by Segall (1989) and Segall *et al.* (1994), who evidenced the existence of poroelastic effects during massive depletion of gas and petrol reservoirs. Accordingly to this model, volume reduction (compaction) of a reservoir (due to depletion) generates stresses to the elastically coupled surrounding rock, what may in certain cases trigger seismicity and commonly cause surface subsidence (Figs 1 and 8b). Assuming a simple rectangular finite reservoir geometry, poroelastic stresses favour normal faulting beside the reservoir (horizontal dilatation) and reverse and thrust faulting above and below it (vertical dilatation) (Fig. 8b).

In Fig. 8(b), we adapt this mechanism within a conceptual model in which we consider the different mining districts as finite separated individual reservoirs. The model then suggests that mine water level lowering caused pore pressure lowering and compaction in the subreservoir associated with the mining districts located to the E and SE, respectively (i.e. in the EFC and SE of the CFC), which are located above the lowered mining water level at –30 m a.s.l.. Next to the pore pressure decrease, compaction of the subreservoir might be even amplified by partially collapses of the mine voids. Compaction in this area is then supposed to cause extensive horizontal stresses in the lateral neighbourhood and still flooded mining districts to the W and NW (in the NFC and NW of the CFC) and a decrease the vertical stress below the depleted subreservoir due to unloading (in the EFC and SE of the CFC). The stress change thus favours normal faulting (decrease in $\sigma h/\sigma v$) in the laterally-neighboured mining districts and counteracts normal faulting below the mine works (increase in $\sigma h/\sigma v$), that is, stabilize faults associated with the EFC, SFC and SE CFC.

The model is qualitatively in very good agreement with the spatiotemporal migration trends in the seismic evolution, which seem to represent a fingerprint of the relaxing environment, following the instantaneous volumetric compaction associated with the mine water lowering in 2017 to –30 m a.s.l. (Fig. 7a). Indeed, the mine water level lowering is succeeded by a clear spatiotemporal migration of the seismicity within the CFC from SE towards NW (2017a), where negligible seismicity was recorded before and after this period (Figs 6 and 7a; Section 5.3). Similarly, a lowering of the mine water level during a subsequent drought period (2017b) caused a temporal increase in seismicity in the NFC, which remained seismically quiet since this period (Section 5.4). Following the conceptual model, these temporally isolated activations of the NW of the CFC and the NFC likely result from a temporally decrease in the $\sigma h/\sigma v$ ratio (i.e. an equivalent to bring a normal fault closer to failure; see Appendix C) associated with the instantaneous volumetric stresses produced by volume compaction of the emptied-neighboured mining sectors (i.e. EFC, SFC) (Fig. 8b). Consistently, seismic quiescence in the EFC and in the SE of the CFC during the water level periods (i.e. 2017a and 2017b) (Fig. 7) can be explained by the ‘instantaneous’ decrease in vertical stresses (unloading) and resulting normal fault stabilization below the emptied mining districts (reservoirs) as predicted by the conceptual model (Fig. 8b).

The model further explains that the significant increase in the subsidence rate is observed following the mine water level lowering (2017a) (Figs 6d and 7b) which is a common effect in reservoir depletion cases related to the vertical contraction (Segall 1989 and Segal *et al.* 1994). The presence of volume compaction is also consistent with the non-DC components of the MT solutions showing
large predominately negative CLVD components and also negative isotropic components. Due to the underlying uncertainties, we refrain from an event-wise or cluster-wise interpretation (Section 3.3, Appendix B, Fig. B2).

