# Spatial distribution of alteration and strength in a lava dome: Implications for large-scale volcano stability modelling

Agata Poganj, Michael J. Heap, Patrick Baud

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Agata Poganj, Michael J. Heap, Patrick Baud. Spatial distribution of alteration and strength in a lava dome: Implications for large-scale volcano stability modelling. Journal of Volcanology and Geothermal Research, 2025, 463, 10.1016/j.jvolgeores.2025.108344. insu-05082240

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# Spatial distribution of alteration and strength in a lava dome: Implications for large-scale volcano stability modelling

Agata Poganj<sup>a, *</sup>, Michael J. Heap<sup>a, b</sup>, Patrick Baud<sup>a</sup>

## ARTICLE INFO

### Keywords:

Point load test

Porosity

Bulk density

Uniaxial Compressive Strength

Hydrothermal alteration

Volcanic heterogeneity

Stability modelling implications

## ABSTRACT

Volcanoes are unstable heterogeneous structures that can host hazardous mass movements. Hydrothermal alteration can create weak zones that promote instability. Volcanic instability can be assessed using large-scale numerical models, which require accurate and reliable physical and mechanical rock input parameters. Volcano stability models are often constructed using discrete zones that are assigned homogeneous parameters, rarely accounting for the heterogeneity of volcanoes. Given that the range and distribution of alteration and rock strength in volcanoes are likely highly variable, these factors should be carefully determined for accurate modelling. Here, we performed an integrated field and laboratory study. We examined a total of 544 variably altered andesites from seven sampling locations at La Soufrière de Guadeloupe (Eastern Caribbean). Based on a visual assessment, we assigned the rocks an alteration grade index, from 1 (least altered) to 5 (most altered), and measured the strength of rocks in the field using a point load tester. The alteration and strength distribution maps we provide highlight the extreme heterogeneity of a volcanic structure. We provide a method for direct on-site conversion from field to laboratory strength. We find that porosity and strength increase and decrease, respectively, as a function of increasing alteration. The most altered rocks were weak regardless of their porosity, suggesting that the alteration is the primary factor governing strength. We conclude that a volcano can be heterogeneous in terms of alteration and strength, between and within the discrete zones. Therefore, if possible, material property heterogeneity should be incorporated in future volcanic stability models.

## 1. Introduction

Volcanoes are inherently unstable structures prone to hazardous mass movements, including rock slides and deep-seated slope deformation, which can develop into rock or debris avalanches leading to partial or total flank collapse (Voight et al., 1983; Siebert, 1984; López and Williams, 1993; McGuire, 1996; Reid et al., 2000; Cecchi et al., 2004; Apuani et al., 2005a; Boudon et al., 2007; Delmelle et al., 2015; Rosas-Carbajal et al., 2016; Dondin et al., 2016; Schaefer et al., 2019; Roverato et al., 2021; Heap et al., 2021a, 2023a, 2023b; Di Traglia et al., 2023a, 2023b). Mass movements at volcanoes, which have historically resulted in approximately 20,000 fatalities worldwide (Auker et al., 2013; Siebert et al., 1987, 2010; Siebert and Roverato, 2021), often occur in settings characterised by vast amounts of hydrothermally altered materials. Due to the presence of altered rocks in outcrops of rock avalanches and debris avalanche deposits (DADs), hydrothermal alteration is thought to play a significant role in destabilising volcanic

structures (Salaün et al., 2011; van Wyk de Vries and Francis, 1997; van Wyk de Vries et al., 2001; Reid et al., 2001; Zimbelman et al., 2004; Paguican et al., 2021).

Hydrothermal alteration induces physio-chemical changes in volcanic rocks and is a common process in volcanic environments. Resulting from the interaction of host rock with mixtures of magmatic and meteoric fluids, hydrothermal alteration generally reduces rock strength (Watters and Delahaut, 1995; Watters et al., 2000; John et al., 2008; del Potro and Hürlimann, 2009b; Pola et al., 2012; Heap and Violay, 2021). Experimental laboratory data have shown that low-strength altered rocks are also typically characterised by a high porosity or low density (Watters and Delahaut, 1995; del Potro and Hürlimann, 2009b). Alteration can increase porosity by creating secondary pores in the groundmass through dissolution processes (Pola et al., 2012). Conversely, depending on the type of hydrothermal alteration and its intensity, a porosity decrease can occur in rocks due to mineral precipitation within voids (Frolova et al., 2014; Wyering et al., 2014; Mordensky et al., 2018;

<footnote><sup>a</sup> Université du Strasbourg, CNRS, Institut Terre et Environnement de Strasbourg, UMR 7063, 5 rue René Descartes, Strasbourg F-67084, France</footnote>

<footnote><sup>b</sup> Institut Universitaire de France (IUF), Paris, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: poganj@unistra.fr (A. Poganj).</footnote>
Heap et al., 2020a; Heap et al., 2021b). Porosity-decreasing alteration mechanisms, such as silicification, in some cases, can strengthen volcanic rock (Frolova et al., 2014; Heap et al., 2020b). Nevertheless, many studies have shown that hydrothermal alteration increases volcanic instability, promoting flank collapse or debris avalanches (Voight, 1990; López and Williams, 1993; Watters and Delahaut, 1995; Day, 1996; Yamamoto et al., 1999; Reid et al., 2001, 2010; Watters et al., 2000; Zimbelman et al., 2003, 2004; Cecchi et al., 2004; Apuani et al., 2005a; Boudon et al., 2007; del Potro and Hürlimann, 2009b; Salaün et al., 2011; van Wyk de Vries et al., 2001; Rosas-Carbajal et al., 2016; Ball et al., 2015, 2018; Heap et al., 2021b; Carr et al., 2022; Heap et al., 2023b). For example, the capital of Guadeloupe in the Eastern Caribbean, Basse-Terre, was built on successive layers of thick, altered debris DADs deposited in the last 7700 years (Boudon et al., 2007). Similarly, nearly half of the volumetrically significant materials from the 2006 and 2010 Merapi (Indonesia) dome collapses were highly hydrothermally altered rocks (Darmawan et al., 2022). Finally, the 1998 avalanche of Casita volcano in Nicaragua, which killed 2500 people, revealed a pre-conditioned structure comprised of hydrothermally altered layers (Kerle and van Wyk de Vries, 2001).

The stability of volcanic structures is important for understanding and mitigating volcanic hazards, which can be explored using numerical modelling. The accuracy of large-scale stability models relies, in part, on a sensible selection of the physical and mechanical input parameters chosen to represent the rocks comprising the volcanic structure (Apuani et al., 2005b; Schaefer et al., 2023). Some seminal studies that investigated volcanic instability described a volcanic dome as a large homogeneous structure with uniform properties throughout (Voight, 1990; Reid et al., 2000). However, a volcanic structure is often composed of numerous discrete zones, i.e., faults, alteration pathways, and layers of lava, and as such, different zones within the volcano should be assigned different mechanical properties. Darmawan et al. (2022) revealed that the dome at Merapi concealed altered zones, which Harnett et al. (2022) used to demonstrate that the positioning of discrete altered zones significantly decreases dome stability. Incorporating diffuse heterogeneity (i.e., material property heterogeneity within discrete zones) in volcanic models, as described by Heap et al. (2023b), can increase dome instability and cause significant displacement corresponding better to field examples. While considerable studies used laboratory physical and mechanical property measurements to model volcanic stability (Reid et al., 2001; Apuani et al., 2005a; Ball et al., 2015, 2018; Wallace et al., 2022; Heap et al., 2021b; Harnett et al., 2022, Di Traglia et al., 2023a, 2023b), understanding the distribution of these values is poorly constrained.

Volcano stability modelling is currently hindered by our limited understanding of the distribution and heterogeneity of key modelling inputs, such as rock porosity and strength. Moreover, the variability in the porosity and strength of rock samples found in proximal locations (Frolova et al., 2014; Heap and Violay, 2021; Heap et al., 2022a, 2023a, 2023b; Schaefer et al., 2023; Kereszturi et al., 2023), highlights the need to not only determine the range of these properties, but also the distribution and heterogeneity across different locations within a particular volcanic environment. By gaining a better understanding of alteration and strength distributions and heterogeneity, we can provide more accurate and precise modelling inputs, enhancing the reliability of volcano stability models.

To understand the distribution of alteration, we use a visual assessment to assign the rock samples an alteration grade index (AGI), from 1 (least altered) to 5 (most altered). To understand the distribution of strength, we use a field tool—the point load tester (PLT)—that enables us to collect a large number of strength measurements. We use La Soufrière de Guadeloupe (henceforward, La Soufrière) (Guadeloupe, Eastern Caribbean) as a case study volcano. La Soufrière, an active andesitic stratovolcano, generated phreatomagmatic and hydrothermal activity in the past (Komorowski et al., 2005). Since 1992, volcanic activity at La Soufrière, including degassing, seismicity, and

deformation, has shown a clear unrest phase since 2018 (Moretti et al., 2020). This study is, therefore, essential for assessing future instability scenarios. For this study, we used our field methods, AGI and a PLT, to investigate the distribution of alteration and strength of the volcano, respectively, highlighting the heterogeneity of volcanic environments. Our results show how field methods can be useful to examine material property heterogeneity. Our goal is to better understand and quantify the heterogeneity of volcanic structures, which will allow for the development of heterogeneous, more complex, and more accurate numerical models that can better assess the stability of volcanic structures.

## 2. Methods and materials

### 2.1. Field methods

A field campaign was conducted in May 2023 at La Soufrière de Guadeloupe (Fig. 1a). We collected a large sample suite of variably altered rocks from the top and flanks of the active dome of La Soufrière to measure two field parameters, bulk density and point load strength. Our aim was to subsequently link these field parameters to laboratory-measured parameters, connected porosity and uniaxial compressive strength (UCS).

The rock samples were gathered from seven locations on the dome (Fig. 1a; Supplementary Figs. 1, 6, 12, 20, 25, 31, and 37): four landslides on the flanks of the dome (LS09, LS1, LS4, HAM), two locations on the top of the dome (CSC, TAR), and a section of the Ty fault located on the southern flank of the volcano (TY), a fault that crosscuts the dome in the NS direction. We collected a total of 544 rocks that differ in terms of colour and alteration (Supplementary Figs. 1–45). During rock collection, we were mindful of the sample size, sampling rocks of similar diameter, around 50 mm. As we will discuss below, rock size influences strength, and a correction factor should be applied when calculating point load strength. Our goal was to collect rock samples representative of the sampling location; therefore, we collected all samples randomly. We sampled the scars and deposits of landslides because they expose rock that best represents the altered interior of the dome. The sampling locations HAM and TY, and the two locations at the top of the dome (CSC and TAR; Supplementary Figs. 6 and 31), also showed evidence of active hydrothermal activity (hot rocks, outgassing). Landslide LS09, on the NE flank of the dome, occurred on 19th November 2009 and its scar contains variably altered rocks (Peruzzetto et al., 2019; Moretti et al., 2020; Heap et al., 2021b). Landslides LS1 and LS4 are on the SW and N flanks of the dome (Supplementary Figs. 20 and 25), respectively, and occurred in September 2022 during Tropical Storm Fiona. The deposit of LS1 contains largely unaltered rocks within the soil (Fig. 1b), while the deposits of LS4 show signs of alteration (Fig. 1c). Landslide HAM is a recent landslide on the W flank of Col de l'Échelle, a volcanic summit adjacent to La Soufrière, that exposed an altered outcrop and provided a deposit consisting of altered rocks (Supplementary Fig. 12). The two sampling locations on the top of the dome are both adjacent to active craters (TAR and CSC), the 100-m-deep crater Tarissan (TAR) and Cratère Sud Central (CSC). Cratère Sud Central is the current focus of the majority of the outgassing on the top of the dome (Peruzzetto et al., 2019). Lastly, we sampled an altered outcrop situated on the Ty fault (TY), in La Matylis Ravine, on the southern flank of the dome, where the fault crosses the road (Supplementary Fig. 37).

