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  1. Free, publicly-accessible full text available September 12, 2024
  2. Abstract

    The mantle transition zone connects two major layers of Earth’s interior that may be compositionally distinct: the upper mantle and the lower mantle. Wadsleyite is a major mineral in the upper mantle transition zone. Here, we measure the single-crystal elastic properties of hydrous Fe-bearing wadsleyite at high pressure-temperature conditions by Brillouin spectroscopy. Our results are then used to model the global distribution of wadsleyite proportion, temperature, and water content in the upper mantle transition zone by integrating mineral physics data with global seismic observations. Our models show that the upper mantle transition zone near subducted slabs is relatively cold, enriched in wadsleyite, and slightly more hydrated compared to regions where plumes are expected. This study provides direct evidence for the thermochemical heterogeneities in the upper mantle transition zone which is important for understanding the material exchange processes between the upper and lower mantle.

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  3. We study the open-domain named entity recognition (NER) prob- lem under distant supervision. The distant supervision, though does not require large amounts of manual annotations, yields highly in- complete and noisy distant labels via external knowledge bases. To address this challenge, we propose a new computational framework – BOND, which leverages the power of pre-trained language models (e.g., BERT and RoBERTa) to improve the prediction performance of NER models. Specifically, we propose a two-stage training algo- rithm: In the first stage, we adapt the pre-trained language model to the NER tasks using the distant labels, which can significantly improve the recall and precision; In the second stage, we drop the distant labels, and propose a self-training approach to further improve the model performance. Thorough experiments on 5 bench- mark datasets demonstrate the superiority of BOND over existing distantly supervised NER methods. The code and distantly labeled data have been released in 
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  4. Wasserstein distance plays increasingly important roles in machine learning, stochastic programming and image processing. Major efforts have been under way to address its high computational complexity, some leading to approximate or regularized variations such as Sinkhorn distance. However, as we will demonstrate, regularized variations with large regularization parameter will degradate the performance in several important machine learning applications, and small regularization parameter will fail due to numerical stability issues with existing algorithms. We address this challenge by developing an Inexact Proximal point method for exact Optimal Transport problem (IPOT) with the proximal operator approximately evaluated at each iteration using projections to the probability simplex. The algorithm (a) converges to exact Wasserstein distance with theoretical guarantee and robust regularization parameter selection, (b) alleviates numerical stability issue, (c) has similar computational complexity to Sinkhorn, and (d) avoids the shrinking problem when apply to generative models. Furthermore, a new algorithm is proposed based on IPOT to obtain sharper Wasserstein barycenter. 
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  5. Abstract

    Explosions and earthquakes are effectively discriminated byP/Samplitude ratios for moderate magnitude events (M ≥ 4) observed at regional to teleseismic distances (≥200 km). It is less clear ifP/Sratios are effective explosion discriminants for lower magnitudes observed at shorter distances. We report new tests ofP/Sdiscrimination using a dense seismic array in a continental volcanic arc setting near Mount St. Helens, with 23 single‐fired borehole explosions (ML0.9–2.3) and 406 earthquakes (ML1–3.3). The array provides up to 95 three‐component broadband seismographs, and most source‐receiver distances are <120 km. Additional insight is provided by ~3,000 vertical component geophone recordings of each explosion. Potential controls on local distanceP/Sratios are investigated, including frequency range, distance, magnitude, source depth, number of seismographs, and site effects. A frequency band of about 10–18 Hz performs better than lower or narrower bands because explosion‐inducedSwave amplitudes diminish relative toPfor higher frequencies. Source depth and magnitude exhibited weak influences onP/Sratios. Site responses for earthquakes and explosions are correlated with each other and with shallow crustalVpandVsfrom traveltime tomography. Overall, the results indicate high potential for local distanceP/Sexplosion discrimination in a continental volcanic arc setting, with ≥98% true positives and ≤6.3% false positives when using the array median from ≥16 stations. Performance is reduced for smaller arrays, especially those with ≤4 stations, thereby emphasizing the importance of array data for discrimination of low magnitude explosions.

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  6. Abstract

    Classification of local‐distance, low‐magnitude seismic events is challenging because signals can be numerous and difficult to characterize with approaches developed for larger magnitude events observed at greater distances. Yet, accurate classification is important to studies of earthquake processes and detection of potential underground nuclear tests. Here, we combine two classification metrics: the three‐component ratio of high‐frequency P/S amplitudes and the difference between local and coda duration magnitudes (ML‐MC). The metrics use different parts of the high‐frequency wavefield and exhibit complementary sensitivity for classification ofM∼ 0.5–4 natural earthquakes and borehole explosions, which are the best analog for underground nuclear explosions. Using means from bootstrap resampling across four diverse geologic settings, joint classification achieves >94.4% true positives and <8.4% false positives when using8 seismographs within 200 km of the source. This high performance is obtained without local site corrections, indicating that the method may be transportable for local event classification.

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  7. Abstract

    Seismicity in the Raton Basin over the past two decades suggests reactivation of basement faults due to waste‐water injection. In the summer of 2018, 96 short period three‐component nodal instruments were installed in a highly active region of the basin for a month. A machine‐learning based phase picker (PhaseNet) was adopted and identified millions of picks, which were associated into events using an automated algorithm—REAL (Rapid Earthquake Association and Location). After hypocenter relocation with hypoDD, the earthquake catalog contains 9,259 ML−2.2 to 3 earthquakes focused at depths of 4–6 km. Magnitude of completeness (Mc) varies from −1 at nighttime to −0.5 in daytime, likely reflecting noise variation modulated by wind. The clustered hypocenters with variable depths and focal mechanisms suggest a complex network of basement faults. Frequency‐magnitude statistics and the spatiotemporal evolution of seismicity are comparable to tectonic systems.

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