All these observations highly support the presence of poroelastic effects, which triggered seismicity during the mine water level lowering phases. Observed poroelastic stress perturbations in fluid injection/extraction scenarios are typically significantly smaller than 1 MPa (e.g. Ge and Sar 2022). The highly simplified Mohr–Coulomb considerations in Appendix C show that theoretically such tiny stress perturbations would be enough to cause fault failure under certain conditions (faults orientation and dip and friction properties). As an example, the threshold of the $\sigma h/\sigma v$ ratio for failure of a fault situated 100 m below the mine works is below 0.5 which is very close to the observed $\sigma h/\sigma v$ ratios from *in situ* measurements ranging between 0.46 and 0.8 (Fig. 5a and Appendix C). It should also be noted that pore pressures were still relatively high at the concerned (still flooded) faults (NFC and NW CFC) (Figs 7a and 8b). Similar to post-injection triggering mechanisms in geothermal environments (Segall & Lu 2015), fault failure might thus be considered as a consequence of both, (i) pore pressure increase from the (low stand) flooding and (ii) instantaneous poroelastic stresses caused by the contraction of the depleted-neighboured mine void. The detailed role and the quantification of poroelastic effects in seismic triggering remains beyond the scope of this study and requires numerical modelling approaches considering the complex geometry of the mine works, their potential (elastic) interaction, as well as the dynamics (timescale) of the water lowering process. Other mechanisms which might have been involved include the (a) seismic stress transfer caused by previous long-lasting faulting activity as frequently observed in fluid induced (e.g. Cornet et al. 1997; Guglielmi et al. 2015; Wei et al. 2015; Lengline et al. 2017; Kinscher et al. 2023) and mining induced (e.g. De Santis et al. 2020; Kinscher et al. 2020) seismicity cases.

## 6.4. Current faulting regime and implications for seismic hazard and risk

As stated in Section 5.5, the stabilization of the mine water level to ~ -30 m a.s.l. since 2017 coincided with a stabilization in the swarm activity, which was since than characterized by a constant and comparatively low-seismicity rate and the absence of seismicity bursts. This lowered background activity seems to reflect a rather constant loading and unloading process similar to seismogenic tectonic faults. This behaviour may indicate that the current mine water level stand still implies elevated pore pressures, which cause metastable states of certain fault complexes. In other words, certain fault complexes are still associated with an acceleration of their seismic cycle, as originally imposed by regional tectonic loading due to the still elevated pore pressures. Accordingly, we note that seismic event locations associated with this lowered background activity seem to be preferentially distributed in the widely pore pressure driven fault complexes: EFC, the SE part of the CFC, the SFC and the WSC (Figs 7a and 8a; Section 6.2). In addition, Mohr Coulomb simulations show that even for the mine water level low stand of -30 m a.s.l., a failure is likely for a wide range of fault characteristics (Appendix C) which are in agreement with the constraints from the derived seismic source properties (Appendix B) and *in situ* stress measurements (Fig. 5). Next to the role of elevated pore-pressures also stress transfer from previous (a)seismic

faulting and changes in the frictional properties of the faults (e.g. cohesion, friction coefficient) may be considered here as a factor in causing the persistency in seismicity and the related metastable state of certain faults. In this context, it is worth to mention that the current water level low-stand seems slightly below the water level where first seismic events have been recorded following the flooding front arrival in 2010 (Fig. 6a), what is in line with a change in stress and/or rheological properties of the faults.

Even though the seismicity rate is significantly lowered since the mine water level stabilization, persistent seismicity still raises concerns in terms of seismic hazard and risk. Indeed, larger $M_w > 1$ events felt by location population still regularly occur at least once per year, what bares a risk of long-term nuisance (Fig. 6). Furthermore, the occurrence of extreme meteorological events, which become stronger and more frequent with climate change, or failure of pumping system may result in significant water level changes. This can likely cause future, intensified swarm episodes, as previously experienced before 2017. Even though previous swarm episodes rather document partitioned stress release by numerous rather small magnitude events $M_w < 2$, the estimation of the maximum expected magnitude remains rather uncertain. As an example, it remains unclear if fault pattern of certain fault complexes, as the neighbouried E–W oriented normal fault pattern of the SFC may form comparatively large (~500 m) potential dynamic rupture zones (Fig. 5 and Section 4.2) capable of hosting larger $M_w$ 3–4 events (assuming circular fault model and stress drop of 1 MPa; Appendix B3). The impact of such an event would likely involve severe shaking and building damage, considering the very shallow source depth of these events.