All the collected rock samples were assigned a visual macroscopic Alteration Grade Index (AGI), from pristine rock (AGI 1) to extremely altered rock (AGI 5) (examples shown in Fig. 2a). Similar to the Argillic Alteration Classification of Watters and Delahaut (1995), rocks were graded based on an inspection of their visible surfaces. In our assessment, we considered the colour (presence of discolouration), mineral lustre, presence of a porphyritic texture, and rock friability (Table 1). Our indexing was assigned solely based on the visual inspection in the field, unlike Watters and Delahaut (1995), who included microscopic observations in their classification scheme.
<figure><img src="image_4.png" /><figcaption>Fig. 1. a) Map of La Soufrière de Guadeloupe with white triangles showing the sampling locations: LS09, LS1, LS4, HAM, TAR, CSC, and TY (the map was taken from Google Earth). The inset shows the Guadeloupe archipelago, with the position of the volcano shown by the orange triangle. b) A photograph of landslide LS1 showing soil with embedded rocks. Landslide length is ~70 m. c) A photograph of landslide LS4, a recently formed altered outcrop. Landslide length is ~30 m.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 2. Field methods used in this study. a) A set of photographs of the alteration grading system that was assigned to every tested rock, with rocks representative of each category. AGI 1 (pristine rock) is represented by sample TAR35. AGI 2 (slightly altered rock) represented by sample LS09–26. AGI 3 (moderately altered rock) is represented by sample TY106. AGI 4 (severely altered rock) is represented by sample TY09. AGI 5 (extremely altered rock) is represented by samples HAM13 and HAM60. b) A schematic of the point load tester showing the hand pump that raises the conical platens in the loading cell. An irregularly-shaped rock sample, enlarged as part of the inset, is positioned between the conical platens. The inset shows all the dimensions that are necessary to measure: W₁, W₂, W₃, three widths, D, pre-failure diameter, and D', post-failure diameter (read the Methods and Materials section for a detailed explanation). The manometer displays the force under which the samples broke. c) A schematic showing the setup for the Archimedean bulk density measurement consisting of a balance, a tripod, and a sample holder. Numbers ① and ② represent the dry mass and immersed dry mass needed to obtain the dry bulk density, respectively.</figcaption></figure>

After obtaining AGI, we measured the dry bulk density of each sample using the Archimedean method (Fig. 2c). The configuration consisted of a digital balance (with a maximum capacity of 5000 g and

an accuracy of 0.1 g) placed on the top of a tripod, a sample holder attached to the base of the balance, and a water container (Fig. 2c). The rocks were first sun-dried for over 24 h. The rocks were then weighed on
<table><caption>**Table 1**
Alteration grade indices and description of our rock classification.</caption><thead><tr><th>Alteration Grade Index (AGI)</th><th>Discolouration and rock colour</th><th>Phenocryst lustre</th><th>Porphyritic texture</th><th>Rock friability</th></tr></thead><tbody><tr><td>Pristine rock (AGI 1)</td><td>None; dark light/grey</td><td>Glassy</td><td>Yes</td><td>Low</td></tr><tr><td>Slightly altered rocks (AGI 2)</td><td>Slight; dark/light grey with red or white streaks</td><td>Glassy, rarely dull</td><td>Yes</td><td>Low to Moderate</td></tr><tr><td>Moderately altered rocks (AGI 3)</td><td>Moderate; interchangeably dark/light grey and altered white, red, or yellow surfaces</td><td>Glassy to dull</td><td>Mostly present</td><td>Moderate</td></tr><tr><td>Severely altered rocks (AGI 4)</td><td>Severe; usually white, red, yellow, pink, or purple surfaces</td><td>Mostly dull if mineral phases are visible</td><td>Mostly not present</td><td>Moderate to high</td></tr><tr><td>Extremely altered rocks (AGI 5)</td><td>Total; white, red, yellow, pink, or purple surfaces</td><td>Mineral phases not visible</td><td>Not present</td><td>High</td></tr></tbody></table>

the top of the balance to obtain the weight of the dry rock ($m_d$) (Fig. 2c-①). Finally, the rocks were placed into the submerged sample holder to obtain their immersed dry mass ($m_{id}$) (Fig. 2c-②). For the immersed dry mass, we recorded the first value registered on the balance (the mass of porous samples would increase with time due to water imbibition). The dry bulk density ($\rho_b$) of the rocks was calculated as: $\rho_b = 1000m_d/(m_d-m_{id})$ [kg/m³]. Irregular specimen point load strength tests were then carried out on each sample using a point load tester (Fig. 2b). The International Society for Rock Mechanics (ISRM) has standardised an experiment and data treatment procedures that can be performed on rock cores (axial and diametral tests) and irregularly shaped rocks (irregular specimen tests). Minimal preparation is necessary prior to a point load test on an irregularly shaped rock, which is the procedure used in this study.

We selected the PLT as our field method to estimate strength because we predominantly found irregularly shaped rock blocks at the accessible outcrops at La Soufrière. While Schmidt hammers are somewhat effective at estimating the strength of outcrops (Harnett et al., 2019; Wang and Wan, 2019), their application is limited when dealing with loose and/or blocky surfaces. Additionally, threshold in Schmidt hammers is usually not as accurate in capturing the strength of soft (i.e. altered) rocks (del Potro and Hürlimann, 2009a). Finally, we highlight that the accessible outcrops at La Soufrière were also often heavily fractured, reducing the applicability of the Schmidt hammer, and most outcrops were covered with moss and other plant life.

First, the dimensions of all the rocks were measured: the diameter (D) along which the load was to be applied in the loading cell and three widths that captured the maximum ($W_1$), intermediate ($W_2$), and minimum ($W_3$) axis lengths of the samples (Fig. 2b). These three widths ($W_1$, $W_2$, and $W_3$) were then averaged to provide W. The intermediate width $W_2$ can be chosen arbitrarily depending on the sample cross section; for example, if the rock is tapered, one should choose a $W_2$ that is more similar to $W_1$ because this provides a more accurate width average. Importantly, after the point load test, we measured the diameter of the failure plane (D') (Fig. 2b). D', always equal to or smaller than D, was measured to account for cone penetration post-failure, which is highly advised for altered specimens (Basu and Aydin, 2006). If D' differed from the previously measured D, we used D' in the subsequent calculations. The ISRM (2007) standard for sample dimensions is a D/W ratio between 0.3 and 1, and the maximum length of the sample should be 0.5D. As previously mentioned, we sampled rocks based on their size. Due to the influence of sample size (size effect) on rock strength, we chose to sample fist-sized samples with similar D, around 50 mm. Despite the similar sizes of our samples, the size effect was accounted for using a correction factor (ISRM, 2007), explained below.

Once the dimensions of the samples were measured, we then measured their point load strength (PLS). To measure point load strength, the samples were positioned between the two conical platens of the point load tester (Fig. 2b). The hand pump was then pumped to push oil through the pipe, which raised the lower platen and deformed the sample (Fig. 2b). Macroscopic failure occurred between 10 and 60 s into the test. The maximum force endured by the sample during the test

was displayed on the manometer (Fig. 2b). The maximum load of the apparatus is 100 kN with a resolution of 0.01 kN. The digital manometer can measure a pressure of up to 70 MPa with an accuracy of 0.1 %, and the hand pump can withstand a pressure of 70 MPa.

The PLS of irregular specimens can be calculated using (ISRM, 2007):

$$PLS = P/D_e^2 \text{ [MPa]} \qquad (1)$$

where the failure load (P, visible on the manometer, Fig. 2b) is divided by the equivalent core diameter, $D_e$ (ISRM, 2007):

$$D_e = \sqrt{4WD'} / \pi \qquad (2)$$

where W signifies the average width, and D' signifies the post-failure diameter. As proposed by Protodyakonov and Voblikov (1957) and Broch and Franklin (1972), the collected samples should be of a similar diameter. In our case, the average pre-failure and post-failure dimensions were $D = 53 \pm 10.5$ mm and $D' = 44 \pm 10$ mm, respectively. To normalise the diameter values to 50 mm, we applied a correction factor (F): $F = (D_e/50)^{0.45}$ (ISRM, 2007). To obtain PLS for 50 mm-diameter samples, PLS$_{50}$, we simply multiply the correction factor (F) by the PLS (ISRM, 2007): PLS$_{50} = F \text{ PLS [MPa]}$. The size and shape of the rock sample influence the strength conversion process, and while ISRM (2007) suggests using an exponent of 0.45, some studies found an exponent value of up to 0.93 (Turk and Dearman, 1986; Panek and Fannon, 1992). Estimating the value of this exponent relies on using samples of the same rock (i.e., with similar physical and mineralogical properties) that vary in size and shape. This method, therefore, can be used to describe how the strength of one rock type changes exclusively based on sample size and shape. It is possible to calculate the exponent of the correction factor if the theoretical PLS value for 50 mm is obtained from a graph of P as a function of $D_e^2$, which is suggested for less heterogeneous sample suites (more information provided in ISRM, 2007; Panek and Fannon, 1992; Yin et al., 2017). Our sample suite, contrarily, contains a wide range of rock samples characterised by different physical and mineralogical properties. Not only it is extremely challenging to find samples of varied sizes and shapes that have similar physical and mineralogical properties at La Soufrière, but it is also likely that each rock type encountered would be characterised by its own unique exponent. It is for these reasons that we chose to collect samples of a comparable size and shape in the field, as explained above. Nevertheless, and to account for the potential variability in the exponent of the size correction factor, we used an exponent of 0.45 but also provided error bars for exponents ranging from 0.25 to 0.65.

## 2.2. Laboratory methods

Following point load testing, a small piece of each of the rock samples was taken to the laboratory at the Strasbourg Institute of Earth and Environment (France) for triple-weight water saturation porosity measurements (see imbibition method by Guéguen and Palciauskas, 1994), along with 41 larger pieces that were cored to prepare samples for UCS
experiments. 13 micrographs were also made to examine and identify rock mineral and alteration phases under reflective and transmitted light using a LEICA DM 2500 P microscope. The micrographs were photographed by a LEICA DFC425 digital microscope camera. The micrographs were chosen as representatives of the different alteration classes (Fig. 3). We chose two rocks from AGI 1, two rocks from AGI 2, five rocks from AGI 3, four rocks from AGI 4, and one rock from AGI 5. Preparing micrographs for AGI 5 is challenging due to their friability.

The triple-weight water saturation experiment consisted of measuring three weights: vacuum-dried rock mass ($m_d$), water-saturated rock mass ($m_w$), and immersed water-saturated rock mass ($m_{wi}$). The connected porosity (henceforward porosity, $\phi$) was calculated using: $\phi = (m_w - m_d)/(m_w - m_{wi})$. For $m_d$ measurement, the samples were dried on a hot plate for several days and then vacuum-dried in an oven at 40 °C for at least 24 h. To measure $m_w$, the samples were vacuum-saturated with water, and to measure $m_{wi}$, we used a sample holder similar to the one used in the field (Fig. 2c).

The 41 larger rock pieces provided 115 cylindrical cores for laboratory UCS experiments (examples shown in Fig. 3). The samples were cored to a diameter of 20 mm, precision ground to a nominal length of 40 mm, washed in tap water, dried on a hot plate for several days, and then dried in a vacuum oven at 40 °C for at least 24 h. After obtaining bulk volumes ($V_B$) of the rock cores by measuring their dimensions, a helium pycnometer (AccuPyc II 1340) was used to measure the skeletal volume ($V_S$) of the samples to calculate connected porosity: $\phi = 1 - V_S/V_B$. The connected porosity measured using the triple-weight method and the pycnometer is considered comparable (as shown by Farquharson et al., 2015). However, we note that, for given rock collected in the field (e.g., TAR35), different samples from the parent block were measured using the different techniques: the triple-weight method was used to measure a small piece of the parent block, and the pycnometer was used to measure cylindrical samples cored from a larger piece of the parent block.

Dry UCS was measured on 115 cylindrical rock samples. Samples were deformed under ambient temperature and pressure conditions until macroscopic failure or until 1 % strain (when macroscopic failure was not obvious) at an axial strain rate of $10^{-5} s^{-1}$. During deformation, we used a linear variable differential transducer (LVDT) to measure axial displacement and a load cell to measure axial load, respectively. Axial displacement and axial load were converted to axial strain and axial stress using the sample dimensions and the stiffness of the experimental setup.

# 3. Results

## 3.1. Macroscopic observations

We described and photographed all of the rocks, which were divided into five alteration groups, from AGI 1 to AGI 5 (Table 1, representatives are shown in Fig. 3, and all samples are shown in Supplementary Figs. 1–45). Macroscopic observations include describing the exterior of the rock samples, such as AGI, and the interior of the rock samples, such as any fractured surfaces of the rocks. The pristine rocks (AGI 1) were characterised by porphyritic textures, high hardness and density, and light to darker grey colours. The visible mineral phases were plagioclases (<5 mm phenocrysts) and glassy pyroxenes. Some of the sample surfaces were visibly porous (Supplementary Figs. 21.05, 24.49, and 32.11); however, no disjointing or macrofracturing was observed. Noticeably, the rock samples from sampling location LS1 showed porous surfaces without visible alteration (Supplementary Figs. 21.05, 22.29, and 24.49). The fractured rock surfaces were grey and showed a porphyritic texture containing dull-lustred plagioclases (Supplementary Figs. 21.05, and 22.29). The slightly altered rocks (AGI 2) were mostly dark to light grey in colour, sometimes with red, yellow, or white discolorations. After the point load tests, the fractured surfaces on some of the AGI 2 rocks showed phenocrysts of plagioclases (up to 5 mm) and

pyroxenes. The fractured surfaces were light grey in colour and showed a porphyritic texture. We note that some fractured surfaces had oxidised red-to-yellow-coloured spots (Supplementary Fig. 45.b91), and some of the rocks had zonal alteration rims (Supplementary Figs. 19.b14 and 29.61). The moderately altered rocks (AGI 3) were grey in colour, with an increased presence of yellow and white discolouration (CSC, Supplementary Figs. 9.33, 9.35, and 9.39; TY, Supplementary Figs. 43.88, 43.95, and 43.96). The plagioclase phenocrysts were observed to have a dull lustre. We observed that some of the rock samples had macrofractures before the PLS testing. After the PLS testing, some of the fractured surfaces showed zonal alteration rims (Supplementary Figs. 11.b32, 11.b66, and 19.b17); however, the porphyritic texture was present and dull-lustred altered plagioclases were visible. Some fractured surfaces showed a striking white and yellow discolouration (Supplementary Fig. 5.b46) or brown and red colours (Supplementary Fig. 11.b19), while some had a very thin alteration rim and an interior characterised by a perfectly intact andesitic texture (Supplementary Figs. 11.b32 and 45.b22). The severely altered rocks (AGI 4) were fragile and friable. The colours associated with the alteration of AGI 4 rocks were white, yellow, red, purple, and brown (Supplementary Figs. 19.b22 and 19.b98). Porphyritic textures were less visible in alteration group AGI 4. Furthermore, the fractured surfaces displayed a wide range of alteration colours – white, yellow, red, and purple (Supplementary Figs. 5.b44, 45.b30, and 45.b39). Some fractured surfaces still had a visible porphyritic texture; however, it was often associated with a thick alteration rim (Supplementary Fig. 30.b70). The extremely altered rocks (AGI 5) chipped and broke at the touch and were very fragile and friable rock samples. We observed more pronounced discolouration on their outer surfaces—red, pink, purple, yellow, white, and grey—without visible porphyritic textures. Similarly, the fractured surfaces of AGI 5 rock samples showed white, yellow, red, and purple alteration colours without any visible porphyritic texture (Supplementary Figs. 19.b55 and 19.b65).

