Statistical sedimentary aspects of collapsed volcanic edifices along subduction zones.

doi:10.21203/rs.3.rs-4908267/v1

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Statistical sedimentary aspects of collapsed volcanic edifices along subduction zones.

https://doi.org/10.21203/rs.3.rs-4908267/v1

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Using Google Earth imagery and published data on volcanoes worldwide, several examples of volcanic debris avalanche deposits have been compared to characterize the dynamics of mass flow and matrix transformation during climate change. Sedimentological studies of these deposits help to correlate the spatial and temporal distribution of collapsed edifices with the stratigraphy and textural variations of the matrix.

Parameters such as percentage of matrix, mean grain size, ratio of matrix sand to gravel and other statistical sedimentary parameters have been calculated to characterize the matrix breccias in relation to the spatio-temporal variability of the mass flow deposits. Factors such as quaternary erosion, nival surface conditions and high intensity rainfall contribute to the transformation of the reworked avalanche deposits into debris flows. The sedimentary characteristics of volcanic debris avalanche deposits are influenced by the profile shape, volcanic area and slope gradient of the collapsed edifices, which are related to the critical stability and quaternary cyclic evolution of the stratovolcanoes.

Empirical equations have been developed to propose a co-genetic evolution of avalanche deposits in terms of their spatio-temporal distribution, morphological variations of the collapsed edifices, and climatic changes. Similar correlations between quaternary occurrence, matrix percentage, matrix/gravel ratio, and median grain size show a positive association with the obliquity of the Earth's axis and the precession of the equinoxes. These results highlight the importance of semi-quantitative sedimentological analyses of deposits from collapsed volcanic edifices in refining our understanding of subsequent hazardous flows associated with climate change.

volcanic debris avalanche

sedimentology

climate changes

debris flow

hazards

The deposits of volcanic debris avalanches (VDADs) from collapsed edifices are chaotic matrix-supported breccias associated with debris flow deposits (Glicken 1986). From a total of 210 volcanoes worldwide (Siebert et al. 1987), a total of 288 VDADs have been identified. These are typically associated with subduction zone arc volcanism. For example, 75% of Andean volcanoes (Francis 1984; Dufresne et al. 2021) exhibit VDADs, while a total of 100 destabilizations have been identified along the Japan arc. Additionally, recurrent destabilizations have also been observed in Kamchatka (Shiveluch volcano, Belousov et al. 1999), and Alaska (Waythomas et al. 2000). Avalanche dam break-out floods and fluvial reworking into coarse debris flows during high intensity rainstorms have significant impacts in the vast areas downstream (Capra and Macías 2002; Keigler et al. 2011). The VDADs and their transformed deposits are potentially important archives. However, new ways of parametrizing VDADs are necessary in order to further our understanding of their processes, especially those related to climate controls and matrix transformation into debris flow. Further sedimentary analysis is needed in order to constrain the subsequent hazardous flows associated with quaternary climate changes (Makris et al. 2020; Dufresne et al. 2021).

Textural and semi-quantitative sedimentological analyses contribute to describing the avalanche lithofacies at different scales. The debris avalanche breccias is mainly overlained by a thick carapace of lava blocks. Jigsaw cracks and inherited clasts are observed in a poorly sorted matrix, which shows a coarse stratification with collisional textures. Polymodal and bimodal grain-size distributions interact during collisional and frictional transport (Pierson et al. 1987; Hungr et al. 1996; Iverson 1997). Block ratios decrease distally t with an increasing matrix. This is related to dynamic fragmentation and granular segregation with dilation (Capra and Macías 2002; Clavero et al. 2002; Davies and Mc Savenney 2009; Roverato et al. 2014). Basal fragmentation and comminution contribute to a low basal shear resistance (Caballero and Capra 2011; Bernard and van Wyk de Vries 2017). Clay-rich and hydrothermally altered material can affect the spreading of an avalanche (Glicken 1986): cohesive avalanche deposits (> 3% of silt and clay) are differentiated from non-cohesive massive deposits (< 3% silt and clay). The avalanche flows may be transformed into debris flows by dewatering or dilution downstream (Scott et al. 1995).

The aim of this investigation is to examine the correlations between the spatio-temporal distribution of the quaternary VDADs, the associated volcanic complex, and climate change. For example the Andes have a dry climate, and numerous avalanches have been linked to distinct deposits. A quantitative sedimentary analysis of published data on quaternary volcanoes worldwide has been conducted to determine if climate-related contraints have been recorded in the quaternary avalanche mass flow.

Geological setting

Successive large collapses have occurred along the volcanic arcs of the Pacific Rim subduction zone and the associated microplate boundaries. Collapses along the Juan de Fuca subduction zone were associated with a water-saturated and hydrothermally transformed matrix. For example, the debris flow deposits resulting from the 1980 Mount Saint Helens destabilization along the Cascade magmatic arc and Mount Shasta in the United States (Glicken 1986; Ui and Glicken 1986) have been distinguished. The 1980 Mount St. Helens rockslide was triggered by magmatic intrusion (Voight et al. 1983; Reid et al. 2010; Walter 2011; Siebert and Reid 2023). The snow-rich avalanche deposits on the glaciers of the Illiamna volcano in south-central Alaska were differentiared (1964, Waythomas et al. 2000).

Along the trans-Mexican volcanic complex (TMVB), a total of 12 post-last Glacial Maximum volcanic collapses have been cataloged, indicating a mean recurrence interval of c. 2698 years, despite the majority of VDADs occurring after the Last Glacial Maximum (22 000–18 000 cal BP). Hybrid avalanche deposits with blocks in a cohesive matrix are formed from reworked and interstratified primary edifice deposits. They are a transition between avalanche breccias and debris-flow deposits (Capra and Macías 2002). Field observations and textural analysis consider the basal liquefaction of the Nevado de Toluca edifice with an upper collisional emplacement, and the eastern flank of the Nevado de Colima with a secondary dam breakout and debris-flow downstream. There have been numerous partial collapses of the Colima volcano followed by magmatic eruptions and debris flowing downstream (Capra and Macias 2002; Capra 2007; Caballero and Capra 2011; Roverato et al. 2011, 2014).

The Central Andean Volcanic Zone (CAVZ) shows recurrent destabilizations of 75% of stratovolcano complexes (Francis 1994), with 29% failing between − 1 Ma-112 ka, and 20% between ~-8-7.5 ka. Only two VDADs, Peteroa and Antuco volcanoes in Chile, were identified during the Greenlandien period (-11.7-8.326 ka, Siebert et al. 1987; Dufresne et al. 2021). Likewise, the Antuco basaltic stratovolcano in Chile (Romero et al. 2022) has also been differentiated from the Toreva blocks within the Chimpa VDADs (Argentina, Bustos et al. 2022), and the Azufral and Chimborazo volcanoes (Moreno et al. 2021; Bustos et al. 2022). The asymmetric Chimborazo volcano in Ecuador exhibits lava blocks and mixed avalanche deposits aligned along fault zones. Additionally, the Cubilche VDAD from the northern Andes is distinguished (Roverato et al. 2018). The horseshoe-shaped amphitheater of Tutupaca volcano and its brecciated lava domes (Fig. 1b) are associated with ridged units exhibiting granular segregation of the associated pyroclastic deposits (Valderrama et al. 2016; Bernard et al. 2022).