## 7. CONCLUSION AND OUTLOOK

We investigated the origin of long-lasting (>10 yr) swarm activity in connection with the flooding of an abandoned coal mine at Gardanne, France. Thanks to full waveform source analysis we showed that seismicity of the so-called Fuveau swarm was related to long-lasting activation of pre-existing faults. Conceptually, these faults can be grouped into normal faulting complexes with variable fault orientation, which are part of a local NW–SE directed extensive tectonic regime. Fault structures are widely situated up to some hundred metres below the mine works and efficiently hydromechanically connected. The cause of the persistent fault activity and resulting seismicity can be generally seen in the permanent modification of the local hydrogeological setting caused by the presence of a high-capacity anthropogenic reservoir and aquifer represented by the mining voids. Based on the available seismic and hydrological data and a simple geomechanic considerations, we further show that elevated pore-pressure likely permanently changed the faults criticalities and accelerated their seismic cycle. Furthermore, we highlight that the individual fault complex responds differently to periods of significant mine water level increase (due to periods of intensified rainfall), what likely implies that the fault criticalities and sensitivity to pore-pressure increase locally differs (likely due to differences in fault orientation). Furthermore, we highlight that certain fault complexes are sensitive to periods associated with reinforced anthropogenic mine water lowering/pumping. Triggering of seismicity and coinciding ground subsidence in this case seems to be likely caused by the implied volume compaction and resulting poroelastic stressing comparable with reservoir and groundwater depletion scenarios.
Even though magnitudes associated with the strongest swarming events are rather small compared to other induced/triggered seismicity cases ($M_w < 2$), these events are frequently felt by local population which is likely due to their shallow source depth ($<$1 km). The persistency of the seismicity, as highlighted by our results, underlines the importance of seismic hazard and risk assessment in flooded abandoned mining environments, which are often ignored/underestimated in post-mining risk management. An efficient, but eventually cost-intensive measure to mitigate seismic hazard and risk in the Gardanne case is likely given by the lowering and stabilization of the mining water level below the voids associated with the mining districts exploited by room and pillar. In this case it might be of advantage to envisage a step-wise lowering process itself to avoid potentially seismic activity possibly resulting from poroelastic stress. Another mitigation measure can be seen in the *in situ* monitoring of the water level within the mining voids located above the reactivated faults which was unfortunately not available for our study. Indeed, such measures may allow to better constrain the involved hydromechanic coupling processes and to determine water level monitoring thresholds allowing to minimize the triggering of seismicity.

## ACKNOWLEDGMENTS

This study was conducted in the framework of the POST-MINEQUAKE project https://postminquake.eu/funded by the European Commission Research Fund for Coal and Steel under grant agreement no. 899192. We thank the BRGM for providing seismic data of the temporal seismic network. We thank the editor, Beata Orlecka-Sikora and one anonymous reviewer for their comments and suggestions which helped significantly to improve the manuscript.

## DATA AVAILABILITY

The data underlying this article will be shared on reasonable request to the corresponding author.

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# APPENDIX A: SEISMIC CATALOGUE CREATION

## A.1. Revision of Permnet catalogue

Location and magnitudes of the Fuveau swarm events obtained from *Permnet* are associated with higher uncertainties since often only a single or some quite distant stations were used in this respect. For that reason, more precise event locations and magnitudes from *Tempnet* were used instead if available (widely the case for events >2013 and located in the study area). Locations for events occurring before *Tempnet* installation (<2013) have been partially revised by means of wave form similarity analysis related to certain *Tempnet* events using the closest *Permnet* station PLA (Fig. 2b). In this procedure, we explored the event location by using a spatial grid of 100 m resolution within the study area. For each grid point we looked then for the maximum CC of a given event (<2013) with all adjacent (<50 m) ‘well located’ event from the *Tempnet* catalogue. The revised event location was defined as the grid point associated with the highest cross-correlation value CC, given the case that the CC is at least >0.7. In addition, we derived moment magnitudes for all *Permnet* events by using an empirical relationship between the distance corrected logarithmic amplitudes of PLA station with the $M_w$ derived from source spectrum inversion of the *Tempnet* events (Section 3.3 and Appendix B.3).

## A.2. Refining source location and magnitude estimations for Tempnet events

In order to get information on source depth and improve event location for noisier events we relocated the *Tempnet* events by using manual *P* and *S* and a calibrated velocity model. The location approach was based on a probabilistic scheme taken into account the picking uncertainties (Contrucci *et al.* 2010). The velocity model was derived by minimizing the RMS (root mean square error) of theoretical and observed arrival times for 14 high-quality events of the *Tempnet2019–2020* data for different ranges of Vp and Vs velocities. Only a homogeneous velocity model was used in this case since a double layered model did not improve the global RMS. The results Vp = 3950 m s⁻¹ and Vs = 1950 m s⁻¹ where consistent with previous findings by Dominique *et al.* (2022). Similar as done for *Permnet* (Appendix A.1), moment magnitudes were calculated by using and empirical relationships between the averaged distance corrected logarithmic amplitudes (over all stations) and $M_w$ derived from source spectrum inversion (Section 3.3 and Appendix B.3).