## 3.2. Microscopic observations

We inspected 14 micrographs of rock samples from La Soufrière. The rock samples sent for micrographs were chosen from the 41 large rock pieces retrieved for the uniaxial compressive experiments based on their respective AGI and physical features. In Fig. 3, we provide a macroscopic photograph, a photograph of a rock core, and a photomicrograph of samples TAR57, CSC06, LS09–46, HAM98, and LS4–70. From the microscopically inspected rocks, CSC06 and HAM98 were the least and the most porous, respectively (Fig. 3). A summation of microscopical characteristics is provided for a micrograph from most alteration grades. Microscopic observations were made after the AGI was assigned and, therefore, are specific to the rock sample that was described. Regardless of the AGI classification, all the micrographs from La Soufrière exhibited a common porphyritic texture with plagioclase as the dominant phenocryst, often showing twinning patterns and altered rims. Overall, the samples indicate an increasing degree of alteration with increasing AGI, with plagioclase alteration being a consistent feature.

The micrographs of sample TAR57 (AGI 1) display large plagioclase phenocrysts alongside smaller phenocrysts of magnetite and pyroxene, with a pilotassitic groundmass of feldspars and magnetite (Fig. 3a). The plagioclase crystals generally formed prismatic crystal habits with polysynthetic twinning or oscillatory zoning. Notably, the crystals often showed alteration close to the phenocryst border in the form of darkened spots.

The micrograph of sample CSC06 (AGI 2) shows plagioclase phenocrysts under 3 mm, with anhedral magnetite and a groundmass with darker altered spots (Fig. 3b). The plagioclase phenocrysts showed more pervasive alteration close to the phenocryst border, and the alteration rims were thicker and darker. Additionally, the replacement features that appear on the crystals in the form of darkened spots are larger and denser.
<figure><img src="image_6.png" /><figcaption>b) CSC06 (AGI 2, porosity = 0.04), rock core = CSC06-1</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>c) LS09-46 (AGI 3, porosity = 0.21), rock core = LS09-46-2</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>d) HAM98 (AGI 4, porosity = 0.50), rock core = HAM98-1</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>e) LS4-70 (AGI 4, porosity = 0.41), rock core = LS4-70-3</figcaption></figure>

<figure><img src="image_10.png" /></figure>

**Fig. 3.** Macroscopic photographs, core samples, and photomicrographs of rock samples from La Soufrière. a) A set of images showing sample TAR57, alteration grade index 1, and porosity 0.13. The set represents a macroscopic image of the sample TAR57, an image of cylindrical rock core TAR57-3, and a photomicrograph of rock sample TAR57. b) A set of images showing sample CSC06, alteration grade index 2, and porosity 0.04. The set represents a macroscopic image of the sample CSC06, an image of cylindrical rock core CSC06-1, and a photomicrograph of rock sample CSC06. c) A set of images showing sample LS09-46, alteration grade index 3, and porosity 0.21. The set represents a macroscopic image of the sample LS09-46, an image of cylindrical rock core LS09-46-2, and a photomicrograph of rock sample LS09-46. d) A set of images of samples HAM98, alteration grade index 4, and porosity 0.50. The set represents a macroscopic image of the sample HAM98, an image of cylindrical rock core HAM98-1, and a photomicrograph of rock sample HAM98. e) A set of images showing sample LS4-70, alteration grade index 4, and porosity 0.41. The set represents a macroscopic image of the sample LS4-70, an image of cylindrical rock core LS4-70-3, and a photomicrograph of rock sample LS4-70. Kfs = K-feldspar, mag = magnetite, pl = plagioclase, pyx = pyroxene.
The micrograph of sample LS09–46 (AGI 3) shows plagioclases with mild sericitisation, sanidine crystals, and fractured pyroxenes, with oxides precipitated in the pores (Fig. 3c). The plagioclase phenocrysts are euhedral but are more altered than those present in TAR57 and CSC06. The replacement features in the plagioclase phenocrysts are prevalent, usually spreading through most of the phenocryst. Some plagioclase phenocrysts show crystallisation of brown biotite. The pilotassitic groundmass is not visible due to alteration.

The micrograph of samples LS4–70 (AGI 4) and HAM98 (AGI 4) both exhibit advanced alteration, with HAM65 showing extensive sericitisation and an opaque, altered groundmass. In micrograph LS4–70, the pores and the groundmass are filled with an altered dark brown-coloured coating, while the plagioclases show replacement textures (Fig. 3e). In micrograph HAM98, plagioclase phenocrysts show polysynthetic twinning and sericitisation. We also often found well-preserved plagioclase and pyroxenes forming aggregates. The micrograph of the sample HAM98 showed that it is characterised by an altered groundmass and a very vast network of pores.

### 3.3. Field data

#### 3.3.1. Alteration and strength distribution map

In total, we classified 27 rocks as AGI 1, 73 rocks as AGI 2, 150 rocks as AGI 3, 127 rocks as AGI 4, and 166 rocks as AGI 5. We see that the number of rocks increases with the alteration grade. However, the distribution of rocks classified as AGI 1 to AGI 5 can vary significantly within and between the different sampling sites. Fig. 4 shows a map of the dome in which the distribution of AGI is visualised by a pie chart for each sampling location (LS09, LS1, LS4, HAM, CSC, TAR, and TY). The number of rocks per alteration grade at each sampling point is indicated within the segments of the pie charts. Sampling locations LS1 and TAR have the largest proportion of less-altered rocks (AGI 1 and AGI 2), 50 and 54 % at their respective sampling locations. By contrast, LS09, LS4, CSC, HAM, and TY contain only a few less-altered rock samples per sampling site, around or less than 10 % per sampling location. Furthermore, AGI 1 was only assigned in one other location, LS4, to two other rock samples, which shows that most of the less-altered rocks were found at only two sampling locations: LS1 and TAR. The most-altered rocks, AGI 4 and AGI 5, are most abundant in the sampling location HAM, accounting for almost 90 % of the samples at that sampling

<figure><img src="image_11.png" /><figcaption>**Fig. 4.** Alteration and strength distribution map for La Soufrière dome. For the alteration distribution, sampling points (LS09, CSC, HAM, TY, LS1, TAR, LS4) featured in this study are assigned a pie chart showing the number of rock samples per alteration grade index (AGI). Whereas, for the strength distribution, charts show the percentage of rock samples in each strength interval (n) as a function of point load strength (PLS) per sampling location.</figcaption></figure>
location. Notably, HAM stands out from the other sampling locations due to its very high proportion of extremely altered rocks (AGI 5). The sampling sites LS4, LS09, and TY are roughly similar in their alteration grade proportions. Indeed, around 60 % of the rock samples from sampling locations LS4, LS09, and TY are from the AGI 4 and AGI 5 groups. The rock samples from the location CSC are less altered than those at LS4, LS09, and TY; less than 50 % of rocks from the sampling location CSC are from the AGI 4 and AGI 5 groups. By contrast, LS1 and TAR have barely any rock samples from the AGI 4 and AGI 5 groups, and they only have one and two, respectively, rocks from the extremely altered group (AGI 5).

We also include strength distribution graphs for each sampling location, where PLS signifies the point load strength, and the *n* signifies the percentage of rock samples in each strength interval (Fig. 4). Therefore, the sampling locations with a higher proportion of weak rocks would show a peak closer to the y-axis, while locations with a higher proportion of strong rocks would show peaks further from the y-axis. In the graphs in Fig. 4, we observe that the majority of the sampling locations, such as HAM, TY, LS4, and LS09, show a high proportion of weak rock samples. However, we note that sampling site HAM has a slightly higher proportion of weaker rock samples than TY, LS4, and LS09. At sampling locations LS1, CSC, and TAR, the majority of tested rocks were weak. Despite this, there are more of the stronger rocks at sampling locations LS1, CSC, and TAR than at TY, LS4, and LS09 (Fig. 4). In sampling locations LS1, CSC, and TAR, there are more rocks classified as AGI 1, AGI 2 and AGI 3 than at locations TY, LS4, and LS09, suggesting a link between alteration and strength (discussed in detail below). Finally, we observe that we rarely found very strong rock samples (Fig. 4).

### 3.3.2. Field strength and density as a function of AGI

The volcanic dome at La Soufrière is heterogeneous, which we convey using alteration grade index, bulk density, and point load strength. We measured rock samples across seven sampling locations (see our Methods and Materials section). In Fig. 5, we present how the PLS (Fig. 5a) and bulk density (Fig. 5b) change as a function of AGI, and how the PLS changes as a function of bulk density (Fig. 5c). Readers not interested in specifications and values regarding our field results are advised to look at Table 2, which is used to summarise our result section, and/or to skip to Section 4.

We observe that PLS decreases with the increase of AGI (Table 2 and Fig. 5a). The AGI 1 median and mean point load strength values are 2.87 and 3.32 MPa, respectively. The extension of the box is skewed towards lower values. Our AGI 1 PLS data range from 0.2 to 6.45 MPa, noting two outliers—rock samples LS4–36 and TAR57—which have strength values of 7.36 and 7.86 MPa, respectively (Supplementary Figs. 28.36, and 35.57). The AGI 2 median and mean PLS values are 3.06 and 3.41 MPa, respectively. The extension of the box is skewed towards lower values. Our AGI 2 PLS data range from 0.19 to 7.57 MPa, apart from rock samples CSC05, CSC06 and CSC07 which have PLS values of 10.05, 10.56, and 11.30 MPa, respectively (Supplementary Figs. 7.5, 7.6, and 7.7). Rock samples CSC05, CSC06, and CSC07 were the strongest of the whole sample suite. We observe that the median and mean PLS values of rocks categorised as AGI 2 are slightly higher than those of AGI 1. The AGI 3 median and mean PLS values are 1.26 and 1.86 MPa, respectively. The extension of the box is significantly skewed towards lower values, indicating that the PLS values for this alteration grade were lower than those for AGI 1 and AGI 2. Our AGI 3 PLS data range from 0.04 to 5.92 MPa, apart from rock samples CSC03, CSC32, CSC69, TAR04, TY01, and TY50, which have PLS values of 6.74, 8.12, 6.48, 6.31, 7.03, and 8 MPa, respectively (Supplementary Figs. 7.03, 8.32, 11.69, 32.04, 38.01, and 41.50). The AGI 4 median and mean PLS values are 0.61 and 1.14 MPa, respectively. The extension of the box is significantly skewed towards lower values. Our AGI 4 PLS data range from 0.04 to 3.2 MPa, noting a large decrease in the PLS range, which is over 2.5 MPa smaller than that of AGI 3. The AGI 4 dataset contains a total of 11 outliers, the majority of

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /><figcaption>**Fig. 5.** Field data from this study. (a, b) Box-and-whisker distribution plots of the point load strength and the bulk density as a function of alteration grade index (AGI). The boxplots are comprised of boxes and whiskers. The boxes show the interquartile range (IQR) that represents the range in which 50 % of dataset values fit, an intermediate line that represents the median value of the datasets, and the cross mark that represents the mean value of the datasets. The whiskers show the range of the datasets, and the circles represent the outliers. (c) Point load strength (PLS) as a function of the bulk density. The colour of the data-points corresponds to their AGI. The error bars account for size correction factor (see our Methods and Materials section) ranging from 0.25 to 0.65, while the datapoints correspond to the size correction factor 0.45.</figcaption></figure>

which are rock samples from sampling location CSC. The AGI 5 median and mean PLS values are 0.19 and 0.33 MPa, respectively, the lowest of all the alteration grades. The extension of the box is skewed towards lower values. Our AGI 5 PLS data range from 0.01 to 0.84 MPa and is the dataset with the smallest range and weakest rock samples. The AGI 5 dataset contained ten outliers in total, the majority of which were from the sampling location HAM. Here, we highlight the decreasing trend in median and mean strength values as a function of increasing AGI, apart from AGI 2, which showed a slightly higher median value (3.06 MPa) and a similar mean value (3.41 MPa) than that of AGI 1 (2.87 and 3.32 MPa, respectively). The range of PLS decreases as AGI increases. We
<caption>**Table 2**</caption>

Summary of point load strength (PLS) and bulk density data for different alteration grades (AGI). The table includes the median, mean, and range of porosity and PLS values, along with identified outliers for both properties.

