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This is the Utah FORGE well 16A(78)-32 stimulation microseismic detection and location report from Silixa LLC. The stimulation was accomplished during April, 2022.
Distributed temperature-sensing data collected using a Silixa-XT DTS and 50/125 MM fiber. Data are calibrated using two water baths at the surface and an iMet aloft at the end of the fiber and tether. The data are then assigned an altitude for the TBS flight. Data collected prior to Febuary 11, 2020 were collected at the SGP Central Facility (CF). Data from 2/11/20 were collected with a Sensornet Oryx DTS at the E9 site, not at the CF. Data from May 2021 were collected with a Sensornet Oryx at the CF, and with a Silixa XT at the CF. Data from July 2021 were collected at the SGP CF, EF9, and EF36 with a Silixa XT. Data from October 2021 were collected at the SGP EF36 with a Silixa XT. Data from February 2022 were collected at the CF with a Silixa XT.
Abstract Distributed acoustic sensing (DAS) has emerged as a promising seismic technology for monitoring microearthquakes (MEQs) with high spatial resolution. Efficient algorithms are needed for processing large DAS data volumes. This study introduces a deep learning (DL) model based on a Residual Convolutional Neural Network (ResNet) for detecting MEQs using DAS data, named as DASEventNet. The test data were collected from the Utah FORGE 16A (78)‐32 hydraulic stimulation experiments conducted in April 2022. The DASEventNet model achieves a remarkable accuracy of 100% when discriminating MEQs from noise in the raw test set of 260 examples. Surprisingly, the model identified weak MEQ signatures that have been manually categorized as noise. The decision‐making process with the model is decoded by the classic activation map, which illuminates learning features of the DASEventNet model. These features provide clear illustrations of weak MEQs and varied noise types. Finally, we apply the trained model to the entire period (∼7 days) of continuous DAS recordings and find that it discovers >5,700 new MEQs, previously unregistered in the public Silixa DAS catalog. The DASEventNet model significantly outperforms the traditional seismic method Short‐Term Average/Long‐Term Average (STA/LTA), which detected only 1,307 MEQs. The DASEventNet detection threshold is M w −1.80 compared to the minimum magnitude of M w −1.14 detected by STA/LTA. The spatiotemporal distribution of the newly identified MEQs defines an extensive stimulation zone and more accurately characterizes fracture geometry. Our results highlight the potential of DL for long‐term, real‐time microseismic monitoring that can improve enhanced geothermal systems and other activities that include subsurface hydraulic fracturing.
Distributed fiber optic sensing was an important part of the monitoring system for EGS Collab Experiment #2. A single loop of custom fiber package was grouted into the four monitoring boreholes that bracketed the experiment volume. This fiber package contained two multi-mode fibers and four single-mode fibers. These fibers were connected to an array of fiber optic interrogator units, each targeting a different measurement. The distributed temperature system (DTS) consisted of a Silixa XT-DTS unit, connected to both ends of one of the two multi-mode fibers. This system measured absolute temperature along the entire length of fiber for the duration of the experiment at a sampling rate of approximately 10 minutes. This dataset includes both raw data in XML format from the XT-DTS, as well as a processed dataset with the sections of data pertaining only to the boreholes are extracted. We have also included a report that provides all of the relevant details necessary for users to process and interpret the data for themselves. Please read this accompanying report. If, after reading it, there are still outstanding questions, please do not hesitate to contact us. Happy processing.
Cryosphere/Ocean Distributed Acoustic Sensing (CODAS) data collected from the Beaufort Sea, Alaska, using ~37.4 km of dark telecommunications fiber located at Oliktok Point, Alaska. Data were collected with a Silixa iDAS, using 10 m gauge length, 2 m spatial resolution, and 1000 Hz sample rate. Provided here are the DAS-recorded time series for the rapid refreeze event described in Baker & Abbott (2022) (see link below). This covers a date range of 2021-11-10 15:00 UTC to 2021-11-11 17:00 UTC. Data have been decimated to 100 Hz and 20 m (i.e., every 10th channel for 1831 channels, total), as used in Baker & Abbott (2022). Data have been extracted from raw format into 1-hour long .sac* files and organized into directories by channel number, spanning channels 100 to 18400. Time series units are nano-strainrate (nm/m/s). For distribution, data have been compressed into .zip files containing all time series files for 100 channels. *For information on the Seismic Analysis Code (SAC) file format: https://seiscode.iris.washington.edu/projects/sac
This study demonstrates the feasibility of using Distributed Acoustic Sensing (DAS) on the opportunistic optical fiber infrastructure presented by NEON (National Ecological Observatory Network) sites. The test took place at the NEON Terrestrial Site designated D10 CPER. Unlit portions of the local fiber network were successfully spliced together and optimized for length and a Silixa iDAS sensor was installed. The resulting array (composed of 12 spliced fiber runs) totaled 875m in length. Over a period of 2 weeks, we gathered a high spatial and temporal resolution data set to characterize discernable signals and understand background noise. The data set was reviewed to extract wave signatures of identifiable origin, both anthropogenic and otherwise. Continent wide distribution of these sites makes them an interesting candidate for DAS and identifying measurable signal sources informs future DAS application on opportunistic fiber networks. These observations validate the utility of both the NEON and similarly small fiber networks (<1km) for geoscience, environmental, and security DAS applications and indicate possibilities for further inquiry in methods for DAS data collection and analysis.
Distributed temperature-sensing data collected using a Silixa-XT DTS and 50/125 MM fiber. Data are calibrated using two water baths at the surface and an iMet aloft at the end of the fiber and tether. The data are then assigned an altitude for the TBS flight.
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On 24 September 2023, the Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer sample return capsule (SRC) entered Earth’s atmosphere after successfully collecting samples from an asteroid. The known trajectory and timing of this return provided a rare opportunity to strategically instrument sites to record geophysical signals produced by the capsule because it travelled at hypersonic speeds through the atmosphere. For this work, we deployed two distributed acoustic sensing (DAS) interrogators to sample over 12 km of surface-draped fiber-optic cables, as well as six collocated seismometer-infrasound sensor pairs, spread across two sites near Eureka, Nevada. This campaign-style rapid deployment is the first reported recording of an SRC re-entry with any distributed fiber-optic sensing technology. The DAS interrogators recorded an impulsive arrival with an extended coda which had features that were similar to recordings from both the seismometers and infrasound sensors. While the signal-to-noise ratio of the DAS data was lower than the seismic-infrasound data, the extremely dense spacing of fiber-optic sensors allowed for more phases to be clearly distinguished and the visualization of the continuous transformation of the wavefront as it impacted the ground. Unexpectedly, the DAS recordings contain less low-frequency content than is present in both the seismic and infrasound data. The deployment conditions strongly affected the recorded DAS data; in particular, we observed that fiber selection and placement exert strong controls on data quality.