Assessing infrequent large earthquakes using geomorphology and geodesy in the Malawi Rift

In regions with large, mature fault systems, a characteristic earthquake model may be more appropriate for modelling earthquake occurrence than extrapolating from a short history of small, instrumentally observed earthquakes using the Gutenberg–Richter scaling law. We illustrate how the geomorphology and geodesy of the Malawi Rift, a region with large seismogenic thicknesses, long fault scarps, and slow strain rates, can be used to assess hazard probability levels for large infrequent earthquakes. We estimate potential earthquake size using fault length and recurrence intervals from plate motion velocities and generate a synthetic catalogue of events. Since it is not possible to determine from the geomorphological information if a future rupture will be continuous (7.4 ≤ M W ≤ 8.3 with recurrence intervals of 1,000–4,300 years) or segmented (6.7 ≤ M W ≤ 7.7 with 300–1,900 years), we consider both alternatives separately and also produce a mixed catalogue. We carry out a probabilistic seismic hazard assessment to produce regional- and site-specific hazard estimates. At all return periods and vibration periods, inclusion of fault-derived parameters increases the predicted spectral acceleration above the level predicted from the instrumental catalogue alone, with the most significant changes being in close proximity to the fault systems and the effect being more significant at longer vibration periods. Importantly, the results indicate that standard probabilistic seismic hazard analysis (PSHA) methods using short instrumental records alone tend to underestimate the seismic hazard, especially for the most damaging, extreme magnitude events. For many developing countries in Africa and elsewhere, which are experiencing rapid economic growth and urbanisation, seismic hazard assessments incorporating characteristic earthquake models are critical

Magmatic Processes in the East African Rift System: Insights from a 2015-2020 Sentinel-1 InSAR survey

Abstract The East African Rift System (EARS) is composed of around 78 Holocene volcanoes, but relatively little is known about their past and present activity. This lack of information makes it difficult to understand their eruptive cycles, their roles in continental rifting and the threat they pose to the population. Although previous InSAR surveys (1990–2010) showed sign of unrest, the information about the dynamics of the magmatic systems remained limited by low temporal resolution and gaps in the data set. The Sentinel‐1 SAR mission provides open‐access acquisitions every 12 days in Africa and has the potential to produce long‐duration time series for studying volcanic ground deformation at regional scale. Here, we use Sentinel‐1 data to provide InSAR time series along the EARS for the period 2015–2020. We detect 18 ground deformation signals on 14 volcanoes, of which six are located in Afar, six in the Main Ethiopian Rift, and two in the Kenya‐Tanzanian Rift. We detected new episodes of uplift at Tullu Moje (2016) and Suswa (mid‐2018), and enigmatic long‐lived subsidence signals at Gada Ale and Kone. Subsidence signals are related to a variety of mechanisms including the posteruptive evolution of magma reservoirs (e.g., Alu‐Dallafila), the compaction of lava flows (e.g., Nabro), and pore‐pressure changes related to geothermal or hydrothermal activity (e.g., Olkaria). Our results show that ∼20% of the Holocene volcanoes in the EARS deformed during this 5‐years snapshot and demonstrate the diversity of processes occurring

Observing eruptions of gas-rich compressible magmas from space

Observations of volcanoes from space are a critical component of volcano monitoring, but we lack quantitative integrated models to interpret them. The atmospheric sulfur yields of eruptions are variable and not well correlated with eruption magnitude and for many eruptions the volume of erupted material is much greater than the subsurface volume change inferred from ground displacements. Up to now, these observations have been treated independently, but they are fundamentally linked. If magmas are vapour-saturated before eruption, bubbles cause the magma to become more compressible, resulting in muted ground displacements. The bubbles contain the sulfur-bearing vapour injected into the atmosphere during eruptions. Here we present a model that allows the inferred volume change of the reservoir and the sulfur mass loading to be predicted as a function of reservoir depth and the magma’s oxidation state and volatile content, which is consistent with the array of natural data

Evidence for Active Rhyolitic dike Intrusion in the Northern Main Ethiopian Rift from the 2015 Fentale Seismic Swarm

Abstract Magmatic intrusions play a vital role not only in accommodating extensional stresses in continental rifts but also in feeding volcanic systems. The location, orientation, and timescale of dike intrusions are dictated by the interaction of regional and local stresses, the effect of pre‐existing weaknesses, and the composition of magma. Observing active intrusions can provide important information regarding the interaction between magmatic processes and the tectonic stress field during continental rifting. We focus on a seismic swarm that occurred in 2015 to the northeast of Fentale volcano, in the Main Ethiopian Rift (MER), and use radar interferometry to study surface deformation associated with the seismic swarm. Interferograms show a pattern of dike‐induced deformation, with a model estimate of volume change of 33×106±0.6×106m3 at a depth range of 5.4 to 8 km. We use a small baseline subset algorithm to calculate line of sight time series and find that the displacements decay exponentially with a decay constant of ∼83 days. Coupled source‐sink models suggest that such slow dike intrusions require a high viscosity rhyolitic magma. The difference in behavior between Fentale and other caldera systems in the MER, which show multi‐year cycles of inflation and deflation, suggests fundamental differences in magma composition and architecture of the plumbing system. This is the first direct observation of a dike intrusion in the MER and provides new constraints on the temporal‐spatial patterns of stress and strain that occur during continental rifting. Whether this activity is transient or a long‐term feature associated with rift evolution is an open question