Hummocky structures observed behind the front lobe are associated with avalanche transformations, such as the Meager rock-avalanche deposits in British Columbia (Fig. 1c; Bernard 2020). For example in the Arequipa basin in Peru, flash floods and debris flow from El Misti VDADs (-833-112 ka) are observed (Fig. 1d; Bernard et al. 2017). The study additionally examined 18 VDADs from the Shiveluch volcano in Kamchatka (Belousov et al. 1999), and 8 mass flow deposits from the Taranaki volcano in New Zealand (Roverato et al. 2014), with a mean recurrence rate of 10,000 years. The variability and, in many cases, unknown causes of edifice failure in the past are serious challenges to any attempt to correlate the increased frequency of debris avalanche occurrences with climatic changes at a range of scales.

Using selected examples, the lithostratigraphic sections of different avalanche units are compared (Figs. 2 and 3), such as the Socompa VDADs in Chile (-7 ka, from van Wyk d Vries et al. 2001, Grosse et al. 2022), the Tutupaca ridges in Peru (Bernard et al. 2022), the Nevado de Colima VDADs in TMVB (-18.5-4.3 ka from Capra and Macias 2002), and the Illiamna VDADs in Alaska (1964, from Waythomas et al. 2000). From the collapsed scar to the distal zone, the internal structures and textural variations are considered in the matrix transformation into debris flow.

From the collapsed scar of the Socompa volcano in Chile (Fig. 2a, Grosse et al. 2022), lava dome breccias have been observed along the sheared basal contact, stretched pumices in the proximal Toreva blocks, and ductile deformations of ignimbrite along the hummocky structures in the median zone. Interstratified pyroclastic units are thrust along the proximal Toreva blocks. The faulted ignimbrite has been extruded in the distal zone. Avalanche units from the lava dome collapse (Fig. 2b) show lava breccias overlaid by pyroclastic deposits (Tutupaca in Peru and Socompa in Chile). The channelized avalanche deposits are covered by reworked matrix breccias transformed into debris flow deposits at Nevado de Colima in the TMVB, and at Illiamna in Alaska (Fig. 2c). In addition, interstratified soils and alluvial deposits are observed. At different scales, different avalanche lithofacies along structural units have been repertoried (Table 1).

Spatial distributions and morphological parameters

From mapped surfaces and published data on collapsed edifices worldwide, the spatio-temporal distributions of 17 VDADs from selected volcanic complexes have been cataloged and compared (Siebert 1984; Siebert et al. 1987; Capra 2007; Dufresne et al. 2021) in order to provide complementary information on volcanic collapses during Quaternary glacial and interglacial variations. Distinct quaternary periods are considered, including the Greenlandien with regional warming (-11.7-8.2 ka), the Northgrippien with cooling due to a disruption in ocean circulation related to the melting of glaciers (-8.2-4.2 ka), and the Meghalayeen (between − 4.2 ka and today) with extreme drought. The geographical positions and data on terrestrial volcanoes are freely available from georeferenced Google Earth images and the Global Volcanism Program databases (Smithsonian Institution). The spatio-temporal distributions of these quaternary volcanic collapses were established by several statistical regressions. The square root of R is used to calculate the variance of the analyzed sample values in relation to the statistical regressions.

The investigated collapsed edifices have been analyzed between 2518 and 6263 m above sea level. Based on mapped surfaces and georeferenced image analysis, the basal area of the volcanic edifice, the flank slope gradient, and the profile shape (ratio of the altitude with length) of the collapsed volcanic edifices were calculated (Grosse et al. 2009). The objective is to establish the morphological correlations of the collapsed edifice with climatic surface conditions, the latitudinal temperature gradient, and the lapse rate of temperature with altitude.

Semi-quantitative analysis

From a few examples, the sedimentological parameters of the avalanche units (Table 2) have been compared based on data from Mount Saint Helens VDAD in the United States (Glicken 1986), Shiveluch VDAD in Kamchatka (Belousov et al. 1999), along the TMVB (Nevado de Toluca, Nevado de Colima, Colima, Capra and Macias 2002; Capra 2007; Caballero and Capra 2011; Roverato et al. 2014), Illiamna VDAD in Alaska (Waythomas et al. 2000), the Taranaki VDAD in New Zealand (Roverato et al. 2014), and the Chimpa, Azufral and Chimborazo VDADs in CVZ (Moreno et al. 2021; Bustos et al. 2022). Field observations help to characterize avalanche transformations, such as those at El Misti debris flows in Peru (Bernard et al. 2017) and the Meager rock-avalanche deposits in British Columbia (Bernard 2020).

The sieving of matrix samples at a one-Φ interval in the >–6 Φ to < 6 Φ size range has been used to study the grain-size distributions of the studied VDADs (El Misti and Tutupaca in Peru, Meager in British Columbia). To distinguish primary avalanche units from the transformed and associated deposits, the cumulative curves of the clast-size distributions were compared (eg Scott et al. 1995). To characterize the matrix breccias related to the spatial distribution of the mass flow, the percentage of matrix (≤ 2 mm), the ratio of matrix sand-sized gravels, and the following statistical parameters were computed (Folk and Ward 1957; Blott and Pye 2008). From the statistical parameters of 17 VDADs (Table 2), the median diameter, matrix percentage, sand-sized matrix/gravel ratio, sorting index, skewness, and kurtosis values have been calculated and compared. The statistical results have been analyzed and discussed for any number of readings (N) when N ≥ 12. From these results, several regressions (with R² > 0.4) were established to differentiate these quaternary VDADs. The intersecting points between several regressions indicate similar values for the studied parameters, suggesting co-genetic relationships. Statistics, such as the standard deviation and Pearson's coefficient, have been computed. Pearson's coefficient between two parameters helps to describe the linear correlations, which are considered statistically significant for values exceeding 0.01.

Several sedimentary parameters were compared relative to the location of collapsed edifices, the age of the VDADs, and their syn-sedimentary flow process. Several regression models (Table 3) were established in order to characterize the cataclastic processes during sequential mass spreading. The sedimentary analysis contributes to determining: the stratigraphy and textural variations of the VDADs related to the spatio-temporal distributions along the collapsed volcanic complex; the quantitative sedimentary comparisons of the different VDAD lithofacies to characterize the dynamics of the mass flow; and the avalanche transformations related to climate changes (Zernack and Procter 2021). Furthermore, the median diameter, the matrix percent, and the matrix/gravels were compared to the morphological values of collapsed edifices. Several statistical regressions (Eqs. 10–28 in Table 3) were established to differentiate the relationships between the collapsed edifices and the sedimentary characteristics of the associated VDADs related to quaternary glacial and interglacial variations.