# APPENDIX B: ADVANCED SEISMIC SOURCE ANALYSIS

## B.1. Moment tensor inversion

Source mechanisms have been investigated by applying full MT inversion to the strongest events recorded by the dense *Tempnet2019–2020*, providing good azimuthal coverage with at least ten three-component stations (Fig. 2b). Moment tensors have been retrieved using the probabilistic full waveform inversion tool Grond (Heimann *et al.* 2018). For the inversion, we fitted observed spectral amplitudes from seismometers and accelerometers and the corresponding Z, R (radial) and T (transversal) observed velocity traces and acceleration traces to synthetic data (see e.g. Petersen

*et al.* 2021). The time window is asymmetrically set (−0.15/0.85 s) around the theoretical *P* arrival. Waveforms and amplitude spectra were filtered at 1 and 5 Hz which represented the best compromise between signal quality and minimization of complex propagation effects introduced by the mining voids located above the seismic events. Each inversion runs in 101 independent bootstrap chains with random weightings of the station-component-based misfits. The results of all bootstrap chains are considered to evaluate the stability of the results (Fig. B1). Apart from the moment tensor we also invert for the event depth and the origin time. The Green’s functions, used to produce synthetic waveforms within the inversion, were calculated using the qseis code from Wang (1999) as implemented in the fomosto software (Heimann *et al.* 2019) based on a local velocity model. The stability of the MT inversions strongly depended on the event magnitude, the available data, and the coverage of the focal sphere, that is, the location of seismic sensors all around the event (Table B1). The focal depth solutions are not well constrained but are mainly below the mine excavation level.

## B.2. Waveform similarity-based clustering

Multiplet clustering was applied by means of waveform cross-correlations incorporating multiple stations to provide a better sampling of the focal sphere using the Clusty toolbox (Petersen *et al.* 2021). To avoid bias due to changes in the seismic network we had to limit our analysis to four stations which were continuously operational. The stations still cover the study area well enough. Compared to the MT inversion, we used a higher pass band (3–7 Hz) in order to cluster events with similarities in source mechanism and location within the different faulting complexes. The similarity in mechanism and location maps directly into the waveform of event pairs. An event migration along an active fault, thus a change in event location, will gradually change the waveforms recorded at a given station. Our approach accounts for such gradual change for the mapping of faults by tracking it with the chain-like clustering algorithm DBSCAN (Ester *et al.* 1996). DBSCAN’s search parameters epsilon and minPts, that is, the search radius and the minimum number of core points, were set to 0.21 and 4. The correct choice for these parameters depends on the window-length of the cross-correlated waveforms, the used frequency filters, waveform complexity, attenuation and ultimately the specific purpose of the analysis. For more details on the method and a sensible choice of parameters, see Petersen *et al.* (2021). An example of a long-lasting multiplet family associated with similar waveforms and slight gradual changes is shown in Fig. B3.

## B.3. Source parameter determination

Source spectrum inversion has been applied to the *Tempnet* data sets to examine spatial source dimensions of the recorded events by constraining $M_w$ and source radii $r_0$. Inversion was done by using the *S* wave displacement spectrum expressed in $M_w$ following the principle by Satriano (2022) in a slightly modified approach. A classical Brune’s model (Brune 1970) is combined with an anelastic attenuation law and fitted to the observed spectrum by minimizing the error over a grid search on $M_w, f_C$ and the *Q* factor. The solution space was constrained by the condition that individual stations have a common $M_w$ and $f_C$ while *Q* can individually vary for each station. In this way, we try to reduce the bias from differences in Green
<figure><img src="image_10.png" /><figcaption>**Figure B1.** Example output/result of a probabilistic full MT inversion following Heimann *et al.* (2018). $M_w$0.25 (catalogue ML-0.9) event on 2019 June 12 01:45. Uncertainties are visualized by 101 ensemble solutions. (a) The Hudson plot shows the uncertainty in the contribution of non-DC components. The ‘fuzzy MT’ in the inset shows the stacked beachballs of the ensembles and the best solution in red. (b) MT decomposition into isotropic, CLVD and DC comp. for the mean of the ensemble solutions and the best solution. Similar best and mean solutions indicate a stable inversion. (c) Probabilistic density functions for strike, dip, rake and depth. Focused peaks indicate a stable inversion. (d) Examples of amplitude spectra and waveform fits.</figcaption></figure>