<table><thead><tr><th rowspan="2">Alteration Grade Index (AGI)</th><th colspan="3">PLS (MPa)</th><th rowspan="2">Outliers</th><th colspan="3">Bulk density (kg/m³)</th><th rowspan="2">Outliers</th></tr><tr><th>Median</th><th>Mean</th><th>Range</th><th>Median</th><th>Mean</th><th>Range</th></tr></thead><tbody><tr><td>AGI 1</td><td>2.87</td><td>3.32</td><td>0.2-6.45</td><td>LS4-36 (7.36), TAR57 (7.86)</td><td>2380.5</td><td>2342.7</td><td>2093.3-2509.2</td><td>None</td></tr><tr><td>AGI 2</td><td>3.06</td><td>3.41</td><td>0.19-7.57</td><td>CSC05 (10.05), CSC06 (10.56), CSC07 (11.30)</td><td>2315.1</td><td>2295.6</td><td>2011.7-2679.7</td><td>LS1-44 (1874.8), TAR64 (1594.9), TAR72 (1860.6), TY91 (1670)</td></tr><tr><td>AGI 3</td><td>1.26</td><td>1.86</td><td>0.04-5.92</td><td>CSC03 (6.74), CSC32 (8.12), CSC69 (6.48), TAR04 (6.31), TY01 (7.03), TY50 (8)</td><td>2098.4</td><td>2058.6</td><td>1363.1-2632.7</td><td>HAM15 (1273.8)</td></tr><tr><td>AGI 4</td><td>0.62</td><td>1.14</td><td>0.04-3.2</td><td>11 outliers, mostly from CSC</td><td>1817.8</td><td>1805.2</td><td>1061.4-2632.7</td><td>All HAM (lower range)</td></tr><tr><td>AGI 5</td><td>0.19</td><td>0.33</td><td>0.01-0.84</td><td>10 outliers, mostly from HAM</td><td>1490</td><td>1466.7</td><td>713.62-2238.5</td><td>None</td></tr></tbody></table>

observe that the mean values are always higher than the median value, as the mean is influenced by outliers. Most of the outliers in the groups AGI 2, AGI 3, and AGI 4 were rock samples from sampling location CSC while, in the AGI 5 group, most of the outliers were from sampling location HAM.

We observe that the bulk density decreases with the increase of AGI (Table 2 and Fig. 5b). The AGI 1 values for median and mean bulk density are 2380.5 and 2342.7 kg/m³, respectively. The extension of the box is skewed towards the higher values. Our AGI 1 data range from 2093.3 to 2509.2 kg/m³, without outliers in the dataset. The AGI 2 values for median and mean bulk density are 2315.1 and 2295.6 kg/m³, respectively. There is no apparent preferential skewness in the dataset. The AGI 2 data range from 2011.7 to 2679.7 kg/m³, apart from four outliers in the dataset: LS1-44, TAR64, TAR72, and TY91, which have

bulk density values of 1874.8, 1594.9, 1860.6, and 1670 kg/m³, respectively (Supplementary Figs. 23.44, 35.64, 36.72, and 43.91). The AGI 1 and AGI 2 datasets have a smaller range of bulk density values in comparison to the rest of the groups. The AGI 3 values for median and mean bulk density are 2098.4 and 2058.6 kg/m³, respectively. The extension of the box is slightly skewed towards the higher values. Our AGI 3 data range from 1363.1 to 2632.7 kg/m³, with one outlier in the dataset, rock sample HAM15, which has a bulk density value of 1273.8 kg/m³ (Supplementary Fig. 13.15). The AGI 4 values for median and mean bulk density are 1817.8 and 1805.2 kg/m³, respectively. There is no apparent preferential skewness in the dataset. Our AGI 4 data range from 1061.4 to 2632.7 kg/m³, with five outliers in the dataset. All of the outliers are from the sampling location HAM and are all lower than the data range. The AGI 5 values for median and mean bulk density are 1490

<figure><img src="image_15.png" /><figcaption>**Fig. 6.** Laboratory data from this study. a, c) Box-and-whisker distribution plots of the porosity and UCS as a function of alteration grade index (AGI). The boxplots are comprised of boxes and whiskers. The boxes show the interquartile range (IQR) that represents the range in which 50 % of dataset values fit, an intermediate line that represents the median value of the datasets, and the cross mark that represents the mean value of the datasets. The whiskers show the range of the datasets, and the circles represent the outliers. b) Representative uniaxial stress-strain curves for each alteration grade index (AGI). In the inset, we provide less visible stress-strain curves, LS4-70 and HAM65-1. In the legend, we provide names of rock cores corresponding to the curve. d) Uniaxial compressive strength (UCS) as a function of the porosity. The colour of the datapoints corresponds to their AGI. The legend in the last panel (d) applies to all the graphs in this figure.</figcaption></figure>
and 1466.7 kg/m³, respectively. There is no apparent preferential skewness in the dataset. Our AGI 5 data range from 713.62 and 2238.5 kg/m³, without outliers in the dataset. Finally, we highlight that the interquartile range of bulk density decreases as a function of increasing AGI. In all datasets, the mean value is always slightly lower than the median value.

PLS increases as a non-linear function of bulk density (Fig. 5c). The PLS values vary from 0.012 MPa to 11.296 MPa, and the bulk density values vary from 713.6 to 2674.2 kg/m³ (Fig. 5c). The range of PLS for the bulk density of around 2000 kg/m³ is from 0.11 MPa to 5.75 MPa, showing a large variability. We highlight that, despite their very variable bulk densities, the rocks categorised as AGI 5 all have a PLS less than 2.1 MPa (Fig. 5c).

To conclude, our field data shows that both PLS and the bulk density decrease as a function of increasing AGI (Table 2, Fig. 5a and b), and that PLS increases as a function of the bulk density (Fig. 5c). The data highlight a very wide range of both strength and density and strength values between and also within the sampling sites. We note that weak rocks were found in each AGI category. We find that the less-altered rocks (AGI 1 and AGI 2) are dense and on average, strong, whereas the extremely altered rocks (AGI 5) are weak, while their density is very variable (Fig. 5).

### 3.4. Laboratory data

Small and large pieces of the blocks measured in the field were also brought back to and measured in the laboratory. We measured the connected porosity of small rock pieces from each of the field-measured blocks using the triple-weight method. In Fig. 6a, we show connected porosity as a function of AGI. The larger rock pieces that were brought back were cored, obtaining 115 rock cores, and prepared for pycnometer-porosity measurements and UCS experiments. In Figs. 6b, 6c, and 6d, we present representative stress-strain curves from the UCS experiments (Fig. 6b), UCS as a function of AGI (Fig. 6c), and UCS as a function of connected porosity (Fig. 6d), respectively. Readers not interested in the specifications and values regarding our laboratory results are advised to look at Table 3, which is used to summarise our data, and/or to skip to Section 4.

We observe that the porosity increases with the increase of AGI (Table 3 and Fig. 6a). The AGI 1 values for median and mean porosity are 0.17 and 0.16, respectively. The extension of the box is skewed towards the lower values, and it represents the smallest range of porosity values in comparison to the other alteration grades. Our AGI 1 porosity data range from 0.09 to 0.27, without outliers in the dataset. The AGI 2 values for median and mean porosity are 0.15 and 0.16, respectively. The extension of the box is skewed towards the lower values. Our AGI 2 porosity data range from 0.02 to 0.35, with two outliers in the dataset, rock samples TAR64 and TAR72 (Supplementary Figs. 35.64 and 36.72), which have porosity values of 0.46 and 0.40, respectively. Notably, TAR64 and TAR72 appear to be outliers in both the bulk density (Fig. 5b) and the porosity data (Fig. 6a). The AGI 3 values for median and mean porosity are 0.22 and 0.24, respectively. The extension of the box is skewed towards the lower values. Our AGI 3 porosity data range

from 0.02 to 0.54, with two outliers in the dataset, rock samples LS4–34 and TAR65 (Supplementary Figs. 28.34 and 36.65), which have porosity values of 0.57 and 0.56, respectively. The AGI 4 values for median and mean porosity are both 0.32, respectively. The extension of the box is skewed towards the lower values. Our AGI 4 porosity data range from 0.06 to 0.7, without outliers in the dataset. The AGI 5 values for median and mean porosity are both 0.47, respectively. The extension of whiskers is skewed towards the higher porosity values. Our AGI 5 porosity data range from 0.12 to 0.79, without outliers in the dataset. All datasets show a high variability in porosity and an increase in range of the data values from AGI 1 (0.09–0.27) to AGI 5 (0.12–0.79). Furthermore, we observe an increase in median and mean porosity values as AGI increases, although the median and mean porosity values of AGI 2 (0.15 and 0.16, respectively) are slightly lower than those of AGI 1 (0.17 and 0.16, respectively). There are only a few outliers in the groups AGI 2 and AGI 3, the majority from sampling location TAR.

We provide representative uniaxial stress-strain curves of each AGI that show typical brittle behaviour (Fig. 6b). The first portion of the curve is marked by a non-linear stress increase as a function of strain, often attributed to the closing of pre-existing microcracks in the sample. Next, we observe elastic deformation. The curves return to non-linear behaviour as new microcracks form, signified by curve inflection. Lastly, when microcracks propagate and coalesce to form a macroscopic fracture, stress decreases as a function of axial strain (the “stress drop”), and the experiment is terminated. For TAR57–2 (AGI 1), CSC17–2 (AGI 2), TAR76–2 (AGI 3), LS4–70–2 (AGI 4), and HAM65–1 (AGI 5), the uniaxial compressive strength was 118.2, 109.6, 45.5, 18.4, and 3.36 MPa, respectively (Fig. 6b). The chosen stress-strain curves show a consecutive decrease in compressive strength as the AGI increases.

We observe that UCS decreases as a function of increasing AGI (Table 3 and Fig. 6c), apart from the AGI 3 dataset (the mean and median UCS for AGI 3 is higher than those for AGI 2). The datasets show a large variability in UCS values and an overall decrease in range of the UCS values as AGI increases. The AGI 1 values for median and mean UCS are 69.7 and 82.4, respectively. The extension of the box is skewed towards the lower values, while our AGI 1 UCS data range from 57.4 to 149.0 MPa, without outliers in the dataset. The AGI 2 values for median and mean UCS are 43.4 and 61.3 MPa, respectively. The extension of the box is slightly skewed towards the lower values, while our AGI 2 UCS data range from 37.2 to 133.0 MPa. The AGI 3 values for median and mean UCS are 67.8 and 67.3 MPa, respectively. The extension of the box is skewed towards the lower values. Our AGI 3 UCS data range from 31.9 to 181.1, without outliers in the dataset. The AGI 4 values for median and mean UCS are 15.2 and 29.8 MPa, respectively. The extension of the box is skewed towards the lower values. Our AGI 4 UCS data range from 1.48 to 90.5 MPa, with one outlier in the dataset, rock sample HAM22–2 with the UCS value of 104.5 MPa (Supplementary Figs. 14.22 and 19. b22). The AGI 5 values for median and mean UCS are 3.34 and 6.52 MPa, respectively. The extension of whiskers is skewed towards the lower UCS values. Our AGI 5 UCS data range from 1.62 to 17 MPa, without outliers in the dataset. Almost all the datasets show a high variability in the UCS, except rock samples from the AGI 5 (1.62–17 MPa). Furthermore, median values were always significantly lower than

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

Summary of uniaxial compressive strength (UCS) and porosity data for different alteration grade indices (AGI). The table includes the median, mean, and range of porosity and UCS values, along with identified outliers for both properties.