Limitations of the study

Some limitations of the study include the selection of well-preserved quaternary collapsed edifices from the published data on volcanoes worldwide, and the direct relationships between a proxy-climate effect. The age of the collapsed mass flow is determined by a multimethodic approach. Lithostratigraphy, paleomagnetic polarity reversal stratigraphy, and conventional radiometric dating of interstratified ashes have been used (based on Nb/La, Th/La, Yb/La, K/Ar ¹⁴C, U/Th, Sm/Nd ratios, Siebert 1984; Siebert et al. 1987). However, these sedimentary analyses would be an indirect attempt to establish the statistical correlations between the characteristics of VDADs and quaternary glacial and interglacial variations. The lateral variations in lithostratigraphic relationships between discontinuous structural units have been removed and eroded during a long period of post-collapse erosion, including the water redistribution during the deglaciation and the diagenetic alteration. Furthermore, the reworking by successive mass flow deposits during high-intensity rainstorms, snow melt, and submersion constrains the sedimentaru characteristics of studied deposits.

Spatio-temporal distribution

Figure 3a shows two regressions (Eqs. 1–2) for the western and eastern avalanche deposits with significant correlations (R² > 0.9). A strong correlation has been established between the investigated parameters. The point of intersection (a) indicates similar values around the equator. Figure 3b shows two regressions between − 112 ka and today (Eqs. 3–4) for the VDADs. There is a significant correlation (R² > 0.9) between the recent VDADs (Eq. 3). The Holocene periods have the lowest value of R² around ~ 0.5. The point of intersection (a) indicates similar values for the longitude and time for the recent VDADs situated around − 100°W. In Fig. 3c, the latitude versus age diagram depicts two linear regressions (Eqs. 5–6) and a power regression (Eq. 7) for the VDADs between − 60 ka and today. The lowest values of R² are between 0.35 and 0.66. Two points of intersection (a and b) indicate similar values around the equator and 50°N between − 40 ka and today for the VDADs (Fig. 3c).

Table 2

Statistical parameters from sieved samples of the 17 VDADs showing seven statistical parameters from published data on twelve volcanoes worldwide (Glicken 1986; Siebert et al. 1987; Belousov et al. 1999; Waythomas et al. 2000; Capra 2002; Capra and Macías 2002; Caballero and Capra 2011; Roverato et al. 2014; Bernard et al. 2017; Bustos et al. 2022): 1. Geographical location; 2. Avalanche area (A in km²); 3. *H/L*; 4. Median diameter (*Md = (Φ*₁₆ *+ Φ*_{50 +} Φ₈₄*) / 3*) in phi unit (Φ); 5. Matrix percent; 6. Sand-sized matrix/gravel ratio; 7. Sorting index (*σ= ((Φ*₈₄ *- Φ*₁₆*) / 4) + (Φ*_{95 –} Φ₅*) / 6.6); 8*. Skewness *(SKG = ((Φ*₁₆ *+ Φ*₈₄ *− 2Φ*₅₀*) / 2(Φ*_{84 –} Φ₁₆*)) + ((Φ5 + Φ95 − 2Φ*₅₀*) / 2(Φ*₉₅ *– Φ*₅)); 9. Kurtosis (*K = (Φ*₉₅ *- Φ*₅*) / 2.44(Φ*₇₅ *- Φ*₂₅), Folk and Ward 1957); 10. Reported age (ka) from published data
		1	2	3	4	5	6	7	8	9	10
	Volcanic avalanche deposits	Geographical location	Area (km²)	H/L	Md (Φ)	Matrix %	Matrix / gravels	σ	SKG	K	Reported age (ka)
Cenozoic	Chimpa	24°01'33.1''N 66°06'18.04''W	10	0,09	-2,04	49,32	0,75	2,84	0,09	0,85	Cenozoic
Pleistocen	Azufral	1°05'10''N 77°43'08''W	56,01	0,11	-0,27	60,5	1,54	2,12	-0,17	0,86	-18
	Misti	16°17'38''S 71°24'32''W	-	0,7	-0,83	54,67	1,19	1,85	0,29	0,756	-11.2
	Chimborazo	1°27'50''S 78°48'54''W	150	0,1	-2,34	70,31	6,01	3,38	0,1	1,01	-50
	Nevado de Toluca	19°06'30''N 99°45'30''W	22	0,05	-2,68	34,89	0,53	3,31	0,18	0,94	-28
	Taranaki	39°17'47''S 174°03'50''E	200–250	0,07	-2,47	43,05	0,75	2,98	0,13	1,02	-25
	Colima	19°30'45''N 103°37'06''W	1200	0,09	0,46	53,38	1,14	5,2	0,26	0,78	-18.52
Holocene (Meghalayen)	Antuco	37°24'40''S 72°21'12''W	200	0,1	4,5	44,58	0,8	5,5	0	1,89	-7.1
	Nevada de Colima	19°33'48''N 103°36'30''W	2200	0,04	-0,5	48,33	0,94	4,71	0,32	0,83	-4.3
	Illiamna	60°01'55''N 153°05'29''W	20	0,27	-1,17	46,16	0,857	2,6	0,18	-	-2
	Shiveluch	59°39'12''N 161°21'42''E	196	0,02	-0,1	59,8	2,34	1,48	3,1	-	-0.6
	Tutupaca	17°01'30''S 70°21'29''W	12	0,17 − 0,23	-3,02	25,2	0,34	2,5	0,35	0,96	-0.218
	Illiamna	60°01'55''N 153°05'29''W	7,4	0,3	-0,85	51,33	1,054	2,22	0,203	-	-0.1
	Shiveluch	59°39'12''N 161°21'42''E	98	0,07 − 0,17	0,97	70,1	2,34	2,77	2,99	-	1964
	Illiamna	60°01'55''N 153°05'29''W	1,5 11	0,24 − 0,29	-1,25	40	0,92	2,72	0,268	-	1964
	Mount St- Helens	46°11'29''N 122°11'44''E	64	0,11	-0,6	54,43	1,21	3,43	0,12	0,96	1980
	Meager	50°40'N 123°31'W	16,6	0,17	-1,36	40,89	0,89	1,45	-0,16	0,9	2010
Mean		-	282,06	0,16	-1,18	50,49	1,48	2,87	0,61	0,91	-

Sedimentological parameters

The matrix percent versus median and matrix/gravels diagram (Fig. 4) depicts two regressions (Eqs. 8–9) of the rock avalanche-debris flow deposits with significant correlations (R² > 0.7). A strong correlation has been established between the investigated parameters. These regressions correlate all textural variations in mesoscale structures, ranging from the primary fracturing with < 40% matrix and ranging from 0.34 to 0.53 matrix/gravel ratios with jigsaw cracks; to the transformed matrix into debris-flow with > 50% matrix. The secondary fractured matrix contains 40–50% of the breccia matrix and 0.7–0.9 matrix/gravel ratios. Secondary hybrid avalanches contain over 50% matrix and a sand-sized matrix-gravel ratio exceeding 0.75. These values are associated with the matrix transformation into debris-flow deposits. With a median diameter, the matrix-gravel ratio increases from 0.34 to 2.34. The spatial distribution of the VDADs is compared with the percent of matrix, the matrix/gravel ratio, and the median diameter. For the studied collapsed edifices between 2518 and 6263 m above sea level, a climate relationship appears from the fractured matrix with 25 to 70% matrix and 0.34 to 2.34 matrix/gravels (Table 2). The median, matrix percent, and sand-sized matrix-gravel ratio exhibit an increase with latitude: matrix/gravels from 0.34 to 1.19 with Md around ~-0.8-0.6 Ф for − 16–17°N to 0.5-1 matrix-gravel ratio with median values ranging from − 2.6 to -0.46 Ф for the TMVB. The latitudes between 46 and 60°N, with matrix/gravels around ~ 0.8–2.3 and median values ranging from − 1.36 to 0.9 Ф, have been differentiated (Table 2). The co-genetic evolution is correlated with increasing values of the matrix percent, from 25% around − 17°N to 51–54% around 46–60°N.