functions on corner frequency estimates (e.g. Abercrombie 2015; Ide et al. 2003). Weighting is performed during inversion using the spectral signal-to-noise ratio for each frequency band and for each station. Other relevant parameters used were the average radiation

coefficient for S-waves $R_S$ = 0.6 (Boore & Boatwright 1984) and an average density $\rho$ assumed to be equal to 2700 kg m$^{-3}$. Source radii were derived from the corner frequency using the model by Madariaga (1976) and assuming a rupture velocity of 2.9 km s$^{-1}$.
<table><caption>Table B1. Result from MT inversion for the 26 strongest events of *Tempnet2019–2020*.</caption><thead><tr><th>Time<br>DD/MM/YYHH:MM</th><th>Lat</th><th>Lon</th><th>M<sub>w</sub></th><th>Strike1<br>[°]</th><th>Dip1<br>[°]</th><th>Rake1<br>[°]</th><th>Strike2<br>[°]</th><th>Dip2<br>[°]</th><th>Rake2<br>[°]</th><th>ISO<br>[norm.]</th><th>CLVD<br>[norm.]</th></tr></thead><tbody><tr><td>12/01/2019 22:19</td><td>43.439 50</td><td>5.531 10</td><td>0.57</td><td>79.4</td><td>77.9</td><td>-92.3</td><td>270.0</td><td>12.3</td><td>-79.6</td><td>-0.32</td><td>-0.34</td></tr><tr><td>16/01/2019 23:09</td><td>43.439 00</td><td>5.535 40</td><td>1.34</td><td>95.7</td><td>47.0</td><td>-69.0</td><td>246.3</td><td>47.0</td><td>-111.0</td><td>-0.23</td><td>-0.53</td></tr><tr><td>23/01/2019 22:04</td><td>43.439 00</td><td>5.532 30</td><td>0.44</td><td>35.8</td><td>41.3</td><td>-86.2</td><td>210.8</td><td>48.8</td><td>-93.3</td><td>0.16</td><td>-0.24</td></tr><tr><td>24/01/2019 05:01</td><td>43.439 90</td><td>5.529 80</td><td>0.28</td><td>77.2</td><td>42.4</td><td>-1.9</td><td>168.6</td><td>88.7</td><td>-132.4</td><td>-0.06</td><td>-0.76</td></tr><tr><td>24/02/2019 14:56</td><td>43.443 00</td><td>5.535 40</td><td>0.28</td><td>242.5</td><td>70.4</td><td>-51.6</td><td>355.4</td><td>42.4</td><td>-150.2</td><td>0.25</td><td>-0.40</td></tr><tr><td>25/02/2019 18:25</td><td>43.442 60</td><td>5.536 70</td><td>0.47</td><td>252.9</td><td>62.1</td><td>-115.2</td><td>118.2</td><td>36.9</td><td>-51.1</td><td>-0.16</td><td>-0.65</td></tr><tr><td>26/02/2019 09:40</td><td>43.445 70</td><td>5.524 90</td><td>0.55</td><td>121.5</td><td>74.5</td><td>-83.6</td><td>278.6</td><td>16.7</td><td>-112.0</td><td>-0.39</td><td>-0.27</td></tr><tr><td>30/03/2019 08:24</td><td>43.442 10</td><td>5.537 30</td><td>0.43</td><td>266.3</td><td>54.6</td><td>-118.3</td><td>129.3</td><td>44.1</td><td>-56.3</td><td>0.21</td><td>-0.55</td></tr><tr><td>14/04/2019 15:07</td><td>43.444 40</td><td>5.539 10</td><td>0.77</td><td>202.1</td><td>85.3</td><td>-79.8</td><td>316.5</td><td>11.2</td><td>-155.2</td><td>-0.51</td><td>-0.30</td></tr><tr><td>19/04/2019 09:04</td><td>43.439 50</td><td>5.532 30</td><td>1.55</td><td>81.5</td><td>56.3</td><td>-86.5</td><td>255.2</td><td>33.9</td><td>-95.2</td><td>-0.30</td><td>-0.36</td></tr><tr><td>22/04/2019 02:06</td><td>43.442 60</td><td>5.541 