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Continuous geodetic measurements near volcanic systems can image magma transport dynamics, yet resolving dike intrusions with high spatiotemporal resolution remains challenging. We introduce fiber-optic geodesy, leveraging low-frequency distributed acoustic sensing (LFDAS) recordings along a telecommunication fiber-optic cable, to track dike intrusions near Grindavík, Iceland, on a minute timescale. LFDAS reveals distinct strain responses from nine intrusive events, six resulting in fissure eruptions. Geodetic inversion of LFDAS strain reveals detailed magmatic intrusions, with inferred dike volume rate peaking systematically 15 to 22 min before the onset of each eruption. Our results demonstrate DAS’s potential for a dense strainmeter array, enabling high-resolution, nearly real-time imaging of subsurface quasi-static deformations. In active volcanic regions, LFDAS recordings can offer critical insights into magmatic evolution, eruption forecasting, and hazard assessment. 

Geophysical characterization of calderas is fundamental in assessing their potential for future catastrophic volcanic eruptions. The mechanism behind the unrest of Long Valley Caldera in California remains highly debated, with recent periods of uplift and seismicity driven either by the release of aqueous fluids from the magma chamber or by the intrusion of magma into the upper crust. We use distributed acoustic sensing data recorded along a 100-kilometer fiber-optic cable traversing the caldera to image its subsurface structure. Our images highlight a definite separation between the shallow hydrothermal system and the large magma chamber located at ~12-kilometer depth. The combination of the geological evidence with our results shows how fluids exsolved through second boiling provide the source of the observed uplift and seismicity. 

Abstract Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake monitoring and subsurface imaging. However, its distinct characteristics, such as unknown ground coupling and high noise level, pose challenges to signal processing. Existing machine learning models optimized for conventional seismic data struggle with DAS data due to its ultra-dense spatial sampling and limited manual labels. We introduce a semi-supervised learning approach to address the phase-picking task of DAS data. We use the pre-trained PhaseNet model to generate noisy labels of P/S arrivals in DAS data and apply the Gaussian mixture model phase association (GaMMA) method to refine these noisy labels and build training datasets. We develop PhaseNet-DAS, a deep learning model designed to process 2D spatio-temporal DAS data to achieve accurate phase picking and efficient earthquake detection. Our study demonstrates a method to develop deep learning models for DAS data, unlocking the potential of integrating DAS in enhancing earthquake monitoring. 

Abstract Distributed Acoustic Sensing (DAS) is an emerging technology that converts optical fibers into dense arrays of strainmeters, significantly enhancing our understanding of earthquake physics and Earth's structure. While most past DAS studies have focused primarily on seismic wave phase information, accurate measurements of true ground motion amplitudes are crucial for comprehensive future analyses. However, amplitudes in DAS recordings, especially for pre‐existing telecommunication cables with uncertain fiber‐ground coupling, have not been fully quantified. By calibrating three DAS arrays with co‐located seismometers, we systematically evaluate DAS amplitudes. Our results indicate that the average DAS amplitude of earthquake signals closely matches that of co‐located seismometer data across frequencies from 0.01 to 10 Hz. The noise floor of DAS is comparable to that of strong‐motion stations but higher than that of broadband stations. The saturation amplitude of DAS is adjustable by modifying the pulse repetition rate and gauge length. We also demonstrate how our findings enhance the understanding of fiber‐optic seismology and its implications for natural hazard mitigation and Earth structure imaging and monitoring. Specifically, our results suggest that with proper settings, DAS can detectP‐waves from an M6+ earthquake occurring 10 km from the cable without saturation, indicating its viability for earthquake early warning. Through quantitative comparison and analysis, we also find that local ambient traffic noise levels strongly affect the quality of seismic interferometry measurement, which is a powerful tool for near‐surface imaging and monitoring. Our methodology and findings are valuable for future DAS experiments that require precise seismic amplitude measurements. 

Abstract Earthquake focal mechanisms provide critical in-situ insights about the subsurface faulting geometry and stress state. For frequent small earthquakes (magnitude< 3.5), their focal mechanisms are routinely determined using first-arrival polarities picked on the vertical component of seismometers. Nevertheless, their quality is usually limited by the azimuthal coverage of the local seismic network. The emerging distributed acoustic sensing (DAS) technology, which can convert pre-existing telecommunication cables into arrays of strain/strain-rate meters, can potentially fill the azimuthal gap and enhance constraints on the nodal plane orientation through its long sensing range and dense spatial sampling. However, determining first-arrival polarities on DAS is challenging due to its single-component sensing and low signal-to-noise ratio for direct body waves. Here, we present a data-driven method that measures P-wave polarities on a DAS array based on cross-correlations between earthquake pairs. We validate the inferred polarities using the regional network catalog on two DAS arrays, deployed in California and each comprising ~ 5000 channels. We demonstrate that a joint focal mechanism inversion combining conventional and DAS polarity picks improves the accuracy and reduces the uncertainty in the focal plane orientation. Our results highlight the significant potential of integrating DAS with conventional networks for investigating high-resolution earthquake source mechanisms. 