The longitude and latitude versus matrix percent diagram in Fig. 5a depicts four regressions (Eqs. 10–13) for the VDADs. The matrix percent increased with the latitude and the longitude. The time versus matrix percent diagram (Fig. 5b) displays two logarithmic regressions for the Pleistocene and Holocene VDADs (Eqs. 14–15). For the collapsed edifices between 2518 and 6263 m above sea level, the matrix values increased from 35 to 60% between − 25 and − 20 ka, and from 45 to 70% between − 10 ka and today. Figure 5c exhibits two regressions (Eqs. 16–17) for the VDADs. The matrix values increased with the elevation of the collapsed edifices, from 40 to 70% between 2000 and 3000 m above sea level and from 25 to 70% between 4500 and 6000 m above sea level. The studied parameters show modest to moderate correlations with time and elevation data. A strong correlation has been established for the geographical position along the subduction rim.

Figure 6a shows two linear regressions (Eqs. 18–19) for the VDADs. The median diameter, the matrix/gravels versus longitude, time, and elevation diagrams (Figs. 6b-d) exhibit 8 regressions (Eqs. 20–28) for the VDADs. The median diameter and the matrix/gravels increased with the latitude and elevation of the collapsed edifices and decreased with the longitude and age of the VDADs. These values decreased with age between − 100 years, and 2010 from 70 to 40% matrix with matrix-gravel ratios between 0.34 and 1.54, then increased between − 60000-100 years from 34 to 51% matrix related to matrix-gravel ratios between 2.34 and 0.89. The median values show modest to moderate correlations with the geographical position and elevation of the collapsed edifices. Strong correlations have been established between median values and time, between matrix/gravels, longitude, and elevation for the avalanche area.

The collapsed edifices were between 2518 and 6263 m above sea level and covered an area of 467 km² with a profile shape of around 0.46 (Table 2). The average slope of the volcanoes is about 30%. The mean values of the volcanic areas, the flank slope gradient, and the profile shape of the collapsed edifices were compared to the matrix percent, median diameter and matrix/gravel ratios. The studied parameters show modest to moderate correlations with volcanic parameters. The flank slope gradient versus matrix percent diagram in Fig. 7a exhibits a power regression for the VDADs (Eq. 29). The flank slope gradient increased from 16 to 33%, with a matrix between 25 and 70%. A logarithmic regression (Eq. 30) has been differentiated for the profile shape around 0.1–2.3, with a matrix > 45%. Figure 7b demonstrates a power regression for the VDADs (Eq. 31). The area values increased with the percentage of matrix from 4 km² around 25% matrix for Tutupaca ridges to 70% around 1291 km² for Shiveluch VDADs. A strong correlation has been established between the area of the avalanche and the percentage of the matrix.

The median diameter, the matrix/gravels versus flank slope gradient, and profile shape diagrams in Fig. 7c demonstrate four regressions for the studied VDADs (Eqs. 32–35). A strong correlation has been established between the flank slope and the median values. With a flank slope gradient between 7 and 23%, the median diameter and the matrix/gravels increased, respectively, from − 2 to 0.9 Ф and 0.34 to 2.34. These values decreased with profile shape between 0.04 and 2.1. Figure 7d exhibits two regressions for the studied VDADs (Eqs. 36–37). For example, the area values increased with the median and the matrix/gravels from 4 km² between−2−1 Φ with the matrix/gravels around ~ 0.1–1.2 to 2510 km² between−0.7−0.2 Φ with the matrix/gravels around ~ 0.2.

Statistical parameters

The matrix percent and the sand-sized matrix gravel ratios decreased, respectively, from 70 to 25% and from 6 to 0.34 (Table 2). Polymodal matrix breccias are poorly sorted and exhibit very platykurtic distributions (K ranging from 0.78 to 1.02). The values of skewness (< 1.3 with Skg around ~ 3) are low and increase from 0.12 to 0.35.

The sorting index, the skewness, and the kurtosis have been compared and correlated to the spatial distribution of the studied VDADs. A moderate correlation has been established with the volcanic parameters. The statistical parameters versus latitude diagram in Fig. 8a depicts three power regressions for the VDADs (Eqs. 38–40), and four regressions (Eqs. 41–44), which are related to the longitude of the VDADs (Fig. 8b). The sorting index has been differentiated into western and eastern longitudes. The statistical parameters versus time or elevation diagrams (Figs. 8c-d) indicate six regressions for the VDADs (Eqs. 45–50). The values of skewness increased from − 0.6 to 3 in accordance with the latitude, longitude, age of the VDADs, and elevations of the collapsed edifices. A strong correlation has been established for the skewness values with geographical positions and time. The sorting index decreased from 5.5 to 1.4 with latitude, and the age of the VDADs, and increased with longitude, and the elevations of the collapsed edifices. Two regressions for the sorting index are related to the western and eastern longitudes, time between − 25-0.21 ka and − 18 ka and today, and elevation between 2000–3000 m and 4500–6000 m above sea level. The kurtosis values decreased from 1.8 to 0.78 with latitude, and increased with longitude and age for the VDADs. A strong correlation has been established between the kurtosis values and time.

The mean values of the volcanic areas, the flank slope gradient, and the profile shape of the collapsed edifices are also compared with the statistical parameters of the associated VDADs, such as the sorting index, skewness, and kurtosis. The studied parameters show a moderate correlation with volcanic parameters. Figure 9a exhibits three regressions for the studied VDADs (Eqs. 51–53). The kurtosis values increased with the flank slope gradient, while the sorting index and skewness values decreased between 8 and 30% of the flank slope gradient. The sorting index increased from 1.4 to 5.5 between 0.04 and 2.1 (Fig. 9b). The values for profile shape decreased around 0.04–2.1. A strong correlation has been established between the sorting and the profile shape. The statistical parameters versus the volcanic area diagram in Fig. 11c show three regressions for the studied avalanche deposits (Eqs. 57–59). The area values increased with statistical parameters from 4 km² between−2 and 2 to 2510 km² around 0.7–2.9 for the studied VDADs. A strong correlation has been established for the volcanic area with skewness and kurtosis values.

These quantitative analyses correlate the following characteristics about the quaternary volcanic collapses: the spatio-temporal distribution of quaternary collapses associated with volcano-tectonic context and water saturation during glacial and interglacial variations; and the sedimentary variations and matrix transformations related to a quaternary proxy-climate effect.