<table><thead><tr><th rowspan="2">Alteration Grade Index (AGI)</th><th colspan="3">UCS (MPa)</th><th rowspan="2">Outliers</th><th colspan="3">Porosity</th><th rowspan="2">Outliers</th></tr><tr><th>Median</th><th>Mean</th><th>Range</th><th>Median</th><th>Mean</th><th>Range</th></tr></thead><tbody><tr><td>AGI 1</td><td>69.7</td><td>82.4</td><td>57.4–149.0</td><td>None</td><td>0.17</td><td>0.16</td><td>0.09–0.27</td><td>None</td></tr><tr><td>AGI 2</td><td>43.4</td><td>61.3</td><td>37.2–133.0</td><td>None</td><td>0.15</td><td>0.16</td><td>0.02–0.35</td><td>TAR64 (0.46), TAR72 (0.40)</td></tr><tr><td>AGI 3</td><td>67.8</td><td>67.3</td><td>31.9–181.1</td><td>None</td><td>0.22</td><td>0.24</td><td>0.02–0.54</td><td>LS4–34 (0.57), TAR65 (0.56)</td></tr><tr><td>AGI 4</td><td>15.2</td><td>29.8</td><td>1.48–90.5</td><td>HAM22–2 (104.5 MPa)</td><td>0.32</td><td>0.32</td><td>0.06–0.70</td><td>None</td></tr><tr><td>AGI 5</td><td>3.34</td><td>6.52</td><td>1.62–17.0</td><td>None</td><td>0.47</td><td>0.47</td><td>0.12–0.79</td><td>None</td></tr></tbody></table>
mean values except for the AGI 3 group. Notably, the AGI 3 dataset shows significantly larger variability and higher median and mean UCS values, not following the general decrease in strength. The mean values were higher than the median values in all the datasets, apart from AGI 3, which showed almost equal median and mean values (67.3 and 67.8, respectively).

UCS decreases as a non-linear function of increasing porosity (Fig. 6d). The range of porosity, measured using the pycnometer, varies from 0.01 to 0.71, while the range for UCS varies from 1 MPa to 181.1 MPa, respectively. We observe a large variability in strength for a given porosity below a porosity of 0.2; for example, at a porosity of 0.2, the strength values range from 1 MPa to 50 MPa. The variability in UCS at porosities greater than 0.2 is less, although this may reflect the fact that the majority of samples contained a porosity less than 0.2.

In conclusion, our laboratory data show that porosity and UCS increase and decrease, respectively, as a function of increasing AGI (Table 3, Figs. 6a and 6c), and that UCS decreases as a function of the porosity (Fig. 6d). The data highlight a very wide range of both porosity and UCS values. We note that weak rocks were found in each AGI category. However, the AGI 5 group had a very wide range of connected porosities (0.12–0.79), while the UCS values were in a very narrow range (1.62–17 MPa).

# 4. Linking the field data to the laboratory data

## 4.1. Heterogeneity of the volcanic dome in both field and laboratory data

On the volcanic dome of La Soufrière, we found rock samples that display a wide range of alteration (Table 1, Figs. 3 and 4) that reflects on their physical and mechanical properties (Figs. 4 and 5). The relations we find in our field and laboratory datasets were possible due to a high number of collected rock samples across the volcanic dome, capturing the differences between and within the different sampling locations.

The field data—PLS and bulk density—show an overall trend of decreasing strength and density as a function of increasing AGI (Fig. 5a and b). Also, PLS increases as a function of increasing bulk density (Fig. 5c). The rock samples we measured from La Soufrière varied from pristine (AGI 1) to extremely altered (AGI 5), and their PLS and bulk density values range from 0.20 to 11.30 MPa and from 713.6 to 2674.2 kg/m³, respectively. Prior research measured the point load strength of a range of different rock types (Broch and Franklin, 1972; Brook, 1985; ISRM, 2007; Watters et al., 2000; Kahraman, 2001, 2014; Basu and Aydin, 2006; Gurocak et al., 2008; Karaman and Kesimal, 2015; Yin et al., 2017), while fewer studies focused on andesites and altered volcanic rocks (Agustawijaya, 2007; Kohno and Maeda, 2012; Wong et al., 2017). Agustawijaya (2007) tested soft rocks with PLS values less than 1 MPa, Wong et al. (2017) obtained high PLS values, from 4.35 to 9.76 MPa, for slightly to moderately altered rocks, and Kohno and Maeda (2012) measured PLS values from 0.07 to 5.25 MPa for moderately weak to weak rocks. Kohno and Maeda (2012) measured exclusively soft hydrothermally altered rocks that had an average PLS lower than 3.5 MPa, which agrees with our PLS data for more altered rock samples, AGI 4 and AGI 5 (PLS < 3.2 MPa). Our study exceeds the previously measured PLS range for volcanic rocks globally, capturing a wider range of alteration and PLS for volcanic rocks collected from just one volcano, La Soufrière (Fig. 5c). The bulk density values measured in La Soufrière correspond to the priorly measured values in Volcán de Colima in Mexico (Farquharson et al., 2015). Rock samples from our study exhibited a vast range of bulk densities, in agreement with recent laboratory measurements for rocks collected from La Soufrière (1800–2600 kg/m³, Heap et al., 2021b). Our bulk density range is larger than that previously measured, which we assign to having a much larger sampling suite. Prior muon tomography data recorded on the volcano, similarly, showed a less wide density range (1100–1900 kg/m³) than that of our study, and noted that high-density regions in the NE and S flanks are anomalous (Lesparre et al., 2012; Rosas-Carbajal et al., 2017). However, the muon

tomography method averages values to accommodate for larger rock masses (km scale) than sample-sized rocks (cm scale), accounting for cavities and voids spread throughout the structure (Lesparre et al., 2012; Rosas-Carbajal et al., 2017). Our bulk density data aligns and exceeds ranges from individual studies on La Soufrière providing the density of a very high number of rock samples.

The laboratory data—porosity and UCS—show decreasing strength and increasing porosity as a function of AGI (Figs. 6a and 6b). In Fig. 6d, we also note that UCS decreases as a non-linear function of porosity, as found by previous laboratory studies (Wyering et al., 2014; Zhu et al., 2016; Mordensky et al., 2018; Heap et al., 2020a; Heap and Violay, 2021). The AGI 3 data showed a wider range of UCS than the other alteration groups (Fig. 6c). The fractured surfaces of rocks (from AGI 3) showed alteration rims of various thickness (Supplementary Figs. 11. b32, 11.b66, 19.b17, and 36.b57). For the UCS experiments, the samples are cored from the central part of the rock block, while the PLS measurements were performed on larger samples that can contain alteration rims and macroscopic fractures. This difference could explain the discrepancy between the PLS and UCS strength ranges (Figs. 5a and 6c), underscoring the heterogeneity of individual rock samples.

Both the field (PLS) and laboratory data (UCS) show a decrease in strength as a function of increasing AGI, demonstrating that the alteration process weakens the rocks, as found in previous laboratory studies (Watters and Delahaut, 1995; Reid et al., 2001; Opfergelt et al., 2006; del Potro and Hürlimann, 2009b; Pola et al., 2012; Heap et al., 2021b; Heap and Violay, 2021; Mordensky et al., 2022; Kereszturi et al., 2023, Schaefer et al., 2023). The overall reduction in bulk density and increase in porosity of the sample suite can influence the strength of rock samples (Figs. 5b, 5c, 6a, and 6d) (Zhu et al., 2016; Mordensky et al., 2018; Heap et al., 2020a; Kendrick et al., 2021). However, we show that the group AGI 5 (purple colours in Figs. 5a, 5c, 6a, and 6d) captures a wide range of bulk densities and porosities, the PLS and UCS is lower than 2.1 and 20 MPa, respectively. The provided data highlight that even the dense and less porous rock samples can be weak, and therefore that alteration can reduce strength without necessarily reducing density or increasing porosity.

## 4.2. On-site conversion from point load strength to uniaxial compressive strength

To show that our field data are consistent with our laboratory data, we establish an empirical equation that can estimate UCS from field-measured PLS values. Given the heterogeneity of our data (see included and excluded data in Fig. 7b), we applied a linear regression to our entire dataset, obtaining a conversion factor of 19.3. This linear equation was then used to estimate UCS values. To remove outliers, we excluded data points where the estimated UCS values differed from the measured values by 50 % or more (Fig. 7; excluded data marked as diamond points). After filtering, we replotted UCS as a function of PLS and derived a conversion factor, $k \approx 20$ (Fig. 7):

$$ UCS = \sim 20PLS_{50} \qquad (3) $$

Where UCS represents the uniaxial compressive strength of 20 mm-diameter rock cores, and PLS50 represents the calibrated point load strength of rocks in the field. Published research shows a wide range of k for a variety of rock types, from 11.4 to 31 (Broch and Franklin, 1972; Brook, 1985; ISRM, 2007; Watters et al., 2000; Kahraman, 2001, 2014; Basu and Aydin, 2006; Agustawijaya, 2007; Kohno and Maeda, 2012; Karaman and Kesimal, 2015; Yin et al., 2017; Liu and Liu, 2023). In Fig. 7, we present UCS as a function of PLS for each AGI and all AGI combined. As a result, we obtain a value of k ranging from 16.8 to 21.2, with an R² value between 55 and 94 % for the different alteration grades (Fig. 7a), and a value of k ≈ 20, with an R² value of 83 % (Fig. 7b). All of our conversion factors are within the previously measured conversion factor range. We note that AGI 5 exhibited a slightly lower conversion factor than other alteration grades (Fig. 7a; k = 16.8), in agreement with
<figure><img src="image_16.png" /><figcaption>**Fig. 7.** a) Uniaxial compressive strength (UCS) as a function of point load strength (PLS<sub>50</sub>) with formulas for on-site conversion for each individual AGI. For pristine rock (AGI 1): UCS = 18.5PLS<sub>50</sub> and an R<sup>2</sup> value of 55 %, for slightly altered (AGI 2): UCS = 19.3PLS<sub>50</sub> and an R<sup>2</sup> value of 94 %, for moderately altered (AGI 3): UCS = 21.2PLS<sub>50</sub> and an R<sup>2</sup> value of 73 %, for severely altered (AGI 4): UCS = 20.6PLS<sub>50</sub> and an R<sup>2</sup> value of 86 %, for extremely altered (AGI 5): UCS = 16.8PLS<sub>50</sub> and an R<sup>2</sup> value of 79 %. b) Uniaxial compressive strength (UCS) as a function of point load strength (PLS<sub>50</sub>) with a formula for on-site conversion for all of the rock samples: UCS = ~20PLS<sub>50</sub> and an R<sup>2</sup> value of 83 %. The dashed line represents the linear best-fit line for the included data (circles). The excluded points (diamonds) represent samples that deviated from the measured UCS values by 50 % or more. The provided equation can be used to estimate UCS from field-measured PLS data.</figcaption></figure>
the $κ$ of 16.4 found for soft altered rocks by Kohno and Maeda (2012).

In our study, the UCS of some rocks was higher than the linear trend described by Eq. (3) (Fig. 7; i.e., above the dashed black line), which was likely due to the difference in the strength measuring technique. The point load test measures irregularly shaped bulk rocks that can often have disjointed surfaces, discontinuities, and large individual pores. Noticeably, some rock samples with cracked surfaces or alteration rims were remeasured in the PLT following an unsuccessful test (Supplementary Figs. 16.56, 19.b14, 19.b17, 19.b22, 35.49, and 45.b30). UCS tests, on the other hand, are standardised tests conducted in controlled laboratory conditions on small rock samples with fewer cracks and discontinuities, and, as a result, individual cores can have higher strengths than their parent rock. We also note that, although measures were taken to dry the samples for our PLT tests (see our Methods and Materials section), it is possible that some of the samples still contained some water; our laboratory samples, by contrast, were vacuum-dried in an oven at 40 °C. Water-weakening, observed in volcanic rocks (Heap and Violay, 2021; Heap et al., 2023a), could, therefore, also help explain the large difference between PLS and UCS values. There is a possibility that the outliers above the dashed line could form their own separate trend (Fig. 7). However, we note that this separate trend would require a conversion factor that differs significantly from previous research, as well as the existence of rocks with low PLS values and high UCS values. A smaller group of rock samples exhibited UCS values below the line of best fit (Fig. 7; i.e., below the dashed black line). Due to rock heterogeneity, 20 mm-diameter samples cored from the same parent block may not have the same strength, and their strengths may also differ from those of the parent block. We observed a wide range of rock strengths in a small sampling area due to heterogeneity within sampling locations (Fig. 4). We can additionally propose that the PLS values might be influenced by larger, unseen cracks within the sample, which could be avoided in more homogeneous sampling locations by conducting multiple test iterations on the same or similar samples from the same location (Broch and Franklin, 1972).

To obtain rocks for analysis, it is important to have an overview of the recent activity of the sampling area on the volcanic dome and to select locations accordingly based on importance, accessibility, and safety. To perform a similar study on a similar volcano to that reported herein, we suggest sampling areas that exhibit volcanic activity or that recently underwent mass wasting events. When rock samples are collected, the dimensions (diameter and widths) should be measured before the rock is broken in the point load tester loading cell (Fig. 2b). After the point load test, the post-failure diameter (D') should be measured, as cone penetration can be greater in weak, altered, and saturated rocks (Basu and Aydin, 2006). As explained in our Methods and Materials section, the $D_e$ and PLS should be calculated using Eqs. (2) and (1), respectively. The measured PLS can then be multiplied by our conversion factor (~20) to provide a UCS estimation (Eq. (3); Fig. 7b). Moreover, depending on the alteration grade of the obtained rock samples and/or the sampling suite, it is possible to choose a conversion factor that is specific to the alteration grade (Fig. 7a). For example, if the sampling suite in question is extremely altered (AGI 5), using a $κ$ of 16.8 could yield more precise UCS estimations (Fig. 7a).