00</td><td>0.26</td><td>75.3</td><td>56.5</td><td>-77.7</td><td>233.8</td><td>35.4</td><td>-107.9</td><td>0.12</td><td>-0.41</td></tr><tr><td>02/05/2019 09:13</td><td>43.439 50</td><td>5.535 40</td><td>0.25</td><td>109.5</td><td>60.9</td><td>-86.3</td><td>281.8</td><td>29.3</td><td>-96.7</td><td>-0.13</td><td>-0.53</td></tr><tr><td>08/05/2019 10:09</td><td>43.444 00</td><td>5.539 80</td><td>1.20</td><td>171.5</td><td>72.8</td><td>-98.2</td><td>17.5</td><td>19.0</td><td>-65.2</td><td>-0.34</td><td>-0.42</td></tr><tr><td>16/05/2019 19:52</td><td>43.446 20</td><td>5.539 80</td><td>0.80</td><td>350.6</td><td>55.4</td><td>-99.7</td><td>187.3</td><td>35.7</td><td>-76.3</td><td>0.09</td><td>-0.28</td></tr><tr><td>22/05/2019 17:03</td><td>43.443 00</td><td>5.532 30</td><td>0.51</td><td>125.9</td><td>83.1</td><td>97.0</td><td>260.3</td><td>9.8</td><td>44.8</td><td>-0.37</td><td>0.20</td></tr><tr><td>12/06/2019 01:45</td><td>43.442 60</td><td>5.536 00</td><td>0.25</td><td>233.0</td><td>56.1</td><td>-79.0</td><td>33.7</td><td>35.5</td><td>-105.9</td><td>0.04</td><td>-0.39</td></tr><tr><td>02/07/2019 11:51</td><td>43.439 00</td><td>5.531 10</td><td>1.28</td><td>78.6</td><td>59.8</td><td>-88.0</td><td>254.7</td><td>30.3</td><td>-93.4</td><td>0.21</td><td>-0.55</td></tr><tr><td>07/07/2019 05:40</td><td>43.440 80</td><td>5.532 93</td><td>0.29</td><td>238.6</td><td>69.1</td><td>-71.3</td><td>15.0</td><td>27.8</td><td>-130.1</td><td>0.02</td><td>-0.84</td></tr><tr><td>10/07/2019 00:41</td><td>43.443 50</td><td>5.534 20</td><td>0.13</td><td>88.4</td><td>74.7</td><td>-86.5</td><td>255.5</td><td>15.6</td><td>-102.5</td><td>-0.06</td><td>-0.38</td></tr><tr><td>30/07/2019 05:41</td><td>43.444 40</td><td>5.537 90</td><td>0.20</td><td>162.4</td><td>87.8</td><td>-94.9</td><td>47.9</td><td>5.4</td><td>-24.6</td><td>0.38</td><td>-0.24</td></tr><tr><td>14/08/2019 17:37</td><td>43.450 20</td><td>5.536 70</td><td>0.60</td><td>238.0</td><td>59.9</td><td>-37.0</td><td>348.6</td><td>58.6</td><td>-144.1</td><td>0.21</td><td>-0.57</td></tr><tr><td>17/08/2019 01:19</td><td>43.443 50</td><td>5.534 20</td><td>0.49</td><td>258.9</td><td>70.5</td><td>-49.6</td><td>10.3</td><td>44.2</td><td>-151.3</td><td>0.01</td><td>-0.92</td></tr><tr><td>15/09/2019 07:02</td><td>43.449 40</td><td>5.532 90</td><td>0.48</td><td>217.9</td><td>66.7</td><td>-86.2</td><td>28.4</td><td>23.6</td><td>-98.7</td><td>-0.25</td><td>-0.35</td></tr><tr><td>23/09/2019 18:58</td><td>43.443 00</td><td>5.534 80</td><td>0.22</td><td>237.4</td><td>53.8</td><td>-79.4</td><td>39.8</td><td>37.5</td><td>-104.2</td><td>0.07</td><td>-0.16</td></tr><tr><td>25/09/2019 22:41</td><td>43.439 90</td><td>5.529 20</td><td>0.41</td><td>57.9</td><td>13.9</td><td>-23.5</td><td>170.8</td><td>84.5</td><td>-102.8</td><td>0.21</td><td>-0.35</td></tr><tr><td>07/10/2019 12:32</td><td>43.444 40</td><td>5.534 80</td><td>0.18</td><td>77.3</td><td>56.5</td><td>-56.3</td><td>206.8</td><td>46.1</td><td>-130.0</td><td>0.10</td><td>-0.64</td></tr></tbody></table>