Spatial-temporal distribution

The spatial distributions of VDADs exhibit different correlations. The eastern VDADs have negative correlations (Eqs. 1, 4 and 7). The oldest avalanche deposits are situated between 0 and 10°N and are associated with collapsed edifices along the North American subduction zone. Quaternary periods affect regression models related to decreasing R² values. The intersecting point indicates the convergent evolution of volcanic collapses around the Equator (Fig. 5a), and between − 25 ka and today. These are related to the volcanic destabilization moving north. Furthermore, the recurrent volcanic collapses of 75% of Pleistocene Andean volcanoes in the CAVZ (Francis 1994) are associated with the Superfaults (Spray 1997), and the subduction of the Nazca microplates.

The El Misti and Pichu Pichu volcanic complexes in Peru, which exhibit avalanche transformations into debris-flow, are differentiated from the Tutupaca ridges with associated pyroclastic deposits (Valderrama et al. 2016; Bernard et al. 2017, 2022). Few volcanic collapses occurred just after the Last Glacial Maximum (22 000–18 000 cal BP), with a lower limit of permanent snow around ~ 3600 m above sea level, such as the Nevado de Colima deposits along the TMVB associated with the subduction of the Cocos and Rivera plates (Siebe et al. 1992; Capra and Macias 2002; Bandy et al. 2005; Roverato et al. 2011). The positive correlations (Eqs. 2–3 and 5–6) with the northeastern VDADs between − 20 ka and today include the water-saturated collapses of hydrothermally altered VDADs along the Cascade magmatic arc localized along subduction of the Juan de Fuca plate (Bernard 2020). The avalanche flows on Glaciers have been observed for Iliamna volcano during the Holocene in Alaska (Waythomas et al. 2000).

Sedimentological variations of avalanche basement

Recurrent destabilizations and sedimentological variations (Tables 1 and 2) are associated with the cyclic growth and destruction of volcanoes, including Nevado de Colima VDADs along the TMVB, Shiveluch volcano in Kamchatka, and Illiamna VDADs in Alaska (Belousov et al. 1999; Capra et al. 2002; Zernack et al. 2009, 2021; Roverato et al. 2014). The morphological parameters of the collapsed edifices are identified. Sedimentological parameters from VDADs increased with volcanic areas (Table 2), implying relationships with granular disaggregation during mass flow spreading (Glicken 1986). More fragmented debris avalanches experience greater spreading, which inundates larger areas. A typical reverse grading of VDADs contributes to a lower basal shear, which increases the avalanche run-out. A matrix-rich avalanche deposit is overlain by a thick breccia carapace (eg van Wyk de Vries et al. 2001).

For example, the tectonic influences on the emplacement of channeled debris avalanches in the basement are distinct from the volcanic substratum heterogeneity, such as the frictionite found at Pichu Pichu in Peru and Mont-Dore in the French Massif Central (Legros et al. 2000; Bernard and van Wyk de Vries 2017), or the soft ignimbritic basement of the Mombacho volcano (Shea and van Wyk de Vries 2008). Distal volcano-tectonic variations contribute to matrix transformation into debris-flow along the frontal thrust lobe, like in the Arequipa basin in Peru, and Mont-Dore in France (Bernard et al. 2017; Bernard and van Wyk de Vries 2017). Substrate lithology and the geomorphology of the area that the VDAD runs out across also have an influence; for example, the entrainment of halloysite clay-rich palaeosols at Taranaki resulted in increased run-out due to lubrication (Roverato et al. 2014).

Sedimentary fragmentation

The statistical grain-size parameters highlight the cataclastic segregation of the poorly sorted matrix, which comprises approximately 0.89 matrix/gravels, with a median around ~-1.36 Φ and σ = 1.45–5.2 (Table 2). These values decreased from 70 to 25% with matrix/gravels ranging from 6 to 0.3. These values are correlated with the cataclastic segregation of the very poorly sorted matrix fractions, such as the Tutupaca ridges in Peru, the transformed matrix of the Mount Saint Helens VDADs in the United States and the Chimborazo VDADs in the Equator. The syn-emplacement textures of the transformed matrix have resulted in higher skewness and kurtosis values (SKg = 0-0.55; K = 1-1.07), as observed in the Ruapehu, Chimborazo, Meager and Ticsani VDADs (Keigler et al. 2011; Bernard et al. 2019).

Table 3

Synthesis of sedimentological parameters related to spatio-temporal distributions and the morphological parameters of the volcanic complex and the associated avalanche deposits (Figs. 10–15)
	Regressions for the calculated parameters related to the collapsed volcanic edifices
	Spatial distribution	Time	Elevation	Flank slope gradient	Profil shape	Volcanic area
Matrix %	10. f(x) = -19ln(x) + 240.9 11. f(x) = 49ln(x) – 144.4 12. f(x) = 1.5x – 102.9 13. f(x) = 2.2x – 220.2	14. f(x) = 15288.1ln(x) − 62631 15. f(x) = 19848ln(x) – 98768	16. f(x) = 18x+ 4632.3 17. f(x) = 844ln(x) – 233	29. f(x) = 3.4x^0,4	30. f(x) = -1.6ln(x) + 6	31. f(x) = 0x^5.34
Median Matrix/ gravels	19. f(x) = 0.02x – 1.8 21. f(x) = -0.016x − 3.7 22. f(x) = 0.026x – 3.5 18. f(x) = 0.01x + 1.1 20. f(x) = 0.3 * 0.9^x	24. f(x) = 0x – 0.96 25. f(x) = 0x − 3.12 23. f(x) = 0.77 * 1^x	27. f(x) = -4.3ln(x) + 35.9 28. f(x) = 9.5ln(x) – 77.3 26. f(x) = 0.001x – 2.83	34. f(x) = 0.1x − 3.8 33. f(x) = 0.03x^1.14	35. f(x) = -0.8ln(x) – 2 32. f(x) = 1.5 * 0.5^x	37. f(x) = 0.42ln(x) – 3 36. f(x) = 0.39x^0.18
Sorting Skewness Kurtosis	38. f(x) = 2.7 * 0.9^x 41.f(x) = 0.6 * 0.9^x 42. f(x) = 0.03x − 2.9 40. f(x) = 0.09 * 1.05^x 44. f(x) = 0.01x + 1.16 39. f(x) = 1.03 * 0.9^x 43. f(x) = 0.9 * 1.001^x	45. f(x) = 2.18 * 1x 47.f(x) =−121.29ln(x) + 921.9 46. f(x) = 0x + 0.89	48. f(x) = 0.001x – 1.71 50. f(x) = 0x – 0.2 49. f(x) = 0.71 * 1^x	51. f(x) = -0.05x + 3 52. f(x) = -2ln(x) + 8 53. f(x) =-0.1ln(x) + 1	54. f(x) = 3.1x^0.23 56. f(x)=-0.6ln(x) + 0.05 55. f(x) = 0.8x^− 0.07	57. f(x) = 1.6x^0.07 58. f(x) = 0.4ln(x) – 1.5 59. f(x) = 0x + 0.85
Results	Correlation between spatial distribution with sedimentological parameters	Correlation between time and elevation with sedimentological parameters related to Quaternary erosion		Correlation between sedimentological parameters related to Quaternary morphological parameters of the collapsed edifices

During mass spreading, the fractions of grain-size decreased between the poorly sorted matrix and the transformed deposits. The matrix percent versus matrix/gravels and median diagram (Table 3) presents regressions (Eqs. 8–9) from primary fracture (less than 40%) to matrix transformations into debris flow (more than 60%). The primary fracturing is identified for the coarser fractions of the Tutupaca ridges in Peru and the Meager debris-flow deposits in British Columbia, and the matrix transformations into debris-flow, such as the El Misti lithofacies with a matrix of 45–54%.