With the spreading of hate speech on social media in recent years, automatic detection of hate speech is becoming a crucial task and has attracted attention from various communities. This task aims to recognize online posts (e.g., tweets) that contain hateful information. The peculiarities of languages in social media, such as short and poorly written content, lead to the difficulty of learning semantics and capturing discriminative features of hate speech. Previous studies have utilized additional useful resources, such as sentiment hashtags, to improve the performance of hate speech detection. Hashtags are added as input features serving either as sentiment-lexicons or extra context information. However, our close investigation shows that directly leveraging these features without considering their context may introduce noise to classifiers. In this paper, we propose a novel approach to leverage sentiment hashtags to enhance hate speech detection in a natural language inference framework. We design a novel framework SRIC that simultaneously performs two tasks: (1) semantic relation inference between online posts and sentiment hashtags, and (2) sentiment classification on these posts. The semantic relation inference aims to encourage the model to encode sentiment-indicative information into representations of online posts. We conduct extensive experiments on two real-world datasets and demonstrate the effectiveness of our proposed framework compared with state-of-the-art representation learning models. 

Abstract Isolated dwarf galaxies usually exhibit robust star formation but satellite dwarf galaxies are often devoid of young stars, even in Milky Way–mass groups. Dwarf galaxies thus offer an important laboratory of the environmental processes that cease star formation. We explore the balance of quiescent and star-forming galaxies (quenched fractions) for a sample of ∼400 satellite galaxies around 30 Local Volume hosts from the Exploration of Local VolumE Satellites (ELVES) Survey. We present quenched fractions as a function of satellite stellar mass, projected radius, and host halo mass, to conclude that overall, the quenched fractions are similar to the Milky Way, dropping below 50% at satelliteM_*≈ 10⁸M_⊙. We may see hints that quenching is less efficient at larger radii. Through comparison with the semianalytic modeling codeSatGen, we are also able to infer average quenching times as a function of satellite mass in host halo-mass bins. There is a gradual increase in quenching time with satellite stellar mass rather than the abrupt change from rapid to slow quenching that has been inferred for the Milky Way. We also generally infer longer average quenching times than recent hydrodynamical simulations. Our results are consistent with models that suggest a wide range of quenching times are possible via ram pressure stripping, depending on the clumpiness of the circumgalactic medium, the orbits of the satellites, and the degree of earlier preprocessing. 

Abstract Complex shear wave splitting (SWS) patterns in subduction zones are often interpreted geodynamically as resulting from complex mantle flow; however, this may not always be necessary. We analyzed 7,093 high‐quality SWS measurements from teleseismic S waves recorded by Hi‐net stations across the Ryukyu arc in Japan. Our findings show a systematic rotation of the fast S polarization from trench‐parallel to trench‐perpendicular depending on the earthquake backazimuth. For the same earthquake, the measured splitting patterns also vary spatially across the southwest Japan. Using full‐wave seismic modeling, we showed that a dipping slab with ∼30% shear anisotropy of the tilted transverse isotropy (TTI) type, with a symmetry axis perpendicular to the slab interface, can predict the observed delay times and polarization rotation. Our results highlight the importance of considering dipping anisotropic slabs in interpreting SWS at subduction zones. 

ABSTRACT The Merian survey is mapping ∼ 850 deg2 of the Hyper Suprime-Cam Strategic Survey Program (HSC-SSP) wide layer with two medium-band filters on the 4-m Victor M. Blanco telescope at the Cerro Tololo Inter-American Observatory, with the goal of carrying the first high signal-to-noise (S/N) measurements of weak gravitational lensing around dwarf galaxies. This paper presents the design of the Merian filter set: N708 (λc = 7080 Å, Δλ = 275 Å) and N540 (λc = 5400 Å, Δλ = 210 Å). The central wavelengths and filter widths of N708 and N540 were designed to detect the $$\rm H\alpha$$ and $$\rm [OIII]$$ emission lines of galaxies in the mass range $$8\lt \rm \log M_*/M_\odot \lt 9$$ by comparing Merian fluxes with HSC broad-band fluxes. Our filter design takes into account the weak lensing S/N and photometric redshift performance. Our simulations predict that Merian will yield a sample of ∼ 85 000 star-forming dwarf galaxies with a photometric redshift accuracy of σΔz/(1 + z) ∼ 0.01 and an outlier fraction of $$\eta =2.8~{{\ \rm per\ cent}}$$ over the redshift range 0.058 < z < 0.10. With 60 full nights on the Blanco/Dark Energy Camera (DECam), the Merian survey is predicted to measure the average weak lensing profile around dwarf galaxies with lensing S/N ∼32 within r < 0.5 Mpc and lensing S/N ∼90 within r < 1.0 Mpc. This unprecedented sample of star-forming dwarf galaxies will allow for studies of the interplay between dark matter and stellar feedback and their roles in the evolution of dwarf galaxies.