<figure><img src="image_11.png" /><figcaption>**Figure B2.** MT Decomposition: Hudson plot showing all inverted MTs: The events have predominantly negative CLVD and isotropic components. Positions show the relative contribution of CLVD and isotropic components to the full MT solution. The beachballs show the DC component. Dotted lines indicate the standard deviation of CLVD and isotropic components based on Grond's bootstrapping uncertainties.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Figure B3.** Waveform example of long-lasting multiplet occurrence. Waveforms (filtered at 3–7 Hz) of station BOS (Fig. 2b) of multiplet Family 0 (Fig. 4) identified from clustering analysis (Section 3.3 and Appendix B.2).</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Figure B4.** Source radii $r_0$ and moment magnitude derived from source spectrum inversion (Appendix B.3). Continuous show stress drops for reference and dashed lines represent uncertainties, which were approximated by the variance found in the results of the individual station (for a similar procedure see Kinscher et al. 2020).</figcaption></figure>

## APPENDIX C: MOHR COULOMB FAULT FAILURE FROM PORE PRESSURE EFFECTS

Normal and shear stresses $\sigma_n$ and $\tau$ on a fault plane are expressed as a function of total vertical and minimal horizontal stress $\sigma_v$ and $\sigma_h$ and the angle of the fault plane $\theta$ to the vertical axis (where $\theta = 90^\circ - \alpha$ and $\alpha$ is the fault dip from the horizontal axis) as follows (see Fig. C1)

$$ \sigma_n = \frac{1}{2}(\sigma_v + \sigma_h) - \frac{1}{2}(\sigma_v - \sigma_h)\cos(2\theta)\tau \\ = \frac{1}{2}(\sigma_v - \sigma_h)\sin(2\theta), $$

where: $\sigma_v = \sigma_1 = \rho g d$ and $\sigma_h = \sigma_3 = R \sigma_v$, $\rho = 2.4 \text{ kg m}^{-3}$ the rock density, $g = 9.8 \text{ m s}^{-2}$ the gravitational acceleration, $d$ is the fault depth, $R$ is a constant varying between 0 and 1 which represents the ratio between maximum and minimum principal stress. Here, we assume that the principal stress is vertical and of similar order

to the load of the overburden which is consistent with the available in situ stress measurements (Fig. 5a) where $R$ ranges between 0.5 and 0.9. Using a Mohr coulomb failure criterium (e.g. Ge & Saar et al. 2022) and considering one section of the fault at depth $d$ and its distance $h$ to the mine water level, we assume that failure on the fault occur when

$$ \tau/(\sigma_{\text{neff}} \tan(\Psi)) > 1, $$

where $\sigma_{\text{neff}} = \sigma_n - P$ is the effective normal stress, $\Psi$ is the internal angle of friction of the fault and $P$ is the fluid pore pressure which is given by $P = \rho_{\text{fluid}} gh$ where $\rho_{\text{fluid}} = 1 \text{ kg m}^{-3}$.

Fig. C1 shows the resulting failure conditions for faults at different depths for mine water high and low stands when varying three parameters namely $R$ (the ratio between vertical and minimum horizontal stress $\sigma_h/\sigma_v$), the fault dip and the internal angle of friction $\Psi$.
<figure><img src="image_14.png" /><figcaption>**Figure C1.** Failure conditions for normal faulting at the Fuveau swarm constrained by Mohr–Coulomb fault friction modelling (see text).</figcaption></figure>