Rift Focusing and Magmatism During Late‐Stage Rifting in Afar

Moore, Chris; Wright, Tim; Hooper, Andrew

doi:10.1029/2020jb021542

Cited by 8 publications

(6 citation statements)

References 121 publications

(233 reference statements)

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“…The present-day deformation of the Afar rift is well constrained by geodetic observations (Moore et al, 2021;Pagli et al, 2014). Here we combine available GPS data (Doubre et al, 2017) with InSAR from two different Sentinel-1a/b tracks in ascending and descending geometries spanning the period between 2014 and 2021 to generate a high-resolution continuous 3D velocity field for central Afar following the method described in Pagli et al (2014).…”

Section: Deformation Rate From Geodesymentioning

confidence: 99%

To Rotate or to Link? The Connection Between the Red Sea and Gulf of Aden Rifts in Central Afar

Muluneh,

Brune,

Pagli

et al. 2024

Geophysical Research Letters

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Central Afar is shaped by the interaction between the Red Sea (RS) and Gulf of Aden (GoA) rifts. While there have been several studies conducted in the region, we know surprisingly little about the mechanism of connection between these two rift branches. Here we use high‐resolution 3D lithospheric scale geodynamic modeling to capture the evolution of linkage between the RS and GoA rifts in central Afar. Our results demonstrate that the two rifts initially overlap and interact across a broad zone of faulting and vertical axis block rotation. However, through time, rift overlap is abandoned in favor of direct linkage which generates a series of localized en‐echelon basins. The present‐day direct linkage between the two rifts is supported by geodetic observations. Our study reconciles previously proposed models for the RS and GoA rift connection by considering spatial and temporal evolution of the rifts.

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Section: Deformation Rate From Geodesymentioning

confidence: 99%

To Rotate or to Link? The Connection Between the Red Sea and Gulf of Aden Rifts in Central Afar

Muluneh,

Brune,

Pagli

et al. 2024

Geophysical Research Letters

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“…For the seven steady deformation signals detected, velocities range from −3.2 to 4.6 cm/yr with most of the cases showing slow subsidence (Table 2). Steady uplift over the 5-year time period was only reported at two volcanoes, Corbetti and Dabbahu, where sustained uplift has been attributed to a combination of magma movement and a viscoelastic rheology [44,45].…”

Section: Signal-to-noise Ratiomentioning

confidence: 99%

“…From our study, we show that the average error is ∼0.5 cm/yr for the EARS, which is five times larger than the errors associated with noise. Therefore, the use of an external dataset (e.g., GNSS data) for referencing Sentinel-1, e.g., [3,45], is crucial to be able to study strain accumulation and partitioning in the EARS, where extension rates range from ∼2 mm/yr in Malawi to ∼6 mm/yr in the Main Ethiopian Rift [68].…”

Section: Velocity Errors For Long-term Signalsmentioning

confidence: 99%

Routine Processing and Automatic Detection of Volcanic Ground Deformation Using Sentinel-1 InSAR Data: Insights from African Volcanoes

et al. 2022

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Since the launch of Sentinel-1 mission, automated processing systems have been developed for near real-time monitoring of ground deformation signals. Here, we perform a regional analysis of 5 years over 64 volcanic centres located along the East African Rift System (EARS). We show that the correction of atmospheric signals for the arid and low-elevation EARS volcanoes is less important than for other volcanic environments. We find that the amplitude of the cumulative displacements exceeds three times the temporal noise of the time series (3σ) for 16 of the 64 volcanoes, which includes previously reported deformation signals, and two new ones at Paka and Silali volcanoes. From a 5-year times series, uncertainties in rates of deformation are ∼0.1 cm/yr, whereas uncertainties associated with the choice of reference pixel are typically 0.3–0.6 cm/yr. We fit the time series using simple functional forms and classify seven of the volcano time series as ‘linear’, six as ‘sigmoidal’ and three as ‘hybrid’, enabling us to discriminate between steady deformation and short-term pulses of deformation. This study provides a framework for routine volcano monitoring using InSAR on a continental scale. Here, we focus on Sentinel-1 data from the EARS, but the framework could be expanded to include other satellite systems or global coverage.

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“…In this section, we focus on the Afar region of Ethiopia where Albino and Biggs (2021) and Moore et al (2021) 2), of which 6 have known deformation: Alu-Dalafilla, Dabbahu-Hararo, Dallol, Erte Ale, Gada Ale and Nabro (Albino and Biggs 2021). The mean coherence is > 0.34 (Table 2) and the volcanoes are mostly low relief such that atmospheric artefacts are negligible.…”

Section: Signal Characteristicsmentioning

confidence: 99%

Large-scale demonstration of machine learning for the detection of volcanic deformation in Sentinel-1 satellite imagery

et al. 2022

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Radar (SAR) satellites systematically acquire imagery that can be used for volcano monitoring, characterising magmatic systems and potentially forecasting eruptions on a global scale. However, exploiting the large dataset is limited by the need for manual inspection, meaning timely dissemination of information is challenging. Here we automatically process ~ 600,000 images of > 1000 volcanoes acquired by the Sentinel-1 satellite in a 5-year period (2015–2020) and use the dataset to demonstrate the applicability and limitations of machine learning for detecting deformation signals. Of the 16 volcanoes flagged most often, 5 experienced eruptions, 6 showed slow deformation, 2 had non-volcanic deformation and 3 had atmospheric artefacts. The detection threshold for the whole dataset is 5.9 cm, equivalent to a rate of 1.2 cm/year over the 5-year study period. We then use the large testing dataset to explore the effects of atmospheric conditions, land cover and signal characteristics on detectability and find that the performance of the machine learning algorithm is primarily limited by the quality of the available data, with poor coherence and slow signals being particularly challenging. The expanding dataset of systematically acquired, processed and flagged images will enable the quantitative analysis of volcanic monitoring signals on an unprecedented scale, but tailored processing will be needed for routine monitoring applications.

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Rift Focusing and Magmatism During Late‐Stage Rifting in Afar

Abstract: It has long been hypothesized that magma plays a crucial role in the final stages of continental breakup and the early stages of sea-floor spreading (

Cited by 8 publications

References 121 publications

To Rotate or to Link? The Connection Between the Red Sea and Gulf of Aden Rifts in Central Afar

To Rotate or to Link? The Connection Between the Red Sea and Gulf of Aden Rifts in Central Afar

Routine Processing and Automatic Detection of Volcanic Ground Deformation Using Sentinel-1 InSAR Data: Insights from African Volcanoes

Large-scale demonstration of machine learning for the detection of volcanic deformation in Sentinel-1 satellite imagery

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