Quantitative spatial distribution analysis of mafic monogenic volcanism in the southern Puna, Argentina: Implications for magma production rates and structural control during its ascent

Morfulis, Marcos; Báez, Walter; Retamoso, Santiago; Bardelli, Lorenzo; Filipovich, Rubén Eduardo; Sommer, Carlos Augusto

doi:10.1016/j.jsames.2020.102852

Cited by 8 publications

(18 citation statements)

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“…To determine the eruption event record for Harrat Khaybar, we carried out a morphological analysis using satellite imagery at various scales to integrate the new radiometric ages, vent locations, and tectonic structures, information that was later used to determine the relative eruption timing, volcanic alignments and spatial density estimates. The morphological analysis was conducted following the Geographic Information System methodological approaches of Kereszturi et al (2016), Haag et al (2019), andMorfulis et al (2020), and integrated into a geospatial volcanic database. This database (available as Additional file 1) forms the basis of our analysis to reveal the spatial and temporal evolution of the harrat (cf.…”

Section: Methodsmentioning

confidence: 99%

“…Detecting alignments in volcanic fields using point features (i.e., volcanic vent coordinates) can shed some light on the key controls of intraplate volcanic origin or migration of magma. In other words, vent alignments can represent areas of magma focusing or structural weaknesses, along which volcanism returns in repeated episodes (e.g., Kear 1964;Le Corvec et al 2013;Morfulis et al 2020;Sieron et al 2021). Several methods have been developed to identify orientations in volcanic fields, including: two-point azimuth techniques (Lutz 1986;Lutz and Gutmann 1995;Bleacher et al 2009;Richardson et al 2013), spatial transform methods (e.g., Hough's transform ;Connor 1990;Connor et al 1992;Von Veh and Németh 2009), and strip methods (Zhang and Lutz 1989; Arcasoy et al 2004).…”

Section: Alignment Analysismentioning

confidence: 99%

“…Several methods have been developed to identify orientations in volcanic fields, including: two-point azimuth techniques (Lutz 1986;Lutz and Gutmann 1995;Bleacher et al 2009;Richardson et al 2013), spatial transform methods (e.g., Hough's transform ;Connor 1990;Connor et al 1992;Von Veh and Németh 2009), and strip methods (Zhang and Lutz 1989; Arcasoy et al 2004). In our study, we focused on the nearest neighbor azimuth method of Cebriá et al (2011) using the Thomson and Lang (2016) Matlab tool (e.g., Grosse et al 2020;Morfulis et al 2020). This technique is an improved version of the two-point azimuth method of Lutz (1986).…”

Section: Alignment Analysismentioning

confidence: 99%

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Spatio-temporal forecasting of future volcanism at Harrat Khaybar, Saudi Arabia

et al. 2022

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The 180,000 km2 of Arabian lava fields (“harrats” in Arabic) form one of the largest distributed basaltic provinces in the world. The most recent eruption in 1256 AD, on the outskirts of Medina, as well as shallow dike emplacement in 2009, ~ 200 km northeast of the city, suggest future volcanic threat to this area. Harrat Khaybar (~ 1.7 Ma to present) is one of the largest and most compositionally diverse Arabian lava fields; it is located ~ 137 km northeast of Medina and covers ~ 14,000 km2. Here, we present a new eruption event record and the first estimation of future potential locations and timing of volcanism in Harrat Khaybar. Volcanic vents and eruptive fissures were mapped using remote sensing and field studies, and categorized into a geospatial database, complemented by 16 new 40Ar/39Ar ages. Our analysis reveals that Harrat Khaybar developed over five eruptive phases, where vent locations over time focus towards the central axis forming a broad N-S trend, with a central group concentrated along an axis of the regional Makkah-Madinah-Nafud (MMN) line and wider spatial dispersion between vents outwards from there. For the whole field, we estimate a long-term average recurrence rate of ~ 2.3 eruptions per 10 kyr assuming a Poisson distribution for inter-event times, which indicates that Harrat Khaybar would belong to a global group of highly active distributed volcanic fields. Our analysis also reveals that the field likely had a “flare-up” period between 450 and 300 ka where the vast majority of eruptions occurred, with ~ 18 eruptions per 10 kyr. After this intense period, eruption rates fell to < 2 eruptions per 10 kyr. Based on our findings, we estimate cumulative probabilities of 1.09 and 16.3% as lower and upper bounds of at least one eruption occurring over the next 100 years somewhere in Harrat Khaybar, with the highest probabilities within the central axis region, in particular around Jabal Qidr, Bayda and Abyad.

