DTSGUI: A Python Program to Process and Visualize Fiber‐Optic Distributed Temperature Sensing Data

Domański, Marian; Quinn, Daven P.; Day‐Lewis, Frederick D.; Briggs, Martin A.; Werkema, D. Dale; Lane, John W.

doi:10.1111/gwat.12974

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…Despite the less-precise raw data, results suggest a much lower threshold for the magnitude of groundwater discharge at which FO-DTS is applicable (approximately one-fifth of the value proposed previously [4]). Future studies that rely on more advanced post-processing of temperature data (to calibrate FO-DTS temperatures and remove artefacts in the data) may lead to even more precise identification of zones of focused groundwater discharge, although post-processing techniques need to be efficient enough to be conducted quickly in the field to guide sampling [43]. There has been a recent increase in the application of drone-based infrared thermal imaging for quick reconnaissance of groundwater discharge zones along streams at large scales [44]; however, infrared imaging does not penetrate the water column and low-to-moderate-discharge zones are likely to be missed.…”

Section: Discussionmentioning

confidence: 99%

Streambed Flux Measurement Informed by Distributed Temperature Sensing Leads to a Significantly Different Characterization of Groundwater Discharge

Gilmore

Johnson

Korus

et al. 2019

Water

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Groundwater discharge though streambeds is often focused toward discrete zones, indicating that preliminary reconnaissance may be useful for capturing the full spectrum of groundwater discharge rates using point-scale quantitative methods. However, many direct-contact reconnaissance techniques can be time-consuming, and remote sensing (e.g., thermal infrared) typically does not penetrate the water column to locate submerged seepages. In this study, we tested whether dozens of groundwater discharge measurements made at “uninformed” (i.e., selected without knowledge on high-resolution temperature variations at the streambed) point locations along a reach would yield significantly different Darcy-based groundwater discharge rates when compared with “informed” measurements, focused at streambed thermal anomalies that were identified a priori using fiber-optic distributed temperature sensing (FO-DTS). A non-parametric U-test showed a significant difference between median discharge rates for uninformed (0.05 m·day−1; n = 30) and informed (0.17 m·day−1; n = 20) measurement locations. Mean values followed a similar pattern (0.12 versus 0.27 m·day−1), and frequency distributions for uninformed and informed measurements were also significantly different based on a Kolmogorov–Smirnov test. Results suggest that even using a quick “snapshot-in-time” field analysis of FO-DTS data can be useful in streambeds with groundwater discharge rates <0.2 m·day−1, a lower threshold than proposed in a previous study. Collectively, study results highlight that FO-DTS is a powerful technique for identifying higher-discharge zones in streambeds, but the pros and cons of informed and uninformed sampling depend in part on groundwater/surface water exchange study goals. For example, studies focused on measuring representative groundwater and solute fluxes may be biased if high-discharge locations are preferentially sampled. However, identification of high-discharge locations may complement more randomized sampling plans and lead to improvements in interpolating streambed fluxes and upscaling point measurements to the stream reach scale.

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Section: Discussionmentioning

confidence: 99%

Streambed Flux Measurement Informed by Distributed Temperature Sensing Leads to a Significantly Different Characterization of Groundwater Discharge

Gilmore

Johnson

Korus

et al. 2019

Water

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“…The recent works on the DTS application include the development of the DTSGUI program, which is used for the processing of optical fiber DTS data [ 74 ]. DTSGUI is programmed in Python, which enables the users to edit, process, analysis, and visualize the obtained optical fiber DTS data.…”

Section: Seepage Detection Techniques From the Temperature Measurementioning

confidence: 99%

A Review of Measurement Calibration and Interpretation for Seepage Monitoring by Optical Fiber Distributed Temperature Sensors

Ghafoori

Vidmar

Říha

et al. 2020

Sensors

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Seepage flow through embankment dams and their sub-base is a crucial safety concern that can initiate internal erosion of the structure. The thermometric method of seepage monitoring employs the study of heat transfer characteristics in the soils, as the temperature distribution in earth-filled structures can be influenced by the presence of seepage. Thus, continuous temperature measurements can allow detection of seepage flows. With the recent advances in optical fiber temperature sensor technology, accurate and fast temperature measurements, with relatively high spatial resolution, have been made possible using optical fiber distributed temperature sensors (DTSs). As with any sensor system, to obtain a precise temperature, the DTS measurements need to be calibrated. DTS systems automatically calibrate the measurements using an internal thermometer and reference section. Additionally, manual calibration techniques have been developed which are discussed in this paper. The temperature data do not provide any direct information about the seepage, and this requires further processing and analysis. Several methods have been developed to interpret the temperature data for the localization of the seepage and in some cases to estimate the seepage quantity. An efficient DTS application in seepage monitoring strongly depends on the following factors: installation approach, calibration technique, along with temperature data interpretation and post-processing. This paper reviews the different techniques for calibration of DTS measurements as well as the methods of interpretation of the temperature data.

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“…Entre tanto, M. Domanski et al desarrollan la interfaz gráfica DTSGUI en el entorno de programación Python para el procesamiento y visualización de datos de detección de temperatura distribuidos por fibra óptica (FO-DTS por Fiber-Optic Distributed Temperature Sensing), aplicada a mediciones de variables en ciencias hidrológicas para fenómenos de intercambio hiporreico, interacción de agua subterránea, entre otros. La técnica FO-DTS genera un volumen considerable de datos que se capitalizan en la interfaz gráfica a partir de la edición, análisis estadístico, georreferenciación y visualización de datos [54].…”

Section: A Perspectiva Internacionalunclassified

Análisis del diseño de interfaces gráficas de usuario para el control y monitorización de sistemas optoelectrónicos aplicados a telecomunicaciones e instrumentación electrónica

2022

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El análisis del uso de interfaces gráficas de usuario (GUI) aplicado a sistemas electrónicos ópticos para telecomunicaciones e instrumentación, permite observar las posibilidades existentes en la integración de sistemas ingenieriles con equipos de cómputo para facilitar el acceso a la información y optimizar la interacción hombre/máquina en los diferentes escenarios de las industrias que se desarrollan en la actualidad. Mediante el uso de bases de datos académicas, indexadas y aceptadas por la comunidad científica, se consultaron un total de sesenta referencias bibliográficas orientas al diseño e implementación de interfaces gráficas de usuario desarrolladas a nivel internacional, en Colombia y en la región de Norte de Santander, analizando los diferentes entornos de programación y la usabilidad esperada en el control y monitorización de sistemas optoelectrónicos. Se observa el auge en el uso de entornos de programación de licencia abierta y que los investigadores tienden a integrar múltiples plataformas de desarrollo para aprovechar las características de cada uno de ellos.

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DTSGUI: A Python Program to Process and Visualize Fiber‐Optic Distributed Temperature Sensing Data

Cited by 10 publications

References 13 publications

Streambed Flux Measurement Informed by Distributed Temperature Sensing Leads to a Significantly Different Characterization of Groundwater Discharge

Streambed Flux Measurement Informed by Distributed Temperature Sensing Leads to a Significantly Different Characterization of Groundwater Discharge

A Review of Measurement Calibration and Interpretation for Seepage Monitoring by Optical Fiber Distributed Temperature Sensors

Análisis del diseño de interfaces gráficas de usuario para el control y monitorización de sistemas optoelectrónicos aplicados a telecomunicaciones e instrumentación electrónica

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