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Distributed quantum sensing with mode-entangled spin-squeezed atomic states
Not provided.
The Value of Information From Horizontal Distributed Acoustic Sensing Compared to Multicomponent Geophones Via Machine Learning
Abstract Faults play an important role in recharging many geothermal reservoirs, and seismic information can image the locations of these faults. The value of information (VOI) metric is used to objectively quantify and compare the value of two types of seismic receiver data via a machine learning approach. The demonstrated VOI methodology is novel by including spatial models from seismic data and obtaining the information statistics from machine learning. Our two-dimensional numerical experiments compare images created from sparsely spaced (80 m), two-component geophone sampling to high spatial resolution (1 m), single-component DAS. We used a three-fold cross validation of a U-Net convolutional neural networks to achieve average classification statistics. The results suggest that when horizontal sources are utilized, geophones and DAS identify reflectors and non-reflectors at roughly the same rate. The average F1 score for horizontal DAS is 0.939 and 0.931 for geophones. For images created from a vertical source, DAS performed marginally better (F1 = 0.919) than geophones (F1 = 0.877). Our transferrable methodology can provide guidance on which acquisition scenarios can improve images of important structures in the subsurface and present an efficient method for obtaining reliability statistics from high-dimensional, spatial data.
Raman Scattering in Single Crystal YAG for Application in Distributed Temperature Sensing
SPIE Photonics West 2021 (Conference), Virtual, March 6-11, 2021
Development of a Single Crystal Probe for Raman Distributed Temperature Sensing above 1000°C
SPIE Photonics West On Demand, Virtual, February 21-27, 2022
Improvement of Signal-to-Noise Ratio for Raman Distributed Temperature Sensing on Gas Turbines
SPIE Defense and Commercial Sensing, Orlando, FL, April 30-May 4, 2023
Prospects for Distributed Acoustic Sensing of Polar Environmental Processes : Initial Results from the Beaufort Sea Alaska.
Abstract not provided.
Distributed acoustic sensing of seasonally variable environmental processes in the Beaufort Sea, Alaska.
Abstract not provided.
Strain Measurements of Nb3Sn Composites using Distributed Fiber Sensing
Utilized fiber optic chords to measure strain in composite material and derive mechanical properties while validating the feasibility of the fiber optic sensors.
Rapid formation of pack ice in shallow coastal waters, as observed by seafloor distributed acoustic sensing.
Abstract not provided.
Automated High-Resolution Tracking of Sea Ice Extent Offshore Oliktok Point, Alaska, using Distributed Acoustic Sensing and Machine Learning .
Abstract not provided.
Estimation of First-Year Sea Ice Thickness with Seafloor Distributed Acoustic Sensing.
Abstract not provided.
Effects on Signal Propagation Across the Transition Between Dissimilar Optical Fibers for Distributed Acoustic Sensing Analysis.
Abstract not provided.
Distributed Acoustic Sensing as a Monitoring Tool at LANL [Slides]
DAS records a variety of signals, including (1) earthquakes and volcanic eruptions, (2) explosions (DAG, mining, etc.), (3) acoustic waves, (4) ocean (waves, whales), (5) vehicle, ship and pedestrian traffic, (6) glaciology and landslides, and (7) oil and gas. Main questions include: (1) what is the extent of the signal content? (2) how does DAS compare to traditional sensors? (3) how do interrogators, fiber types, and fiber depth burials impact measurements? (4) special applications offshore to overcome the lack of instrumentation.
Probing the Solid Earth and the Hydrosphere with Ocean-Bottom Distributed Acoustic Sensing [Slides]
Abstract not provided.