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Abstract Submarine groundwater discharge (SGD) in volcanic areas commonly exhibits high temperatures, concentrations of metals and CO₂, and acidity, all of which could affect sensitive coastal ecosystems. Identifying and quantifying volcanic SGD is crucial yet challenging because the SGD might be both discrete, through fractured volcanic rock, and diffuse. At a volcanic area in the Philippines, the novel combination of satellite and drone‐based thermal infrared remote sensing, ground‐based fiber‐optic distributed temperature sensing, and in situ thermal profiling in coastal sediment identified the multi‐scale nature of SGD and quantified fluxes. We identified SGD across ∼30 km of coastline. The different approaches revealed numerous SGD signals from the intertidal zone to about a hundred meters offshore. In active seepage areas, temperatures peaked at 80°C, and Darcy fluxes were as high as 150 cm/d. SGD is therefore locally prominent and regionally important across the study area. 

Abstract 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. 

ABSTRACT 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. 

Abstract 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. 

Abstract Subsurface processes significantly influence surface dynamics in permafrost regions, necessitating utilizing diverse geophysical methods to reliably constrain permafrost characteristics. This research uses multiple geophysical techniques to explore the spatial variability of permafrost in undisturbed tundra and its degradation in disturbed tundra in Utqiaġvik, Alaska. Here, we integrate multiple quantitative techniques, including multichannel analysis of surface waves (MASW), electrical resistivity tomography (ERT), and ground temperature sensing, to study heterogeneity in permafrost’s geophysical characteristics. MASW results reveal active layer shear wave velocities (V_s) between 240 and 370 m/s, and permafrostV_sbetween 450 and 1,700 m/s, typically showing a low‐high‐low velocity pattern. Additionally, we find an inverse relationship between in situV_sand ground temperature measurements. TheV_sprofiles along with electrical resistivity profiles reveal cryostructures such as cryopeg and ice‐rich zones in the permafrost layer. The integrated results of MASW and ERT provide valuable information for characterizing permafrost heterogeneity and cryostructure. Corroboration of these geophysical observations with permafrost core samples’ stratigraphies and salinity measurements further validates these findings. This combination of geophysical and temperature sensing methods along with permafrost core sampling confirms a robust approach for assessing permafrost’s spatial variability in coastal environments. Our results also indicate that civil infrastructure systems such as gravel roads and pile foundations affect permafrost by thickening the active layer, lowering theV_s, and reducing heterogeneity. We show how the resultingV_sprofiles can be used to estimate key parameters for designing buildings in permafrost regions and maintaining existing infrastructure in polar regions. 

Abstract 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.