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Section: Methodsmentioning

confidence: 99%

Section: Alignment Analysismentioning

confidence: 99%

Section: Alignment Analysismentioning

confidence: 99%

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Spatio-temporal forecasting of future volcanism at Harrat Khaybar, Saudi Arabia

et al. 2022

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“…In addition, along with the arc broadening and narrowing, back-arc mafic magmatism also occurred in this region in response to lithospheric foundering events (e.g., Kay et al, 1994;Risse et al, 2013;Mulcahy et al, 2014;Maro et al, 2017;Chen et al, 2020;Gao et al, 2021). Collectively, volcanism has been controlled by a range of varying tectonic processes (e.g., Tibaldi et al, 2017;Naranjo et al, 2018;Morfulis et al, 2020).…”

Section: Introductionmentioning

confidence: 97%

Probabilistic Volcanic Hazard Assessment of the 22.5–28°S Segment of the Central Volcanic Zone of the Andes

Bertin

Lindsay²,

Cronin³

et al. 2022

Front. Earth Sci.

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Evaluation of volcanic hazards typically focusses on single eruptive centres or spatially restricted areas, such as volcanic fields. Expanding hazard assessments across wide regions (e.g., large sections of a continental margin) has rarely been attempted, due to the complexity of integrating temporal and spatial variability in tectonic and magmatic processes. In this study, we investigate new approaches to quantify the hazards of such long-term active and complex settings, using the example of the 22.5–28°S segment of the Central Volcanic Zone of the Andes. This research is based on the estimation of: 1) spatial probability of future volcanic activity (based on kernel density estimation using a new volcanic geospatial database), 2) temporal probability of future volcanic events, and 3) areas susceptible to volcanic flow and fall processes (based on computer modeling). Integrating these results, we produce a set of volcanic hazard maps. We then calculate the relative probabilities of population centres in the area being affected by any volcanic phenomenon. Touristic towns such as La Poma (Argentina), Toconao (Chile), Antofagasta de la Sierra (Argentina), Socaire (Chile), and Talabre (Chile) are exposed to the highest relative volcanic hazard. In addition, through this work we delineate five regions of high spatial probability (i.e., volcanic clusters), three of which correlate well with geophysical evidence of mid-crustal partial melt bodies. Many of the eruptive centres within these volcanic clusters have poorly known eruption histories and are recommended to be targeted for future work. We hope this contribution will be a useful approach to encourage probabilistic volcanic hazard assessments for other arc segments.

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“…This is due, in part, to the spatial and temporal variation of magma properties during dike propagation, such as bubble segregation in conduits [Corazzato and Tibaldi 2006;Pioli et al 2009;McGee and Smith 2016;Taddeucci et al 2021]. Interaction with crustal structures and the local stress field also control the orientation and distribution of emplacement patterns [Gómez-Vasconcelos et al 2020;Kósik et al 2020;Morfulis et al 2020]. The highest points of flux along the fissure will eventually focus activity, creating spatter cones, armored ramparts, or other small-scale features [Parcheta et al 2013;Rivalta et al 2015] (Figure 2).…”

Section: Introductionmentioning

confidence: 99%

Modelling eruptive source parameters in distributed volcanic fields

Gallant

Cole²,

Connor

et al. 2021

Volcanica

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Vent opening hazard models are routinely used as inputs for assessing distal volcanic hazards (lava flows, tephra fallout) in distributed volcanic fields. These vent opening hazard models have traditionally relied on the location of mapped vents; seldom have they taken into account how vents are linked in space and time. We show that inputs needed to appropriately model distal hazards are fundamentally different than thoses required to model near-vent hazards (ground deformation). We provide a computational model to obtain more appropriate eruptive source parameters (ESPs) for distal volcanic hazard sources and show the utility of our code through three examples. The code's strength is that it links events based on the spatio-temporal relationships of vents through heirarchical clustering. The development of the code and its strenghts and weaknesses are discussed. This work challenges previous ideas about ESPs and we hope this work leads to further improvement in hazard assessment methods.

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Quantitative spatial distribution analysis of mafic monogenic volcanism in the southern Puna, Argentina: Implications for magma production rates and structural control during its ascent

Cited by 8 publications

References 65 publications

Spatio-temporal forecasting of future volcanism at Harrat Khaybar, Saudi Arabia

Spatio-temporal forecasting of future volcanism at Harrat Khaybar, Saudi Arabia

Probabilistic Volcanic Hazard Assessment of the 22.5–28°S Segment of the Central Volcanic Zone of the Andes

Modelling eruptive source parameters in distributed volcanic fields

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