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The application of optical fibers for assessing cemented wellbore’s integrity attracted considerable attention recently, because of low cost, decent temporal/spatial resolution and absence of downhole electronics. This study presents an integrated approach to compare measurements from distributed temperature sensing (DTS), distributed strain sensing (DSS) and fiber Bragg grating (FBG), at different stages of the wellbore cementation at Bedretto Underground Laboratory for Geosciences and Geoenergies. Before the cementation, the measurements from DTS provided information about the hydrogeological settings of the wellbore, including the major flow zones, and presence of a highly conductive hydraulic shortcut to a nearby wellbore. During the cement injection, the temperature sensors (DTS and temperature FBG) clearly detected the evolution of the top of the cement. While the mechanical deformation sensors (DSS and strain FBG) did not provide significant insights during this stage, their role became more pronounced in subsequent phases. Results show that the irregularities on the wall have minor influence on the thermo-mechanical response of the wellbore, both during and after cementation. After cementation, the temperature sensors (DTS and temperature FBG) traced different phases of cement-hardening process, while DSS measurements identified areas of major deformation, primarily in fracture/fault zones. It was also observed that localized elevation of temperature and extensional deformation along the wellbore during the cement-hardening are correlated with the presence of permeable structures, most likely due to continuous supply of water. Results of this study show that monitoring of the cemented wellbores using optical fibers, in particular during cement hardening, not only can be used to efficiently assess the wellbore integrity but also can provide us additional important information about the hydrogeological settings of the target reservoir volume.

 

Distributed acoustic sensing (DAS) technology is an emerging field of seismic sensing that enables recording ambient noise seismic data along the entire length of a fiber-optic cable at meter-scale resolution. Such a dense spatial resolution of recordings over long distances has not been possible using traditional methods because of limited hardware resources and logistical concerns in an urban environment. The low spatial resolution of traditional passive seismic acquisition techniques has limited the accuracy of the previously generated velocity profiles in many important urban regions, including the Reno-area basin, to the top 100 m of the underlying subsurface. Applying the method of seismic interferometry to ambient noise strain rate data obtained from a dark-fiber cable allows for generating noise cross correlations, which can be used to infer shallow and deep subsurface properties and basin geometry. We gathered DAS ambient noise seismic data for this study using a 12 km portion of a dark-fiber line in Reno, Nevada. We used gathered data to generate and invert dispersion curves to estimate the near-surface shear-wave velocity structure. Comparing the generated velocity profiles with previous regional studies shows good agreement in determining the average depth to bedrock and velocity variations in the analyzed domain. A synthetic experiment is also performed to verify the proposed framework further and better understand the effect of the infrastructural cover along the cable. The results obtained from this research provide insight into the application of DAS using dark-fiber lines in subsurface characterization in urban environments. It also discusses the potential effects of the conduit that covers such permanent fiber installations on the produced inversion results.

 

Over the past 50 years, the discovery and initial investigation of subglacial lakes in Antarctica have highlighted the paleoglaciological information that may be recorded in sediments at their beds. In December 2018, we accessed Mercer Subglacial Lake, West Antarctica, and recovered the first in situ subglacial lake-sediment record—120 mm of finely laminated mud. We combined geophysical observations, image analysis, and quantitative stratigraphy techniques to estimate long-term mean lake sedimentation rates (SRs) between 0.49 ± 0.12 mm a–1 and 2.3 ± 0.2 mm a–1, with a most likely SR of 0.68 ± 0.08 mm a–1. These estimates suggest that this lake formed between 53 and 260 a before core recovery (BCR), with a most likely age of 180 ± 20 a BCR—coincident with the stagnation of the nearby Kamb Ice Stream. Our work demonstrates that interconnected subglacial lake systems are fundamentally linked to larger-scale ice dynamics and highlights that subglacial sediment archives contain powerful, century-scale records of ice history and provide a modern process-based analogue for interpreting paleo–subglacial lake facies.

 

Hyperspectral imaging allows for rapid, non-destructive and objective assessments of crop health. Narrowband-hyperspectral data was used to select wavelength regions that can be exploited to identify wheat infected with soil-borne mosaic virus. First, leaf samples were scanned in the lab to investigate spectral differences between healthy and diseased leaves, including non-symptomatic and symptomatic areas within a diseased leaf. The potential of 84 commonly used vegetation indices to find infection was explored. A machine-learning approach was used to create a classification model to automatically separate pixels into symptomatic, non-symptomatic and healthy classes. The success rate of the model was 69.7% using the full spectrum. It was very encouraging that by using a subset of only four broad bands, sampled to simulate a data set from a much simpler and less costly multispectral camera, accuracy increased to 71.3%. Next, the classification models were validated on field data. Infection in the field was successfully identified using classifiers trained on the entire spectrum of the hyperspectral data acquired in a lab setting, with the best accuracy being 64.9%. Using a subset of wavelengths, simulating multispectral data, the accuracy dropped by only 3 percentage points to 61.9%. This research shows the potential of using lab scans to train classifiers to be successfully applied in the field, even when simultaneously reducing the hyperspectral data to multispectral data.

 

Raman-based distributed temperature sensing (DTS) is a valuable tool for field testing and validating heat transfer models in borehole heat exchanger (BHE) and ground source heat pump (GSHP) applications. However, temperature uncertainty is rarely reported in the literature. In this paper, a new calibration method was proposed for single-ended DTS configurations, along with a method to remove fictitious temperature drifts due to ambient air variations. The methods were implemented for a distributed thermal response test (DTRT) case study in an 800 m deep coaxial BHE. The results show that the calibration method and temperature drift correction are robust and give adequate results, with a temperature uncertainty increasing non-linearly from about 0.4 K near the surface to about 1.7 K at 800 m. The temperature uncertainty is dominated by the uncertainty in the calibrated parameters for depths larger than 200 m. The paper also offers insights into thermal features observed during the DTRT, including a heat flux inversion along the borehole depth and the slow temperature homogenization under circulation.

 

Abstract Stable boundary layers are still a relatively problematic component of atmospheric modeling, despite their frequent occurrence. While general agreement exists that Monin-Obukhov similarity is not applicable in the stable boundary layer (SBL) due to the non-homogeneous, non-stationary flow, no universal organizing theory for the surface SBL has been presented. The SAVANT (Stable Atmospheric Variability ANd Transport) field campaign took place in the fall of 2018 to explore under what conditions shallow drainage flow is generated. The campaign took place in an agricultural setting and covered the period of both pre- and post-harvest, allowing for not only a basic exploration of the boundary layer but a robust data set for applied agricultural understanding of aerosol dispersion, and impacts of changes in surface cover on drainage flows. This article provides a description of the field campaign. Examples of publicly available data products are presented, as well as examples of shallow drainage flow and corresponding lidar measurements of dispersion. Additionally, the field campaign was used to provide educational opportunities for students from several disciplines and the outcomes of these joint educational ventures are discussed as models for future collaborations.