Figure 8 shows UCS data from this study (circles with black rims) and the UCS values converted using Eq. (3) (blue circles) as a function of porosity. Our converted data provide UCS estimations from 0.3 to 257 MPa, which is a much broader range than for the laboratory-measured UCS values (0.98 to 181.7 MPa). Similarly, the range of porosity measured using the triple-weight porosity method (the small pieces of the parent block measured in the laboratory) is slightly greater than that for the pycnometer-measured porosity, spanning from 0.01 to 0.79 and from 0.02 to 0.71, respectively. We have also added the porosity-UCS data for volcanic rocks compiled by Heap and Violay (2021) (pink circles) to Fig. 8. Our converted UCS data (0.3 to 257 MPa) are within the strength range for the compiled dataset, which ranges from 0.88 to 331.78 MPa (Heap and Violay, 2021). However, our porosity range,

<figure><img src="image_17.png" /><figcaption>Fig. 8. UCS as a function of porosity. The circles with black rims are laboratory-tested samples, the blue circles represent the samples calibrated and corrected from our study, and the pink circles show previously compiled data for volcanic rock from Heap and Violay (2021).</figcaption></figure>

from 0.02 to 0.71, exceeds the porosity range for the compiled dataset, which ranges from 0.01 to 0.57 (Heap and Violay, 2021). The review by Heap and Violay (2021) compiled data from over 50 studies, providing data for different volcanoes and for a range of different volcanic rocks, from lavas to tuffs, whereas our study provided data from only one volcano—La Soufrière. Interestingly, despite this, our range of strength was similar to that provided by Heap and Violay (2021), signifying that the dome-forming rocks at La Soufrière are very variable in terms of their porosity and strength. We do note that, although La Soufrière exhibited great heterogeneity, finding rocks with low alteration and high strength was particularly challenging. It is common in studies of rock strength to use micromechanical models, such as the pore-emanating crack model of Sammis and Ashby (1986), to garner further insight into the mechanical behaviour of the rocks (e.g., Heap et al., 2014; Zhu et al., 2016; Heap and Violay, 2021). We chose here not to use these models because (1) volcanic rocks contain both microcracks and pores, whereas commonly-used micromechanical models contain either microcracks or pores (as discussed in Heap et al., 2014) and (2) plotting model predictions on a graph of UCS as a function of porosity requires unique values of pore radius and fracture toughness, parameters that likely vary considerably within our dataset.

We conclude that there is a prevalent heterogeneity captured on the volcanic dome, between and within the sampling locations, and also within the rock samples. Our results emphasise the variability inherent to volcanic terrains, underscoring the necessity of extensive sampling and careful interpretation of data to account for the diversity of rock properties found. We suggest conversion factors to obtain UCS estimations from field PLS data (Fig. 7), which provide a practical approach to on-site strength estimation and can, in some cases, contribute to the construction of more complex volcanic stability models, discussed below. We suggest that the point load tester is a useful tool to assess the strength of rock samples and, as a consequence, the heterogeneity of strength at volcanic sites. Our conversion factor, $κ = \sim 20$ (Fig. 7b), matched the range from other studies, although we note that our sampling suite was very heterogeneous. For similar volcanoes, if there are no facilities to conduct laboratory experiments to provide a conversion factor, we suggest that our conversion factor can be used with some confidence (Fig. 7b). Additionally, for volcanic sites with more homogeneously altered rock samples, we also provide conversion factors for individual alteration grades (Fig. 7a). However, it could be advisable to either perform experiments to determine a site-specific conversion factor or use another conversion factor for homogeneous rock samples from the literature. Nevertheless, the heterogeneity documented here highlights that understanding the strength range of the rocks comprising a volcanic structure is just one of the steps to producing large-scale stability models with heterogeneity in mind.
# 5. Hydrothermal alteration and strength distribution: Implications for volcano stability

## 5.1. Mineral assemblage and petrological features in argillic alteration

Hot hydrothermal systems facilitate fluid convection often transforming phenocrysts to clay minerals by leaching or replacement. Acid-sulphate leaching leads to the development of argillic alteration through advanced $H^+$ metasomatism with meteoric water input (Browne, 1978; Pirajno, 2009). Argillic alteration can be classified as intermediate and/or advanced depending on the pH level and temperature. Advanced argillic alteration is favoured at lower pH and higher temperatures. In fumarolic environments, oxidation of $H_2S$ leads to the formation of acid-sulphate fluids which, through leaching, produce low-temperature acid-stable minerals (Pirajno, 2009). The most common alteration groups derived from magmatic-hydrothermal systems are kaolinite, alunite, and smectite (Watters et al., 2000; Opfergelt et al., 2006; del Potro and Hürlimann, 2009b). The distinction between intermediate and advanced argillic alteration is indicated by the prevalent presence of alunite.

Globally, volcanic structures characterised by acid-sulphate alteration all exhibit similar alteration processes and assemblages. For example, on Lipari Island (Italy), an advanced argillic alteration was mapped out consisting of $SiO_2$-rich oxides, opals (mainly cristobalites), kaolinites, and alunites (Apollaro et al., 2023). Puracé volcano (Colombia) undergoes alteration due to the mixing of sulphid-chloridic magma gasses with water, resulting in low-pH fluids leaching the wall rock (Sturchio et al., 1993). In Poás volcano (Costa Rica), fossil fumaroles show acid-leaching alteration that has formed an advanced argillic mineral assemblage (Rodríguez and van Bergen, 2017). From X-ray diffraction data from Merapi (Indonesia), Darmawan et al. (2022) showed that more altered rock samples had higher percentage of Na-alunites and pervasive sulphur and iron oxide secondary pore infilling. One of the most phreatically active volcanoes, Kawah Ijen (Indonesia), had acidic, sulphate-rich solutions form under oxidising conditions (Delmelle and Bernard, 1994). In Teide volcano (Tenerife), advanced argillic alteration proximal to the fumaroles contained exclusively alunite, while the more distal samples showed partial alteration to alunite and kaolinite (del Potro and Hürlimann, 2009b). These studies show that advanced argillic alteration is commonplace for many volcanoes worldwide.

At La Soufrière, a similar alteration has been documented, driven by acidic magmatic fluids circulating through discontinuities and pores within the volcanic structure, which has pervasively altered the dome (Komorowski et al., 2005; Rosas-Carbajal et al., 2016; Peruzzetto et al., 2019; Moretti et al., 2020). Over the past 500 years, La Soufrière has underwent seven phreato-magmatic events, all associated with the percolation of acidic hydrothermal fluids (Komorowski et al., 2005; Boudon et al., 2007; Salaün et al., 2011; Lesparre et al., 2012; Hincks et al., 2014; Rosas-Carbajal et al., 2016; Moretti et al., 2020; Heap et al., 2021b). The hydrothermal fluids of La Soufrière, rich in chlorine, silica, sulphates, and sulphides, ascend from a deep magmatic reservoir and interact with the host rock at depths of less than 1 km (Brombach et al., 2000; Rosas-Carbajal et al., 2016). Mineralogical and petrological data suggest that distal DADs, away from the immediate area of the volcanic dome (e.g., Gallion waterfall), predominantly contain smectite, kaolinite, sulfates, and silica polymorphs (Salaün et al., 2011). In contrast, data from the volcanic dome indicate pervasive to selective hydrothermal alteration, characterised by secondary minerals such as kaolinite, alunite, and silica polymorphs (Heap et al., 2021b, 2022b). La Soufrière resumed volcanic activity in 1992 (Komorowski et al., 2005). This activity culminated in the 2018 volcanic unrest, which was considered to be a failed phreatic eruption, and the strongest seismic event since the 1976–77 crisis (Moretti et al., 2020). The volcanic dome is also frequently affected by landslides, including the 2004 collapse triggered by the November 21, 2004, Les Saintes Mw 6.3 earthquake (Feuillet et al., 2011) and the rain-induced 2009 occurred on November

19, 2009 (Peruzzetto et al., 2019; Moretti et al., 2020). The 2009 landslide contains blocks of highly altered material, and the scar shows an altered outcrop. Similarly, during our field campaign, we collected samples from freshly formed landslides that occurred in September 2022 during Tropical Storm Fiona, some of which exposed hydrothermally altered sliding surfaces (sampling location LS4).

The rock samples analysed in this study were collected from landslide scars (LS09, LS1, LS4, and HAM) and areas proximal to increased fumarolic activity (CSC, TAR, and TY). We categorised the samples based on a progression of alteration (AGI; Table 1). Macroscopic observations revealed plagioclase, K-feldspar, and pyroxenes, with a gradual increase in discolouration, from light grey to white, yellow, red, and purple, correlating to greater alteration and decreased porphyritic texture visibility. Microscopic analysis of micrographs from each AGI showed a common porphyritic texture with plagioclase as the dominant phenocryst (Fig. 3). The alteration process is marked by an increase in the thickness of alteration rims, replacement features, and sericitisation (Fig. 3). The unaltered groundmass exhibited pilotassitic texture, where we distinguished plagioclases, micas, and opaque minerals (i.e. magnetite, Fig. 3a). However, the altered groundmass exhibited argilisation, documented as a spotty opaque matrix without visible pilotassitic texture, typical of groundmass alteration (Fig. 3c, d, and e). We also observed pore infilling, which we suggest is from the secondary mineral assemblage (see also Heap et al., 2021b). The plagioclases showed replacement by clay minerals and, in some cases, hydrothermal biotite (Fig. 3c), which can fill microcracks within phenocrysts (Fig. 3c; Pirajno, 2009).

Petrological, mineralogical, and geochemical data on the La Soufrière volcanic dome unequivocally state the presence of the argillic alteration and its mineral assemblage (Komorowski et al., 2005; Salaün et al., 2011; Boudon et al., 2007; Rosas-Carbajal et al., 2016; Heap et al., 2021b, 2022b). The mineral assemblage at La Soufrière results from intense fluid-rock interactions within a heterogeneous and structurally complex hydrothermal system (Rosas-Carbajal et al., 2016). Advanced argillic alteration dominates the shallow dome, driven by acidic ($pH < 4$) hydrothermal fluids circulating at temperatures of 150–350 °C, as discussed in Heap et al. (2021b). Argillic alteration and its mineral assemblage is found in many volcanoes worldwide (del Potro and Hürlimann, 2009b; Rodríguez and van Bergen, 2017; Darmawan et al., 2022; Apollaro et al., 2023), and it significantly impacts the physical and mechanical properties of volcanic rocks (Watters and Delahaut, 1995; del Potro and Hürlimann, 2009b; Pola et al., 2012; Frolova et al., 2014; Wyering et al., 2014; Mordensky et al., 2018; Heap et al., 2020a; Heap et al., 2021b; Heap and Violay, 2021; Schaefer et al., 2023).

## 5.2. Use of alteration and strength distribution maps

Volcanic environments exhibit heterogeneously distributed properties (Kendrick et al., 2021; Heap et al., 2021b), which should be explored and quantified to improve large-scale volcano stability models. Large-scale volcanic models improve our understanding of the mechanisms triggering hazardous behaviour (Voight, 1990; Reid et al., 2000, 2001, 2010; Apuani et al., 2005a; Ball et al., 2015, 2018; Heap et al., 2021b; Harnett et al., 2022). Structural discontinuities often split volcanoes into different zones that might be characterised by different, and heterogeneous, physical and mechanical properties, factors that should be incorporated into future models.

Figure 4 shows a wide distribution of alteration grades (AGI) within and between the sampling locations. We observe structural commonalities at locations near the Ty fault (CSC, LS4, TAR, and TY), which has an orientation of N175E with an eastward dip (map in Fig. 9d). The Ty fault passes through fumaroles of Cratère Sud Centrale and Tarissan, which are both currently active. At the bottom of the 100-m-deep crater Tarissan is a hot, acidic lake that is connected to the Ty fault. Alongside the Ty fault, the hydrothermal alteration is more pronounced, suggesting that the observed alteration is likely structurally influenced, as found by
<figure><img src="image_18.png" /><figcaption>**Fig. 9.** a) Schematic of a volcano with one discrete zone and homogeneous properties. b) An example of using discrete heterogeneity on a schematic of a volcano with multiple discrete zones each assigned their own homogeneous properties. c) Schematic of a volcano with multiple discrete zones each assigned their own heterogeneous properties. An example of using material property heterogeneity, diffuse heterogeneity. The plots signify the difference between each discrete zone and heterogeneous distribution inside the discrete zone. d) An example of how strength and alteration distribution maps can be utilised with the aid of multidisciplinary research. On the right, we present a map with sampling locations and the Ty fault (black line) that spreads NS. The spreadsheet provides median values to be assigned in different discrete zones on the volcano alongside their strength distribution, responding to sampling locations shown in the left schematic. The alteration zones on the left schematic were modified from Rosas-Carbajal et al. (2016). The legend in colour belongs to the colour scheme of alteration grade indices (AGI) and the lower legend shows if we assigned the zone with discrete heterogeneity or diffuse heterogeneity (monochromatic-discrete, stripped shading-diffuse).</figcaption></figure>
previous studies (Reid et al., 2001; Cecchi et al., 2004; del Potro and Hürlimann, 2008, 2009b; Rosas-Carbajal et al., 2016; Moretti et al., 2020; Harnett and Heap, 2021; Jessop et al., 2021; Weydt et al., 2022). Habitually, hydrothermal alteration concentrates around craters, dykes, and fault discontinuities that are used as pathways for fluid flow within the volcanic structure (Crowley and Zimbelman, 1997; Rosas-Carbajal et al., 2016; Byrdina et al., 2017; Ghorbani et al., 2018). Hydrothermal alteration is known to weaken rocks (Watters and Delahaut, 1995; del Potro and Hürlimann, 2009b), which we witness, especially in sampling locations LS4 and TY (Fig. 4).