The negative correlations (Eqs. 30, 32, 35, 55–56) were established for the sedimentological parameters and profile shape with an increasing sorting index (Table 3). During downstream spreading, granular segregation with cataclastic sorting appears along the profile shapes, such as the Pichu Pichu VDADs in Peru (Bernard et al. 2019). For an increasing flank slope gradient, a reverse relation (Eqs. 29, 33–34 and 51–53) appears between the increasing matrix percent, median, matrix/gravels, and the decreasing values of the statistical parameters (Table 3). These phenomena are associated with dynamic fragmentation, collisional transport, and frictional processes (Capra and Macías 2002; Clavero et al. 2002; Davies and Mc Savenney 2009; Roverato et al. 2014). The correlations between the sedimentological parameters and volcanic area (Eqs. 31, 36–37, 57–59) are positively related to mass flow spreading (Table 3).

A proxy-climate effect

Matrix transformations are rarely associated with climate changes such as Casita landslides in Nicaragua (Kerle and van Wyk de Vries 2001), the El Misti flash flood in Peru, (Thouret et al. 2013; Ettinger et al. 2015), and the rock-avalanche debris-flow in British Columbia (Bernard 2020). The median, matrix percent, and sand-sized matrix-gravel ratio increased with latitude. These statistical grain-size parameters of the VDADs highlight that the water saturation was affected by climatic variations along the TMVB between − 185.2 and − 28 ka BP, and during the recent period for higher latitudes around ~ 46–60°N (Mount Saint Helens, Shiveluch, Illiamna, and Meager).

The sedimentary parameters of the studied VDADs (Table 3) are positively correlated with matrix percent (Eqs. 10–17 and 29–31). A high matrix percent will correspond to higher latitudes, longer longitudes related to increasing elevation, and longer times. The median diameter, matrix percent, matrix/gravels, and skewness values increased with latitude (Eqs. 10,12, 18–19 and 38–40), while the kurtosis values decreased (Table 3). Higher values of the spatio-temporal distributions and elevations of the collapsed edifices contributed to the matrix fracturing with cataclastic gradient and syn-transport sorting. Latitudinal and climate factors affect these results. Table 3 shows that the sedimentary statistical parameters are negatively correlated with longitudes (Eqs. 20–21, 41 and 43). The western and eastern VDADs exhibit an increasing skewness.

Quaternary erosion caused negative relationships (Eqs. 23–25, 45–47) between time and sedimentary parameters (Table 3). The matrix percent increased between − 100 years and today from 40 to 70% and between − 60 − 0.1 ka from 34 to 51%. During high intensity rainstorms, the matrix fractions are reworked and transformed into high velocity debris flows, with dilution downstream into hyperconcentrated floods and flash floods such as Mount Rainier in the United States (Scott et al. 1995, 2001), Illiamna in Alaska (Waythomas et al. 2000), Citlaltepetl and Nevado de Colima in the TMVB (Carrasco-Núñez et al. 1993; Capra and Macías 2002; Roverato et al. 2011), Ruapehu in New Zealand (Keigler et al. 2011), and El Misti in Peru (Bernard et al. 2017).

Table 3 shows that sedimentary statistical parameters have positive correlations with elevation (Eqs. 16–17, 26–28 and 48–50). Higher elevations will correspond to higher matrix percent and matrix/gravels, increasing values of sorting, skewness and kurtosis. These similar positive correlations (Eqs. 16 and 17) can be related to the differentiated nival surface conditions between 2000–3000 m and 4500–6000 m above sea level. Deglaciation, the addition of snow, and water saturation contribute to the proximal fluidization of the rock-avalanche debris-flow (Waythomas et al. 2000; Capra and Macias 2002; Roverato et al. 2011; Valderrama et al. 2016; Bernard 2020).

Sedimentary synthesis of the collapsed mass flow

The occurrence of VDADs and the sedimentary variations are correlated with the Earth movement parameters (Berger and Loutre 1991; Bertrand et al. 2002; Cionco et al. 2020). The positive relationships with the precession of the equinox are established between the sedimentary statistical parameters (Table 3; Fig. 10). From Northgrippien, higher values of precession and latitudes will correspond to higher matrix percent and matrix/gravels, with decreasing statistical parameters. A reverse correlation is observed with decreasing obliquity. The recent VDADs along the Asian arc (1628–1979), and the Juan de Fuca boundaries (1650–2010) have shown the highest values of matrix percent and matrix/gravels with the lowest statistical parameters. Similar Pleistocene variations between − 30 − 25 ka are observed. However, positive correlations are established with the obliquity and precession of the equinox, which affect the water saturation associated with deglaciation and seasonal variations. No avalanche deposits are recorded during climate variations related to decreased solar activity (1400–1550, the Spörer Minimum; 1645–1715, the Maunder Minimum). The avalanche flow transformation is recorded during the transitional context of the Spörer, Maunder and Dalton Minimums, which are related to the 11-year solar cycle and highest irradiance (Bertrand et al. 2002; Lockwood et al. 2017).

The occurrence of the volcanic collapses followed the high volcanic activity during the deglacial period (21 − 9 ka), implying a decrease in glacial volume and a redistribution of mass (Lin et al. 2022). The studied VDADs are cataloged during a short cycle of 24 ka with elevated activity during 8 ka, similar to the Mediterranean tephrite records (Bryson and Goodman 1980). These variations are correlated with the obliquity variations, which are superior to the precession of the equinox. A switch in the magmatic chamber may contribute to volcanic destabilization (Belousov et al. 1999), which is related to the variations of the ratios (0.05%) between the hydrostatic and lithostatic pressures (Bryson and Goodman 1980) following the deglacial period. Several correlations have been established between the spatial distributions, quaternary glacial and interglacial variations, and sedimentary parameters of the VDADs. Morphological variations in collapsed edifices related to the cyclic growth and destruction of volcanoes constrained the recurrent destabilizations. The effects of climate change with the variations of the Earth's orbital parameters are considered. Quaternary erosion, nival surface conditions, and high-intensity rainstorms constrained the avalanche mobility and the matrix transformation into debris flow. These results highlight the importance of the sedimentological studies of the VDADs to better constrain subsequent hazardous flows related to the effects of quaternary glacial and interglacial variations.

