To gain a deeper understanding of the extensive and varied lithospheric deformations beneath northern Oman, we examine seismic anisotropy in this region using splitting analysis of teleseismic shear wave data. Our study utilizes data from a dense network consisting of 13 permanent and 45 temporary seismic stations, which were operational for approximately 2.5 years starting from 2013. By examining the azimuthal distribution of shear wave splitting (SWS) parameters, we are able to divide the study area into three sub-regions. The stations located to the west of the Hawasina window exhibit relatively azimuthally invariant SWS parameters suggesting a single anisotropic layer. On the other hand, most of the stations located in the central and eastern regions display a 90-degree periodicity versus back-azimuth, indicating the presence of depth-dependent anisotropy. The General NW-SE trend of the Fast Polarization Directions (FPDs), one-layer/upper layer FPDs in the east and one-layer FPDs in the west, is concordant with the strike of the structures resulting from the collision between the continental and oceanic plates. Notably, a distinct contrast in the SWS parameters is observed at Semail Gap Fault Zone (SGFZ), suggesting that the SGFZ can be a geological border for the mafic intrusive emplacement from the east. Furthermore, the fast axes of the lower layer exhibit an NE-SW trend, which may be indicative of the large-scale mantle flow resulting from the present-day plate motion.

Teleseismic shear-wave splitting analyses are typically performed by reversing the splitting process through the application of frequency- or time-domain operations minimizing transverse-component waveforms. These operations yield two splitting parameters, φ (fast-axis orientation) and δt (delay time). In this study, we investigate the applicability of a recurrent neural network, SWSNet, for determining the splitting parameters from pre-selected waveform windows. Due to the scarcity of sufficiently labelled real waveform data, we generate our own synthetic training dataset. The model is capable of determining φ and δt with a root mean squared error (RMSE) of 9.58◦ and 0.143 s for noisy synthetic test data. The application to real data involves a deconvolution step to homogenize the waveforms. When applied to data from the USArray dataset, the results exhibit similar patterns to those found in previous studies with mean absolute differences of 11.12◦ and 0.25 s in the calculation of φ and δt, respectively.

The presence of the Etendeka flood basalts in northwestern Namibia is taken as evidence for the activity of the Tristan da Cunha mantle plume during the breakup process between Africa and South America. We investigate seismic anisotropy beneath NW Namibia by splitting analysis of core-refracted teleseismic shear waves (XKS phases) to probe mantle flow and lithospheric deformation related to the tectonic history of the region. The waveform data were obtained from 34 onshore stations and 12 Ocean Bottom Seismometers. The results presented here are from joint splitting analysis of multiple XKS phases. The majority of the fast polarization directions (FPDs) exhibit an NE-SW orientation consistent with a model of large-scale mantle flow due to the NE motion of the African plate. No evidence for a direct effect of the mantle plume is observed. In the northern part, we observe NNW-SSE-oriented FPDs that is likely caused by shallow lithospheric structures.

The detection and rapid characterisation of earthquake parameters such as magnitude are important in real time seismological applications such as Earthquake Monitoring and Earthquake Early Warning (EEW). Traditional methods, aside from requiring extensive human involvement can be sensitive to signal-to-noise ratio leading to false/missed alarms depending on the threshold. We here propose a multi-tasking deep learning model – the Convolutional Recurrent model for Earthquake Identification and Magnitude Estimation (CREIME) that: (i) detects the earthquake signal from background seismic noise, (ii) determines the first P-wave arrival time and (iii) estimates the magnitude using the raw 3-component waveforms from a single station as model input. Considering, that speed is essential in EEW, we use up to two seconds of P-wave information which, to the best of our knowledge, is a significantly smaller data window compared to the previous studies. To examine the robustness of CREIME we test it on two independent datasets and find that it achieves an average accuracy of 98\% for event-vs-noise discrimination and can estimate first P-arrival time and local magnitude with average root mean squared errors of 0.13 seconds and 0.65 units, respectively. We compare CREIME with traditional methods such as short-term-average/ long-term-average (STA/LTA) and show that CREIME has superior performance, for example, the accuracy for signal and noise discrimination is higher by 4.5\% and 11.5\% respectively for the two datasets. We also compare the architecture of CREIME with the architectures of other baseline models, trained on the same data, and show that CREIME outperforms the baseline models.

Continental rifting is a critical component of the plate tectonic paradigm, and occurs in more than one mode, phase, or stage. While rifting is typically facilitated by abundant magmatism, some rifting is not. We aim to develop a better understanding of the fundamental processes associated with magma-poor (dry) rifting. Here, we provide an overview of the NSF-funded Dry Rifting In the Albertine-Rhino graben (DRIAR) project, Uganda. The project goal is to apply geophysical, geological, geochemical, and geodynamic techniques to investigate the Northern Western Branch of the East African Rift System in Uganda. We test three hypotheses: (1) in magma-rich rifts, strain is accommodated through lithospheric weakening from melt, (2) in magma-poor rifts, melt is present below the surface and weakens the lithosphere such that strain is accommodated during upper crustal extension, and (3) in magma-poor rifts, there is no melt at depth and strain is accommodated along pre-existing structures such as inherited compositional, structural, and rheological lithospheric heterogeneities. Observational methods in this project include: passive seismic to constrain lithospheric structure and asthenospheric flow patterns; gravity to constrain variations in crustal and lithospheric thickness; magnetics to constrain the thermal structure of the upper crust; magnetotellurics to constrain lithospheric thickness and the presence of melt; GNSS to constrain surface motions, extension rates, and help characterize mantle flow; geologic mapping to document the geometry and kinematics of active faults; seismic reflection analyses of intra-rift faults to document temporal strain migration; geochemistry to identify and quantify mantle-derived fluids in hot springs and soil gases; and geodynamic modeling to develop new models of magma-poor rifting processes. Fieldwork will begin in January 2022 and the first DRIAR field school is planned for summer 2022. Geodynamic modeling work and morphometric analyses are already underway.

The polarity of first P-wave arrivals plays a significant role in the effective determination of focal mechanisms specially for smaller earthquakes. Manual estimation of polarities is not only time-consuming but also prone to human errors. This warrants a need for an automated algorithm for first motion polarity determination. We present a deep learning model - PolarCAP that uses an autoencoder architecture to identify first-motion polarities of earthquake waveforms. PolarCAP is trained in a supervised fashion using more than 130,000 labelled traces from the Italian seismic dataset (INSTANCE) and is cross-validated on $\sim$22,000 traces to choose the most optimal set of hyperparameters. We obtain an accuracy of 0.98 on a completely unseen test dataset of almost 33,000 traces. Furthermore, We check the model generalizability by testing it on the datasets provided by previous works and show that our model achieves a higher recall on both positive and negative polarities.