At the sampling locations on the top of the dome, CSC and TAR (Supplementary Figs. 6 and 31), we observed a more balanced distribution of AGI, with rock samples from all of the alteration grades present (Fig. 4). At sampling location CSC, where we were able to sample close to Cratère Sud Centrale, we found a large amount of discolouration and mineral precipitates (Supplementary Fig. 6, 8.17, 8.19, and 8.32). When fractured, some of the rocks collected at CSC showed alteration rims (Supplementary Figs. 11.b02, 11.b17, 11.b19, 11.b32, and 11.b66). Similar to Heap et al. (2021b), we found rock samples of very different strengths adjacent to each other, implying heterogeneity on the scale of the sampling location. At the sampling location TAR, half of the rocks collected were unaltered and strong (AGI 1 and AGI 2), which we partly attribute to collecting at a clearance NW to the crater Tarissan. The rocks collected from the TAR sampling location were possibly unaltered materials expunged in one of the prior phreatomagmatic events (Komorowski et al., 2005). Nonetheless, we suspect that the rock samples closer to and within the crater Tarissan are more altered and weaker than those collected as part of our sampling suite.

The rocks from the two sampling locations on the north and south flanks of La Soufrière, LS4 and TY, were usually quite altered and weak. Our field findings showed sampling location LS4 to be a relatively new landslide (from September 2022) uncovering an altered outcrop (Fig. 1c; Supplementary Fig. 25). In some areas of the TY location, we noticed slight degassing and that some of the rock outcrops were hot to the touch (Supplementary Fig. 37). Both sampling locations, LS4 and TY, also contained a few less altered and stronger samples, such as rock samples from Supplementary Figs. 27.20, 27.21, 28.36, 41.52, 44.107, and 45.117. These less-altered rocks (AGI 1 and 2) highlight the lack of uniformity in our sampling locations, exposing heterogeneity on the sampling location scale.

Sampling location HAM (Supplementary Fig. 12), on the NW flank of l'Échelle, exhibited similar features to landslides LS4 and TY, a recent landslide with visible discolouration and warmth of the outcrop (Supplementary Figs. 12c and 12d). Rock samples from the sampling location HAM were the most altered and the weakest of the whole sampled area (Fig. 4). Although this sampling location is adjacent to the La Soufrière dome, it shows similar characteristics (Fig. 4). The similarity between the alteration observed at HAM and some of the other is likely due to the proximity to the Breislack fault, which is a part of the faulting system of La Soufrière (Komorowski et al., 2005; Feuillet et al., 2011; Rosas-Carbajal et al., 2016). The fluids could have migrated along this fault, resulting in fluid-rock interaction at the observed alteration and rock weakening at the sampling site.

Landslide LS1, similar to sampling location TAR, contained more unaltered rocks than the other sampling locations. Indeed, more than half of the rock samples at LS1 were classified as either pristine or slightly altered (Fig. 4, Supplementary Figs. 21.4, 21.5, 22.29, 24.b05, and 24.b29). However, the rock samples in sampling location LS1 were still weak (Fig. 4). We observed that the primary rock weakening is due to hydrothermal alteration; however, high porosity can also influence rock strength (Zhu et al., 2016; Mordensky et al., 2018; Heap et al., 2020a; Kendrick et al., 2021). Some of the surfaces of rock samples from sampling location LS1 contained large pores (Supplementary Figs. 21.5, 21.12, 22.23, and 22.29) and have a mean porosity of 0.20 (Table 4). Nonetheless, there is still a non-negligible percentage of rock samples that are moderately strong (Fig. 4). The stronger rock samples from landslide LS1 originate from the top of the dome, potentially being unaltered rocks similar to the ones observed in the sampling locations CSC and TAR. Rosas-Carbajal et al. (2016, 2017) referred to the area of the south flank of La Soufrière as the unaltered bulge, which was somewhat visible in our own sampling (LS1 and TAR, Fig. 4). Indeed, subsurface ERT data showed a low conductivity layer covering the top of the volcanic dome, interpreted as pristine rock (grey layer in Fig. 9d; modified from Rosas-Carbajal et al., 2016) and a crescent-shaped high conductivity layer extending from the top of the dome (CSC and TAR) towards the south flank, interpreted as highly altered rock (yellow, orange, and purple colours in Fig. 9d).

While the data discussed above highlight large-scale heterogeneities, our laboratory data expose small-scale heterogeneity. From 115 cores, we observed a vast range of porosity and UCS (Fig. 6). The porosity and UCS values in this study (from 0.01 to 0.71 and from 1 to 181.7 MPa, respectively) vary not only within sampling locations, but, also, within the rock samples used for coring. Rock samples found adjacent in the field can show vastly different physical and mechanical characteristics (Heap and Violay, 2021; Heap et al., 2021b; Kereszturi et al., 2023). Interestingly, the less-altered samples exhibited less variability in porosity than the samples that were more altered. For instance, three rock cores from the sample TAR49 (AGI 1) show porosity values from 0.12 to 0.13 and UCS values from 55 to 70 MPa, while three rock cores from the sample LS4–70 (AGI 4) show porosity values from 0.17 to 0.32 and UCS values from 18.4 to 61.5 MPa. On the small-scale, we observe a high degree of alteration that consequently leads to a higher degree of heterogeneity, which is additionally confirmed by our core samples and micrographs (i.e., Fig. 3e). While at the outcrop-scale, the alteration varies from pristine rock to completely altered, which could be affixed to the presence of smaller faults and fractures that channel fluids. Therefore, although our sampling was unsystematic, this suggests that the alteration change could be dependent on the proximity to the craters or discontinuities (i.e., sampling locations CSC and TAR). Finally, on the scale of the volcanic terrain, the alteration is nucleated around the faults and fumarole systems (Rosas-Carbajal et al., 2016, 2017). We conclude that alteration is heterogeneously distributed, on a number of length scales, and structurally controlled.

It is uncommon for volcano stability modellers to split a volcanic structure into numerous discrete zones, adding discrete heterogeneity, differentiated by different physical and mechanical input parameters

<caption>**Table 4**</caption>

Summary of suggested mean and median values for each sampling location at La Soufrière de Guadeloupe.

<table><thead><tr><th rowspan="2">Sample name</th><th colspan="2">Bulk density, kg/m³</th><th colspan="2">Porosity</th><th colspan="2">PLS<sub>50</sub>, MPa</th><th colspan="2">UCS, MPa</th><th rowspan="2">Homogeneity index, m</th></tr><tr><th>Mean</th><th>Median</th><th>Mean</th><th>Median</th><th>Mean</th><th>Median</th><th>Mean</th><th>Median</th></tr></thead><tbody><tr><td>LS09</td><td>1921.6</td><td>1950.6</td><td>0.28</td><td>0.28</td><td>1.08</td><td>0.48</td><td>20.8</td><td>9.26</td><td>7</td></tr><tr><td>LS1</td><td>2265.9</td><td>2299.9</td><td>0.21</td><td>0.20</td><td>1.86</td><td>1.78</td><td>35.9</td><td>34.3</td><td>3</td></tr><tr><td>LS4</td><td>1706.8</td><td>1668.6</td><td>0.35</td><td>0.34</td><td>1.02</td><td>0.42</td><td>19.7</td><td>8.11</td><td>7</td></tr><tr><td>HAM</td><td>1438.5</td><td>1273.8</td><td>0.49</td><td>0.56</td><td>0.57</td><td>0.20</td><td>11.0</td><td>3.86</td><td>8</td></tr><tr><td>CSC</td><td>2071.3</td><td>2074.1</td><td>0.22</td><td>0.19</td><td>2.61</td><td>2.04</td><td>50.4</td><td>39.4</td><td>2</td></tr><tr><td>TAR</td><td>2090.4</td><td>2158.1</td><td>0.23</td><td>0.20</td><td>2.53</td><td>2.17</td><td>48.9</td><td>41.9</td><td>2</td></tr><tr><td>TY</td><td>1852.1</td><td>1835.4</td><td>0.33</td><td>0.34</td><td>1.26</td><td>0.59</td><td>24.3</td><td>11.4</td><td>6</td></tr></tbody></table>
(Fig. 9b; Wallace et al., 2022; Kereszturi et al., 2021). The locations of altered discrete zones, zones characterised by different rock properties, are complicated to determine with great certainty, however, there are various field approaches that can be used. In our case, we sampled seven locations, not only providing alteration, but also strength values (Fig. 4), discussed below. Previously, volcano stability models typically relied on laboratory data from a few rocks collected from different areas of the volcano to represent one discrete zone (Fig. 9a), without first knowing whether the rock collected was representative of the sampling location. If our seven sampling locations were to be considered as discrete zones, our data provide a characteristic median strength value for each zone (Fig. 4). In sampling locations LS09, LS4, HAM, and TY, more than half of the rock samples were very altered (AGI 4 and AGI 5). Furthermore, the most common strength distribution on the volcano is found in sampling locations LS09, LS4, HAM, and TY, where there is a positive skew and low median strength values (Fig. 4). We suggest that future sampling locations that exhibit clear alteration features, such as discolouration on the outcrops, are assigned very low strength values as input parameters for large-scale models. For example, Müller et al. (2021) used optical and thermal sensors on drones to survey hydrothermal alteration at Vulcano Island (Italy). These authors segmented the alteration into 13 discrete zones, all proximal to fumarolic fields, and highlighted that discrete alteration zones should be observed as individual units with their own unique levels of hydrothermal activity and alteration degree (Müller et al., 2021). If remote sensing and our approach were to be combined in future studies, we would know the locations of discrete zones and strength values that could be assigned to them. For sampling locations LS1, CSC, and TAR, we observe that the alteration and strength values are well-distributed. Sampling locations that exhibit well-distributed alteration and strength could be divided into multiple discrete zones, comprised of either stronger, less altered rock samples and weaker, more altered rock samples. However, in cases of sampling location LS1, CSC, and TAR, we could benefit from the use of diffuse heterogeneity that lets us incorporate the distribution of strength into our respective discrete zones.

Material property heterogeneity, diffuse heterogeneity, is scarcely discussed and studied, but, nonetheless, should be considered when using volcanic stability models (Heap et al., 2023b). While the above-discussed discrete heterogeneity (Fig. 9b) contains discrete zones with different physical and mechanical properties, using diffuse heterogeneity means that the physical and mechanical properties inside the discrete zones are also heterogeneous (Fig. 9c). Heap et al., 2023b suggested that incorporating diffuse heterogeneity in large-scale volcano stability models distributes the strain and displacement throughout the structure with the possibility of mobilising larger rock masses. Even so, diffuse heterogeneity depends on thorough understanding of strength distribution at respective sampling locations in order to assign a discrete zone with appropriate diffuse heterogeneity parameters. In our results, we show that strength distribution varies between the different sampling locations (Fig. 4). For instance, sampling locations LS1, CSC, and TAR contained rock samples with a wide distribution of strength (Fig. 4). It is possible to implement a polynomial equation that describes the strength distribution of these locations, which can then be inputted into volcano stability models. In the absence of strength distribution data, Heap et al., 2023b incorporated diffuse heterogeneity into large-scale numerical models using a Weibull probability density function. The Weibull probability density function is characterised by shape parameter (m; homogeneity index) and a scale parameter (u). The scale parameter in the function corresponds to, in our case, UCS or PLS values, while the shape parameter (m), also referred to as homogeneity index, determines how close the mechanical properties are to the median value of the scaling parameters (Xu et al., 2018). The homogeneity index, as described by Heap et al., 2023b, serves as a useful variable to characterise the kurtosis of the distribution of the scale parameter. Usually, and due to the absence of data, the homogeneity index is arbitrarily prescribed to generate a distributed scale parameter. However, our strength

distribution maps can be used to provide the kurtosis of strength distribution curves (Fig. 4, Supplementary Table 1), which can then be used to select appropriate homogeneity indices for the Weibull probability density function. In the next section, we discuss and quantify the use of this approach in a specific area—La Soufrière.

