Uppermost mantle (Pn) velocity model for the Afar region, Ethiopia: an insight into rifting processes

The Afar Depression, Ethiopia, offers unique opportunities to study the transition from continental rifting to oceanic spreading because the process is occurring onland. Using traveltime tomography and data from a temporary seismic deployment, we describe the first regional study of uppermost mantle P-wave velocities (VPn). We find two separate low VPn zones (as low as 7.2 km s−1) beneath regions of localized thinned crust in northern Afar, indicating the existence of high temperatures and, potentially, partial melt. The zones are beneath and off-axis from, contemporary crustal magma intrusions in active magmatic segments, the Dabbahu-Manda-Hararo and Erta'Ale segments. This suggests that these intrusions can be fed by off-axis delivery of melt in the uppermost mantle and that discrete areas of mantle upwelling and partial melting, thought to characterize segmentation of the uppermost mantle at seafloor spreading centres, are initiated during the final stages of break-up

Velocity-space sensitivity of the time-of-flight neutron spectrometer at JET

The velocity-space sensitivities of fast-ion diagnostics are often described by so-called weight functions. Recently, we formulated weight functions showing the velocity-space sensitivity of the often dominant beam-target part of neutron energy spectra. These weight functions for neutron emission spectrometry (NES) are independent of the particular NES diagnostic. Here we apply these NES weight functions to the time-of-flight spectrometer TOFOR at JET. By taking the instrumental response function of TOFOR into account, we calculate time-of-flight NES weight functions that enable us to directly determine the velocity-space sensitivity of a given part of a measured time-of-flight spectrum from TOFOR

Relationship of edge localized mode burst times with divertor flux loop signal phase in JET

A phase relationship is identified between sequential edge localized modes (ELMs) occurrence times in a set of H-mode tokamak plasmas to the voltage measured in full flux azimuthal loops in the divertor region. We focus on plasmas in the Joint European Torus where a steady H-mode is sustained over several seconds, during which ELMs are observed in the Be II emission at the divertor. The ELMs analysed arise from intrinsic ELMing, in that there is no deliberate intent to control the ELMing process by external means. We use ELM timings derived from the Be II signal to perform direct time domain analysis of the full flux loop VLD2 and VLD3 signals, which provide a high cadence global measurement proportional to the voltage induced by changes in poloidal magnetic flux. Specifically, we examine how the time interval between pairs of successive ELMs is linked to the time-evolving phase of the full flux loop signals. Each ELM produces a clear early pulse in the full flux loop signals, whose peak time is used to condition our analysis. The arrival time of the following ELM, relative to this pulse, is found to fall into one of two categories: (i) prompt ELMs, which are directly paced by the initial response seen in the flux loop signals; and (ii) all other ELMs, which occur after the initial response of the full flux loop signals has decayed in amplitude. The times at which ELMs in category (ii) occur, relative to the first ELM of the pair, are clustered at times when the instantaneous phase of the full flux loop signal is close to its value at the time of the first ELM

DAS-N2N: machine learning distributed acoustic sensing (DAS) signal denoising without clean data

This paper presents a weakly supervised machine learning method, which we call DAS-N2N, for suppressing strong random noise in distributed acoustic sensing (DAS) recordings. DAS-N2N requires no manually produced labels (i.e. pre-determined examples of clean event signals or sections of noise) for training and aims to map random noise processes to a chosen summary statistic, such as the distribution mean, median or mode, whilst retaining the true underlying signal. This is achieved by splicing (joining together) two fibres hosted within a single optical cable, recording two noisy copies of the same underlying signal corrupted by different independent realizations of random observational noise. A deep learning model can then be trained using only these two noisy copies of the data to produce a near fully denoised copy. Once the model is trained, only noisy data from a single fibre is required. Using a data set from a DAS array deployed on the surface of the Rutford Ice Stream in Antarctica, we demonstrate that DAS-N2N greatly suppresses incoherent noise and enhances the signal-to-noise ratios (SNR) of natural microseismic icequake events. We further show that this approach is inherently more efficient and effective than standard stop/pass band and white noise (e.g. Wiener) filtering routines, as well as a comparable self-supervised learning method based on masking individual DAS channels. Our preferred model for this task is lightweight, processing 30 s of data recorded at a sampling frequency of 1000 Hz over 985 channels (approximately 1 km of fibre) in <1 s. Due to the high noise levels in DAS recordings, efficient data-driven denoising methods, such as DAS-N2N, will prove essential to time-critical DAS earthquake detection, particularly in the case of microseismic monitoring

Embodied conversational agents : computing and rendering realistic gaze patterns

We describe here our efforts for modeling multimodal signals exchanged by interlocutors when interacting face-to-face. This data is then used to control embodied conversational agents able to engage into a realistic face-to-face interaction with human partners. This paper focuses on the generation and rendering of realistic gaze patterns. The problems encountered and solutions proposed claim for a stronger coupling between research fields such as audiovisual signal processing, linguistics and psychosocial sciences for the sake of efficient and realistic human-computer interaction

Uppermost mantle velocity from Pn tomography in the Gulf of Aden

We determine the lateral variations in seismic velocity of the lithospheric mantle beneath the Gulf of Aden and its margins by inversion of Pn (upper mantle high-frequency compressional P wave) traveltimes. Data for this study were collected by several temporary seismic networks and from the global catalogue. A least-squares tomographic algorithm is used to solve for velocity variations in the mantle lithosphere. In order to separate shallow and deeper structures, we use separate inversions for shorter and longer ray path data. High Pn velocities (8.2–8.4 km/s) are observed in the uppermost mantle beneath Yemen that may be related to the presence of magmatic underplating of the volcanic margins of Aden and the Red Sea. Zones of low velocity (7.7 km/s) are present in the shallow upper mantle beneath Sana’a, Aden, Afar, and along the Gulf of Aden that are likely related to melt transport through the lithosphere feeding active volcanism. Deeper within the upper mantle, beneath the Oman margin, a low-velocity zone (7.8 km/s) suggests a deep zone of melt accumulation. Our results provide evidence that the asthenosphere undergoes channelized flow from the Afar hotspot toward the east along the Aden and Sheba Ridges