Solving the Insoluble: A New Rule for Quantization

The rules of canonical quantization normally offer good results, but sometimes they fail, e.g., leading to quantum triviality ($=$ free) for certain examples that are classically nontrivial ($\ne$ free). A new procedure, called Enhanced Quantization, relates classical models with their quantum partners differently and leads to satisfactory results for all systems. This paper features enhanced quantization procedures and provides highlights of two examples, a rotationally symmetric model and an ultralocal scalar model, for which canonical quantization fails while enhanced quantization succeeds.Comment: 10 pages, several minor corrections, contribution to 2017 Coherent States workshop as a CIRM conference proceeding

A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets

Satellites enable widespread, regional or global surveillance of volcanoes and can provide the first indication of volcanic unrest or eruption. Here we consider Interferometric Synthetic Aperture Radar (InSAR), which can be employed to detect surface deformation with a strong statistical link to eruption. The ability of machine learning to automatically identify signals of interest in these large InSAR datasets has already been demonstrated, but data-driven techniques, such as convolutional neutral networks (CNN) require balanced training datasets of positive and negative signals to effectively differentiate between real deformation and noise. As only a small proportion of volcanoes are deforming and atmospheric noise is ubiquitous, the use of machine learning for detecting volcanic unrest is more challenging. In this paper, we address this problem using synthetic interferograms to train the AlexNet. The synthetic interferograms are composed of 3 parts: 1) deformation patterns based on a Monte Carlo selection of parameters for analytic forward models, 2) stratified atmospheric effects derived from weather models and 3) turbulent atmospheric effects based on statistical simulations of correlated noise. The AlexNet architecture trained with synthetic data outperforms that trained using real interferograms alone, based on classification accuracy and positive predictive value (PPV). However, the models used to generate the synthetic signals are a simplification of the natural processes, so we retrain the CNN with a combined dataset consisting of synthetic models and selected real examples, achieving a final PPV of 82%. Although applying atmospheric corrections to the entire dataset is computationally expensive, it is relatively simple to apply them to the small subset of positive results. This further improves the detection performance without a significant increase in computational burden

A cautionary tale of topography and tilt from Kīlauea Caldera

We conduct finite element analysis to investigate the effect of sharp topography on surface ground deformation caused by pressure changes in a magma reservoir. Tilt data expresses the horizontal gradient of vertical deformation and therefore can emphasise small variations in deformation that go unnoticed using other methods. We find that the vertical displacement profile at a surface with a cliff can be thought of as the superposition of the deformation from shallow and deeper sources. This combination can create a small peak in vertical displacement that acts as a pseudo‐source, creating a reversal of the deformation gradient and therefore anomalous tilt magnitude and a rotation of up to 180° . We apply these models to Kīlauea Caldera and find that surface geometry creates a tilt rotation of ∼10°, partially explaining anomalous tilt that has been observed. Our analysis highlights the importance of considering topography when assessing tilt measurements at active volcanoes

New perspectives on 'geological strain rates' calculated from both naturally deformed and actively deforming rocks

A value of ∼10−14 s−1 is commonly cited as an average geological strain rate. This value was first suggested for finite strain across an orogen, but based on more limited information than the combined geophysical, geological, and experimental data now available on active and ancient rock deformation. Thus, it is timely to review the data constraining strain rates in the continents, and to consider the quantifiable range of crustal strain rates. Here, where resolution allows, both spatial and temporal strain rate variations are explored. This review supports that a strain rate of 10−14±1 s−1 arises from geological estimates of bulk finite strains. Microstructural arguments combining laboratory-derived piezometers and viscous flow laws, however, imply local rates that are orders of magnitude faster. Geodetic rates, in contrast, are typically ∼10−15 s−1 in actively deforming areas, about an order of magnitude slower than the bulk rates estimated from geological observations. This difference in estimated strain rates may arise from either low spatial resolution, or the fact that surface velocity fields can not capture strain localisation in the mid to lower crust. Integration of geological and geodetic rates also shows that strain rates can vary in both space and time, over both single and multiple earthquake cycles. Overall, time-averaged geological strain rates are likely slower than the strain rates in faults and shear zones that traverse the crust or lithosphere