Our data suggests that, due to their differences in mechanical properties, altered discrete zones in volcanoes should be separated from less-altered discrete zones (Fig. 9b). Furthermore, if large sample suites are possible, it is desirable to understand the strength distribution within these discrete zones to incorporate diffuse heterogeneity in large-scale volcano stability models (Fig. 9c). However, although we measured samples sourced from the scars of landslides, characterising strength distributions using a PLT distinguishes the strength of rocks collected from the surface. Ideally, an assessment of the strength distribution of discrete zones within a volcanic terrain would also involve measurements performed on samples retrieved from boreholes. Likewise, some studies have detected alteration on the surface through spectral imaging (Crowley and Zimbelman, 1997; Hubbard et al., 2003; Rejas et al., 2012; Schaefer et al., 2021, 2023; Müller et al., 2021; Kereszturi et al., 2018, 2020, 2023; Müller et al., 2024). In spectral imaging, various methods rely on the use of unoccupied aircraft systems (UAS) (Crowley and Zimbelman, 1997; Rejas et al., 2012; Kereszturi et al., 2018, 2020; Müller et al., 2021; Müller et al., 2024), which can be hampered by prohibited visibility, i.e., clouds, rain, fumarolic activity, and/or plant shroud. Similar to our approach, airborne imaging systems provide information on alteration zones exclusively on the surface. Therefore, in order to better locate the distribution of discrete zones, we need to inspect the subsurface of altered volcanoes using geophysical methods, such as, aeromagnetic data (Kereszturi et al., 2020), electrical resistivity tomography data (ERT; Rosas-Carbajal et al., 2016; Byrdina et al., 2017; Ghorbani et al., 2018), and muon tomography surveys (Lesparre et al., 2012; Rosas-Carbajal et al., 2017, Tioukov et al., 2019, 2022; Bajou et al., 2023), among others. For example, Kereszturi et al. (2020) combined hyperspectral data with aeromagnetic data, to quantify demagnetized subsurface rock volumes. This approach detects hydrothermally altered discrete zones below the surface due to their lack of magnetite-rich crystals (Finn et al., 2018). ERT data detects presence and migration of fluids in fluid-saturated media documenting high electrical conductivity in fault discontinuities or around conduits, where fluid percolation is prominent (Rosas-Carbajal et al., 2016). Moreover, high electrical conductivity is observed in rocks with high clay mineral content (Rosas-Carbajal et al., 2016). Byrdina et al. (2017) found that Merapi (Indonesia) has hydrothermally altered zones on the south flank due to low resistivity values and the shifting of the conduit towards the south. These examples offer direct insight into the heterogeneity distribution and extent on the surface and subsurface of the studied volcanic terrain. We underscore the importance of an interdisciplinary and multi-approach perspective to studying the impact of alteration on volcanic hazards, from mapping alteration zones (Müller et al., 2021; Kereszturi et al., 2023), to understanding the influence of alteration on rock properties (Heap et al., 2021b) to, finally, performing volcano stability models with discrete and diffuse heterogeneity in mind (Heap et al., 2023b).

### 5.3. *Suggestions for numerical modelling specific to La Soufrière de Guadeloupe*

In the following, we will use La Soufrière as a case study to show how the data in our study can be used in large-scale volcano stability models (Fig. 9d). And, to do so, we use the Weibull probability density function as the means with which to assign heterogeneity quantification to discrete zones, as described in Heap et al., 2023b. In our example, we use approximations to determine the kurtosis of the strength curves in Fig. 4. We also assign our sampling locations a homogeneity index (m) varying from 1, low kurtosis and high heterogeneity, to 10, high kurtosis and low heterogeneity (see Heap et al., 2023b). In the case of sampling locations
LS09, LS4, HAM, and TY, we see that the kurtosis is high (Fig. 4), so we would assign high homogeneity indices to these zones (for example, 8, 7, 7, and 6, respectively). Whereas sampling locations LS1, CSC, and TAR have a slight-positively skewed distribution, respectively, and therefore would be assigned low homogeneity indices, 3, 2 and 2, respectively. The strength distributions provided in Fig. 4 can also be used to determine the representative median PLS and UCS value at a given sampling site (Table 4). For example, sampling locations LS4 and LS09 have a calculated median PLS₅₀ values of 0.42 and 0.48 MPa, which provides a UCS estimate of 8.11 and 9.26 MPa, respectively. For sampling location LS1, we calculated a PLS₅₀ value of 1.78 MPa and the UCS estimate of 34.3 MPa. In the sampling location CSC, the PLS₅₀ value is 2.04 MPa, and the UCS estimate is 39.4 MPa. Previously, modelling studies have often relied on the strength of one sample to represent the mechanical properties of an entire discrete zone. TAR is a sampling location that shows a bimodal distribution of strength. In this instance, we propose to differentiate between the weaker and stronger rock samples. One can use two values to describe the respective location. The sampling location TAR can be split into two different discrete zones (Fig. 9d), one closer to the crater assigned TY strength values (PLS and UCS values 0.59 and 11.4 MPa, respectively), and another more distal from the crater Tarrisan that is assigned a median PLS₅₀ value of TAR of 2.17 MPa (equal to a UCS value of 41.9 MPa). For crater Tarrisan, we propose to use TY and/or LS4 values to represent the strength in the yellow, orange, and purple zones in Fig. 9d, which are 11.4 and 8.11 MPa. The ERT data from Rosas-Carbajal et al. (2016) show a lower density, suggesting an unaltered layer of pristine rock covering parts of the top of the volcano (grey layer in Fig. 9d; modified from Rosas-Carbajal et al. (2016)) and a crescent-shaped alteration zone extending from the top of the dome (CSC and TAR) towards the south and north flank (TY and LS4; yellow, orange, and purple zones in Fig. 9). Interestingly, in Heap et al. (2021b), geophysical data and physical and mechanical parameters from La Soufrière were used to produce a simplified model of the dome with discrete heterogeneity showing a large zone of weakened rocks towards the south flank of the volcano (similar to Fig. 9b). Their results yielded high strain rates and large collapse volumes. However, not accounting for discontinuities underestimated the strain rates measured in the field (Heap et al., 2021b), which using our method can be improved. All the aforementioned suggestions can help us understand the diffuse strength distribution in altered discrete zones beneath the surface, which can be incorporated into volcanic stability modelling. Rosas-Carbajal et al. (2016), using ERT, found a large concave-shaped alteration zone spreading from the top of the dome (CSC and TAR) towards the southern flank, which is also supported by evidence of a volcano sliding in the same direction (Fig. 9d).

The study presented here could be a common practice for assessment of volcano stability worldwide. To understand the heterogeneity of a volcano, it is vital to use field and laboratory techniques together. From the field campaigns and rock sampling, using visual indicators to grade alteration (Watters and Delahaut, 1995; del Potro and Hürlimann, 2008; Farquharson et al., 2015). The strength of volcanic outcrops and rocks can be assessed using a Schmidt hammer for outcrops (Harnett et al., 2019; Wang and Wan, 2019) and a point load tester for irregular specimens. However, we suggest that point load testing should be the preferred field method for the estimation of uniaxial compressive strength due to its versatility (del Potro and Hürlimann, 2009a). Forthcoming studies should be focused towards linking point load strength to other mechanical rock properties, e.g., cohesion and Young's modulus. The alteration grade indices we used were successful in determining which rocks were the weakest, with less than 2.1 MPa strength, which makes us speculate about the potential of visually determining which parts of the larger rock masses would be the most unstable, and which might pose the highest threats. We encourage further research into the domain of linking point load strength to other methods that directly quantify hydrothermal alteration, such as, for example, hyperspectral imaging data (Kereszturi et al., 2018, 2023;

Schaefer et al., 2021). Especially in hard-to-reach areas, remote sensing is an ideal tool to determine the distribution of alteration (Walter, 2011; Müller et al., 2021; Kereszturi et al., 2020; Müller et al., 2024). Future research should focus on enhancing all of these connections in order to quantify alteration and determine its distribution to create better and more reliable large-scale volcano stability models. If possible, volcano stability models should contain as many discrete zones as needed and, importantly, incorporate diffuse material property heterogeneity to these discrete zones to achieve the most accurate modelling results.

## 6. Conclusions

Volcanoes are inherently unstable heterogeneous structures composed of volcanic rocks that are altered through hydrothermal fluid percolation and, as a result, may be weakened. Assessing volcanic instability, that may arise due to the formation of weakened altered zones, requires large-scale volcano stability models that incorporate physical and mechanical rock properties as input parameters. Existing studies often use laboratory data to assign individual strength values to the discrete zones forming a volcano; however, due to the heterogeneity of volcanoes and volcanic rocks, it is important to know the range of strength values that characterise a particular discrete zone. Therefore, we propose that volcanic modelling studies should assign a strength distribution (diffuse heterogeneity) to the discrete zones that form their volcano. Our study highlights the importance of incorporating both discrete and diffuse heterogeneity in future volcano stability models.

To capture the heterogeneity, we used field methods (alteration grade index, point load tester, and Archimedean bulk density method) and laboratory methods (porosity and uniaxial compressive strength). 544 volcanic rocks from seven sampling locations displayed alteration grades from 1 (pristine rocks) to 5 (extremely altered rocks). Both bulk density and point load strength decrease as a function of increasing alteration. Point load strength (from 0.012 to 11.296 MPa) increased as a function of bulk density (from 713.6 to 2238.5 kg/m³). The samples considered to be the most altered (alteration grade 5) were exclusively weak, with point load strengths less than 2.1 MPa. However, despite this, these altered samples were characterised by a wide range in density and porosity. We concluded, therefore, that the alteration is the primary driver of rock strength. In the laboratory, the porosity (from 0.01 to 0.79) and uniaxial compressive strength (from 1 to 181.7 MPa) increase and decrease, respectively, as a function of increasing alteration, correlating well with the field data. Thus, in this study, we provided the empirical formula, UCS = ~20PLS₅₀, for a direct on-site conversion from field (point load strength) to laboratory (uniaxial compressive strength) values. For the most altered rock samples, the conversion factor was slightly lower (16.8), which could be used in more altered and homogeneous sample suites to obtain uniaxial compressive strength estimations.

Our alteration and strength distribution maps show heterogeneity both between and within different sampling locations, highlighting the importance of discrete and diffuse heterogeneity as modelling constituents. For discrete heterogeneity, we propose splitting discrete zones based on visible differences in alterations and strength. The values of strength assigned to these discrete zones should ideally be the median strength value from a large dataset. Alteration was prevalent in most zones, and strength values were low, so we encourage modellers to assign low strength values to altered discrete zones in their volcano stability models. Contrarily, we advise modellers to assign higher strength values to less-altered discrete zones. Zones that comprise unaltered and altered rocks should, when and where possible, be separated into two discrete zones. To incorporate diffuse heterogeneity, which implores the use of heterogeneous strength values inside each discrete zone, we provide strength distribution for each sampling location. If it is possible to add material property heterogeneity within discrete zones, our strength distributions can be used to characterise both altered and less-altered zones. Our strength distributions show a positive skew with
a high kurtosis, i.e., low strength values in altered zones. For the less altered zones, we also provide bimodal strength distribution curves and strength distribution curves with a slightly positive skew and a lower kurtosis, which can all be used to incorporate diffuse heterogeneity into volcano stability models. To demonstrate the implementation of our approach, we use La Soufrière as a case study. In future modelling ventures at La Soufrière, we advise to combine our study with prior geophysical methods, as shown herein, in order to include discrete and diffuse heterogeneity.

In conclusion, to create more accurate heterogeneous models, the key is to cooperate interdisciplinarily. If it is possible, we suggest an integrated field and laboratory study using visual tools, such as alteration grading to describe the degree of alteration in rock samples, while point load testing is recommended for uniaxial compressive strength estimation. We also advise future studies to focus on linking point load strength to other mechanical rock properties commonly used in volcano modelling, such as cohesion and Young's modulus. Our study also highlights the potential of linking point load strength to methods that can quantify alteration, such as hyperspectral imaging data. Importantly, airborne hyperspectral imaging can quickly map large areas of a volcanic terrain, including hard-to-reach or dangerous areas. Our study underscores the importance of volcanic heterogeneity, discrete and diffuse, urging modellers to consider it as a vital element for building more complex and accurate large-scale volcano stability models.

## CRediT authorship contribution statement

**Agata Poganj:** Writing – review & editing, Writing – original draft, Visualization, Investigation, Conceptualization. **Michael J. Heap:** Writing – review & editing, Supervision, Investigation, Conceptualization. **Patrick Baud:** Writing – review & editing, Supervision, Investigation, Conceptualization.

## 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 thank Federico Di Traglia and one anonymous reviewer for the remarks that improved our manuscript. Lucille Carbillet, Martha Missy Eppes and Jamie Farquharson are thanked for the early discussions. This work was supported by ANR grant MYGALE ("Modelling the physical and chemical Gradients of hydrothermal Alteration for warning systems of flank collapse at Explosive volcanoes"; ANR-21-CE49-0010). M. J. Heap was additionally supported by a European Research Council Synergy Grant (ERC-ROTTNROCK-101118491). M. J. Heap acknowledges support from the Institut Universitaire de France (IUF). We thank Bertrand Renaudie for preparing the experimental samples. Finally, we thank all of those at Les Bananes Vertes in Guadeloupe for their hospitality (https://www.lesbananesvertes.fr).

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jvolgeores.2025.108344.

## Data availability

Data will be made available on request.

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