The sedimentary inventory of VDADs from worldwide examples provides evidence for recurrent slope failures along volcanic complexes during the Quaternary. The sedimentological variations are correlated with the spatial and temporal distributions and morphological variations of the collapsed edifices. The correlations between mass flow spreading and sedimentary parameters are constrained by the profile shape, the volcanic area, and the slope gradient of collapsed edifices. Negative and positive correlations are associated with the migration of the collapsed edifices along fault zones, and with a convergent evolution of the recurrent destabilization around intersecting points during the Holocene and just after the Last Glacial Maximum. These are related to the critical stability of volcanoes and their quaternary cyclic evolution. The effects of quaternary glacial and interglacial variations related to Earth movement parameters are considered.

Although the sedimentary analysis of the quaternary VDADs contributes to the understanding of proxy-climate effects, the results are limited in both their spatial and temporal extent. The quaternary erosion, nival surface conditions, and strong intensity rainstorm constrained the transformation of the reworked avalanche deposits into debris flow. Further observations and sedimentary analysis of VDADs and associated deposits are needed to better constrain these correlations and subsequent hazardous flows related to quaternary proxy-climate effects.

Acknowledgment

The fieldwork in Peru trip has been funded by the ANR Laharisk research project (ANR-09-RISK-005, Thouret et al.), the French-Peruvian University network termed R Porras Barrenechea and the Institut de la Recherche pour le Développement (IRD) for Tutupaca volcano. F Garcia and GE Tarqui (UNSA), M Rivera, P Valderrama and J Mariňo (INGEMMET) are gratefully acknowledged for field assistance. The fieldwork in Canada has been funded by the French Embassy (D Marty-Dessus) and Simon Fraser University (SFU), and J Clague, GW Jones and K Russel, all from SFU are gratefully acknowledged.

