Seismic interferometry provides insights into in situ reservoir response to stress changes.

We present two new seismic velocity models for Alaska from joint inversions of body-wave and ambient-noise-derived surface-wave data, using two different methods. Our work takes advantage of data from many recent temporary seismic networks, including the Incorporated Research Institutions for Seismology Alaska Transportable Array, Southern Alaska Lithosphere and Mantle Observation Network, and onshore stations of the Alaska Amphibious Community Seismic Experiment. The first model primarily covers south-central Alaska and uses body-wave arrival times with Rayleigh-wave group-velocity maps accounting for their period-dependent lateral sensitivity. The second model results from direct inversion of body-wave arrival times and surface-wave phase travel times, and covers the entire state of Alaska. The two models provide 3D compressional- (VP) and shear-wave velocity (VS) information at depths ∼0–100  km. There are many similarities as well as differences between the two models. The first model provides a clear image of the high-velocity subducting plate and the low-velocity mantle wedge, in terms of the seismic velocities and the VP/VS ratio. The statewide model provides clearer images of many features such as sedimentary basins, a high-velocity anomaly in the mantle wedge under the Denali volcanic gap, low VP in the lower crust under Brooks Range, and low velocities at the eastern edge of Yakutat terrane under the Wrangell volcanic field. From simultaneously relocated earthquakes, we also find that the depth to the subducting Pacific plate beneath southern Alaska appears to be deeper than previous models.

The 30 November 2018 magnitude 7.1 Anchorage earthquake occurred as a result of normal faulting within the lithosphere of subducted Yakutat slab. It was followed by a vigorous aftershock sequence with over 10,000 aftershocks reported through the end of July 2019. The Alaska Earthquake Center produced a reviewed aftershock catalog with a magnitude of completeness of 1.3. This well‐recorded dataset provides a rare opportunity to study the relationship between the aftershocks and fault rupture of a major intraslab event. We use tomoDD algorithm to relocate 2038 M≥2 aftershocks with a regional 3D velocity model. The relocated aftershocks extend over a 20 km long zone between 47 and 57 km depth and are primarily confined to a high VP/VS region. Aftershocks form two clusters, a diffuse southern cluster and a steeply west‐dipping northern cluster with a gap in between where maximum slip has been inferred. We compute moment tensors for the Mw>4 aftershocks using a cut‐and‐paste method and careful selection of regional broadband stations. The moment tensor solutions do not exhibit significant variability or systematic differences between the northern and southern clusters and, on average, agree well with the mainshock fault‐plane parameters. We propose that the mainshock rupture initiated in the Yakutat lower crust or uppermost mantle and propagated both upward into the crust to near its top and downward into the mantle. The majority of the aftershocks are confined to the seismically active Yakutat crust and located both on and in the hanging wall of the mainshock fault rupture.

Deep learning recommendation models (DLRMs) have been used across many business-critical services at Meta and are the single largest AI application in terms of infrastructure demand in its data-centers. In this paper, we present Neo, a software-hardware co-designed system for high-performance distributed training of large-scale DLRMs. Neo employs a novel 4D parallelism strategy that combines table-wise, row-wise, column-wise, and data parallelism for training massive embedding operators in DLRMs. In addition, Neo enables extremely high-performance and memoryefficient embedding computations using a variety of critical systems optimizations, including hybrid kernel fusion, software-managed caching, and quality-preserving compression. Finally, Neo is paired with ZionEX , a new hardware platform co-designed with Neo's 4D parallelism for optimizing communications for large-scale DLRM training. Our evaluation on 128 GPUs using 16 ZionEX nodes shows that Neo outperforms existing systems by up to 40× for training 12-trillion-parameter DLRM models deployed in production.

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The intrinsic array nature of distributed acoustic sensing (DAS) makes it suitable for applying beamforming techniques commonly used in traditional seismometer arrays for enhancing weak and coherent seismic phases from distant seismic events. We test the capacity of a dark-fiber DAS array in the Sacramento basin, northern California, to detect small earthquakes at The Geysers geothermal field, at a distance of ∼100  km from the DAS array, using beamforming. We use a slowness range appropriate for ∼0.5–1.0  Hz surface waves that are well recorded by the DAS array. To take advantage of the large aperture, we divide the ∼20  km DAS cable into eight subarrays of aperture ∼1.5–2.0  km each, and apply beamforming independently to each subarray using phase-weighted stacking. The presence of subarrays of different orientations provides some sensitivity to back azimuth. We apply a short-term average/long-term average detector to the beam at each subarray. Simultaneous detections over multiple subarrays, evaluated using a voting scheme, are inferred to be caused by the same earthquake, whereas false detections caused by anthropogenic noise are expected to be localized to one or two subarrays. Analyzing 45 days of continuous DAS data, we were able to detect all earthquakes with M≥2.4, while missing most of the smaller magnitude earthquakes, with no false detections due to seismic noise. In comparison, a single broadband seismometer co-located with the DAS array was unable to detect any earthquake of M<2.4, many of which were detected successfully by the DAS array. The seismometer also experienced a large number of false detections caused by spatially localized noise. We demonstrate that DAS has significant potential for local and regional detection of small seismic events using beamforming. The ubiquitous presence of dark fiber provides opportunities to extend remote earthquake monitoring to sparsely instrumented and urban areas.

The Imperial Valley, CA, is a tectonically active transtensional basin located south of the Salton Sea; the area hosts numerous geothermal fields, including significant hidden hydrothermal resources without surface manifestations. Development of inexpensive, rugged, and highly sensitive exploration techniques for undiscovered geothermal systems is critical for accelerating geothermal power deployment as well as unlocking a low‐carbon energy future. We present a case study utilizing distributed acoustic sensing (DAS) and ambient noise interferometry for geothermal reservoir imaging, utilizing unlit fiber‐optic telecommunication infrastructure (dark fiber). The study exploits two days of passive DAS data acquired in early November 2020 over a ∼28‐km section of fiber from Calipatria, CA to Imperial, CA. We apply ambient noise interferometry to retrieve coherent signals from DAS records and develop a bin stacking technique to attenuate the effects from persistent localized noise sources and to enhance retrieval of coherent surface waves. As a result, we are able to obtain high‐resolution two‐dimensional (2D) S wave velocity (Vs) structure to 3 km depth, based on joint inversion of both the fundamental and higher overtones. We observe a previously unmapped high Vs and low Vp/Vs ratio feature beneath the Brawley geothermal system, which we interpret to be a zone of hydrothermal mineralization and lower porosity. This interpretation is consistent with a host of other measurements including surface heat flow, gravity anomalies, and available borehole wireline data. These results demonstrate the potential utility of DAS deployed on dark fiber for geothermal system exploration and characterization in the appropriate geological settings.

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