Bandy W, Michaud F, Bourgois J, Calmus T, Dyment J, Mortera-Gutierrez CA, Ortega-Ramirez J, Pontois B, Royer JY, Sichlerg B, Sosson M, Rebolledo-Viera M, Bigot-Cormier F, Diaz-Molina O, Hurtado-Artunduaga AD, Pardo-Castro G, Trouillard-Perrot C (2005) Subsidence and strike-slip tectonism of the upper continental slope of Manzanillo, Mexico. Tectonophysics 398, 115-140
Belousov AB, Belousova MG, Voight B (1999) Multiple edifice failures, debris avalanches and associated eruptions in the Holocene history of Shiveluch volcano, Kamchatka, Russia. Bull Volcanol 61, 324-342
Berger A, Loutre MF (1991) Insolation values for the climate of the last 10 million years. Quaternary Sci Rev 10, 297- 317
Bernard K, Thouret J-C, van Wyk de Vries B (2017) Emplacement and transformations of volcanic debris avalanches - A case study at El Misti volcano, Peru. J Volcanol Geotherm Res 340, 68–91
Bernard K, van Wyk de Vries B (2017) Volcanic avalanche fault zone with pseudotachylite and gouge in French Massif Central. J Volcanol Geotherm Res 347, 112-135
Bernard K, van Wyk de Vries B, Thouret J-C (2019) Fault textures in volcanic debris-avalanche deposits and transformations into lahars: The Pichu Pichu thrust lobes in south Peru compared to worldwide avalanche deposits. J Volcanol Geotherm Res 371, 116-136
Bernard K (2020) Epithermal clast coating inside the rock avalanche-debris flow deposits from Mount Meager Volcanic Complex, British Columbia (Canada). J Volcanol Geotherm Res 402, 106994
Bernard K, van Wyk de Vries B, Samaniego P, Valderrama P, Mariño J (2022) Collisional interactions and the transition between lava dome sector collapse and pyroclastic density currents at Tutupaca volcano (Southern Peru). J Volcanol Geotherm Res 431, 107668
Bertrand C, Loutre MF, Berger A (2002) High frequency variations of the Earth's orbital parameters and climate change. Geophys Res Lett, 29, 1893
Blott SJ, Pye K (2008) Gradistat: a grain size distribution and statistics package for the analysis of unconsolidated sediments. Earth Surf Process Landforms 26, 1237-1248
Bryson RA, Goodman BM (1980) Volcanic activity and climatic changes. Science 207, 1041-1044
Bustos A, Capra L, Arnosio M, Norini G (2022) Volcanic debris avalanche transport and emplacement at Chimpa Volcano (Central Puna, Argentina). J Volcanol Geotherm Res 432, 107671
Caballero L, Capra L (2011) Textural analysis of particles from El Zaguán debris avalanche deposit, Nevado de Toluca volcano, Mexico: Evidence of flow behavior during emplacement. J Volcanol Geotherm Res 200, 75-82
Capra L, Macías JL (2002) The Cohesive Naranjo Debris-Flow Deposit (10 km3): a dam breakout flow derived from the Pleistocene debris-avalanche deposit of Nevado de Colima Volcano (Mexico). J Volcanol Geotherm Res 117, 213-235
Capra L (2007) Volcanic natural dams: identification, stability, and secondary effects. Nat Hazards 43, 45-61
Carrasco-Núñez G, Vallance JW, Rose WI (1993) A voluminous avalanche-induced lahar from Citlaltépetl volcano, Mexico: implications for hazard assessment. J Volcanol Geotherm Res 59, 35–46
Cionco RG, Soon W, Quaranta NC (2020) On the calculation of latitudinal insolation gradients throughout the Holocene. Adv Space Res 66, 3
Clavero JE, Sparks RSJ, Huppert HE (2002) Geological constraints on the emplacement mechanism of the Parinacota avalanche, northern Chile. Bull Volcanol 64, 40–54
Davies TR, McSaveney MJ (2009) The role of rock fragmentation in the motion of large landslides. Eng Geol 109, 67–79
Dufresne A, Siebert L, Bernard B (2021) Distribution and geometric parameters of volcanic debris avalanche deposits. Volcanic Debris Avalanches: From Collapse to Hazard, 75-90
Dufresne A, Zernack A, Bernard K, Thouret JC, Roverato M (2021) Sedimentology of volcanic debris avalanche deposits. Volcanic Debris Avalanches: From Collapse to Hazard, 175-210
Ettinger S, Mounaud L, Magill C, Yao-Lafourcade A-F, Thouret J-C, Manville V, Negulescu C, Zuccaro G, De Gregorio D, Nardone S, Uchuchoque JAL, Arguedas A, Macedo L, Manrique NL (2015) Building vulnerability to hydro-geomorphic hazards: Estimating damage probability from qualitative vulnerability assessment using logistic regression. J Hydrology 541, 563-581
Folk RL, Ward WC (1957) Brazos River bar: a study in the significance of grain size parameters. J Sediment Petrol 27, 3-26
Francis PW (1994) Large volcanic debris avalanches in the central Andes. In: Proceedings of International Conference on Volcano Instability on the Earth and Other Planets, The Geological Society of London
G1icken H (1986) Rockslide-debris avalanche of May 18, 1980, Mount Saint Helens Volcano. PhD Thesis, Univ Calif Santa Barbara
Grosse P, van Wyk de Vries B, Petrinovic IA, Euillades PA, Alvarado GE (2009) Morphometry and evolution of arc volcanoes. Geology 37, 651-654
Grosse P, Danišík M, Apaza FD, Guzmán SR, Lahitte P, Quidelleur X, Self S, Siebe C, van Wyk de Vies B, Ureta G, Guillong M, De Rosa M, Le Roux P, Wotzlay J-F, Bachmann O (2022) Holocene collapse of Socompa volcano and pre- and post- collapse growth rates constrained by multi-system geochronology. Bull Volcanol 84(9), 85
Keigler R, Thouret J-C, Hodgson KA, Neal VE, Lecointre JA, Procter JN, Cronin SJ (2011) The Whangaehu formation: debris-avalanche and lahar deposits from ancestral Ruapehu volcano, New Zealand. Geomorphology 133, 57–79
Kerle N, van Wyk de Vries B (2001) The 1998 debris avalanche at Casita volcano, Nicaragua: investigation of structural deformation as the cause of slope instability using remote sensing. J Volcanol Geotherm Res 105, 49-63
Legros J-F, Cantagrel J-M, Devouard B (2000) Pseudotachylite (Frictionite) at the base of the Arequipa Volcanic landslide (Peru): Implications for emplacement mechanisms. J Geol 108, 601-611
Lin J, Svenoon A, Huidberg CS, Lohmann J, Krishann S, Dahl-Jensen D, Steffensen JP, Rasmussen SO, Cook E, Kjaer HA, Vinther BoM, Fischer H, Stocker T, Sigl M, Bigler M, Sevcri M, Traversi R, Mulvaney R (2022) Magnitude, frequency and climate forcing of global volcanism during the last glacial period as seen in Greenland and Antarctic ice cores (60- 9ka). Clim Past 18, 485-506
Lockwood M, Owens M, Hawkins Ed, Jones GS, Usoskin I (2017) Frost fairs, sunspots and the Little Ice Age. Astron Geophys 58, 2.17–2.23
Makris S, Manzella I, Cole P, Roverato M (2020) Grain size distribution and sedimentology in volcanic mass-wasting flows: implications for propagation and mobility. Int J Earth Sci 109, 2679-2695
Moreno Alfonso SC, Sanchez JJ, Murcia H (2021) Evidence of an unknown debris avalanche event (<0.57 Ma) in the active Azufral volcano (Narino, Colomia). J S Am Earth Sci 107, 103138
Reid ME, Keith TE, Kayen RE, Iverson NR, Iverson RM, Brien DL (2010) Volcano collapse promoted by progressive strength reduction: new data from Mount St. Helens. Bull Volcanol 72, 761-766
Roverato M, Capra L, Sulpizio R, Norini G (2011) Stratigraphic reconstruction of two debris avalanche deposit at Colima Volcano (Mexico): insights into pre-failure conditions and climate influence. J Volcanol Geotherm Res 207, 33-46
Roverato M, Cronin S, Procter J, Capra L (2014) Textural features as indicators of debris avalanche transport and emplacement, Taranaki volcano. Geol Soc Amer Bull 127, 3-18
Roverato M, Larrea P, Casado I, Mulas M, Béjar G, Bowman, L (2018) Characterization of the Cubilche debris avalanche deposit, a controversial case from the northern Andes , Ecuador. J Volcanol Geotherm Res 360, pp.22-35
Scott KM, Vallance JW, Pringle PT (1995) Sedimentology, behavior, and hazards of debris flows at Mount Rainier, Washington. US Geological Survey Professional Paper 1547, 56
Scott KM, Macias JL, Naranjo JA, Rodriguez S, McGeehin JP (2001) Catastrophic debris flows transformed from landslides in volcanic terrains: mobility, hazard assessment, and mitigation strategies. US Geological Survey Professional Paper 1630.59 pp
Shea T, van Wyk de Vries B (2008) Structural analysis and analogue modeling of the kinematics and dynamics of rockslide avalanches. Geosphere 4, 657–686
Siebe C, Komorowski J-C, Sheridan M-F (1992) Morphology and emplacement collapse of an unusual debris avalanche deposit at Jocotitlán Volcano, Central Mexico. Bull Volcanol 54, 573-589
Siebert L (1984) Large volcanic debris avalanches: characteristics of source areas, deposits, and associated eruptions. J Volcanol Geotherm Res 22, 163-197
Siebert L, Glicken H, Ui T (1987) Volcanic hazards from Bezymianny- and Bandai- type eruptions. Bull Volcanol 49, 435- 459
Siebert L, Reid ME (2023) Lateral edifice collapse and volcanic debris avalanches: a post-1980 Mount St. Helens perspective. Bull Volcanol 85(11), 61
Spray JG (1997) Superfaults. Geology 25, 579-582
Thouret J-C, Enjolras G, Martelli K, Santoni O, Luque JA, Nagata M, Arguedas A, Macedo L (2013) Combining criteria for delineating lahar and flash-flood-prone hazard and risk zones for the city of Arequipa, Peru. Nat Hazards Earth Syst Sci 13, 339-360
Ui T, Glicken H (1986) Internal structural variations in a debris-avalanche deposit from ancestral Mount Shasta, California, USA. Bull Volcanol 48, 189-194
Valderrama P, Roche O, Samaniego P, van Wyk de Vries B, Bernard K, Marino J (2016) Dynamic implications of ridges on a debris avalanche deposit at Tutupaca volcano (southern Peru). Bull Volcanol 78, 14
van Wyk de Vries B, Self S, Francis PW, Keszthelyi L (2001) A gravitational spreading origin for the Socompa debris avalanche. J Volcanol Geotherm Res 105, 225-247
Voight B, Janda RJ, Glicken H, Douglass PM (1983) Nature and mechanics of the Mount St Helens rockslide- avalanche of 18 May 1980. Geotech 33(3), 243-273
Walter TR (2011) Structural architecture of the 1980 Mount St. Helens collapse: an analysis of the Rosenquist photo sequence using digital image correlation. Geology, 39(8), 767-770
Waythomas CF, Miller TP, Beget JE (2000) Record of Late Holocene debris avalanches and lahars at Iliamna Volcano, Alaska. J Volcanol Geotherm Res 104, 97-130
Zernack A, Procter J, Cronin S (2009) Sedimentary signatures of cyclic growth and destruction of stratovolcanoes: a case study from Mt Taranaki, New Zealand. Sediment Geol 220, 288-305
Zernack A, Procter JN (2021) Cyclic growth and destruction of volcanoes. In Roverato M, Dufresne A, Procter J, editors. Volcanic Debris Avalanches: From collapse to hazard. Springer 311-355 pp

Table 1 is available in the Supplementary Files section.

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Statistical sedimentary aspects of collapsed volcanic edifices along subduction zones.

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Abstract

Figures

Introduction

Geological setting

Material and methods

Spatial distributions and morphological parameters

Semi-quantitative analysis

Limitations of the study

Results

Spatio-temporal distribution

Sedimentological parameters

Statistical parameters

Discussion

Spatial-temporal distribution

Sedimentological variations of avalanche basement

Sedimentary fragmentation

A proxy-climate effect

Sedimentary synthesis of the collapsed mass flow

Conclusions

Declarations

Acknowledgment

References

Table 1

Supplementary Files

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