Distributed Temperature Sensing Research Articles

Distributed temperature sensing on silicon-on-insulator chip by optical frequency domain reflectometry

This study introduces a novel distributed temperature sensing (DTS) technique on silicon-on-insulator (SOI) chips by the optical frequency domain reflectometry (OFDR) technology. In contrast to traditional on-chip silicon photonics temperature sensors which rely on transmission spectrum detection, this method is based on Rayleigh backscatter induced by the sidewall roughness of as-fabricated waveguides, eliminating the need for a specially designed structure. On-chip DTS results with a remarkable sensing spatial resolution of 200 μm and a high temperature sensitivity of 88 pm/K are demonstrated within the temperature range from 22.8 ∘C to 200 ∘C. Furthermore, the technology is employed to measure non-uniform temperature distributions along an SOI waveguide generated by integrated heaters. Importantly, this approach offers a straightforward sensing structure, opening new possibilities for investigating temperature profiles and thermal crosstalk across the chip.

Heated fibre optics to monitor soil moisture under successive saturation–drying cycles: An experimental approach

AbstractIn recent decades, distributed temperature sensing (DTS) has emerged as a robust technology for environmental applications, enabling high‐resolution temperature measurements along fibre optic cables (FOCs). The actively heated fibre optic (AHFO) method is employed to monitor soil moisture (, m3 m−3), wherein the soil temperature subsequent to the application of a heat pulse is measured by a DTS (AHFO‐DTS approach). Despite significant improvements in the application of AHFO‐DTS under controlled and natural conditions, the thermal behaviour of soil during multiple saturation–natural drying cycles has been insufficiently evaluated. This study aimed to address this gap by constructing an experimental horizontal soil profile in the laboratory for the application of the AHFO‐DTS method during two successive saturation–drainage–evaporation (SDE) cycles. Three heating strategies were applied to a metallic alloy in contact with a FOC, and calibration models were used to correlate with the thermal conductivity (), cumulative temperature increase (), and maximum temperature increase (). The results indicated that during the second SDE cycle, the highest errors in estimates were observed with the low power‐short heat pulse, whereas the application of the low power‐long duration and high power‐short duration pulses improved the accuracy of calculations. Additionally, errors in estimates escalated under wetter conditions, attributed to a shift in soil heat transfer capacity from the first to the second SDE cycle for > 0.10 m3 m−3. This behaviour was ascribed to thermal hysteresis, arising from the contact resistance of the FOC and the alloy with the surrounding soil. Furthermore, the method exhibited the least sensitivity to this effect and yielded reliable estimates, thus its adoption is recommended. Moreover, the use of the low power‐long duration heating strategy is suggested as it promotes a trade‐off between energy saving and accurate estimates. We concluded that assessing soil thermal response under multiple SDE cycles enhances the comprehension of the AHFO‐DTS method. Overall, our findings provide insights into enhancing the applicability of this approach under field conditions, particularly following irrigation schedules and natural rainfall events.

QUANTITATIVE MULTISTAGE FRACTURING EFFICIENCY EVALUATION USING CROSS-WELL STRAIN MEASUREMENTS

Crosswell strain measurements acquired through low-frequency Distributed Acoustic Sensing (LF-DAS) is a maturing technique used to monitor and diagnose the efficiency of hydraulic fracturing treatments. While LF-DAS has demonstrated potential in characterizing far-field fracture communication and geometry, the prevailing analysis in this field has historically relied on qualitative interpretations, focusing on the timing and location of frac hits. In response to the evolving landscape of quantitative studies in this area, we present an advanced quantitative technique using our novel Green-function based inversion algorithm to calculate time-dependent far-field fracture width. The adopted algorithm utilizes the 3D displacement discontinuity method to relate fracture aperture to strains measured by LF-DAS along a monitor well during stimulation treatments. This approach is demonstrated on a subset of four treatment stages where Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS) were acquired within the injection well, alongside LF-DAS from a nearby monitor well. LF-DAS inversion results indicate the loss of zonal isolation in three of the monitored stages, leading to significantly smaller fracture widths at the monitor well for targeted treatment stages, and reactivated fractures in the adjacent previous stages. These interpretations are cross validated through the integration of in-well DAS and DTS analysis, where severe inter-stage fluid communication is observed. The inverted fracture widths quantify the impact on far-field fracture geometry associated with poor stage isolation and decreased fracturing efficiency. This new approach demonstrates the potential of LF-DAS for quantitative analysis and interpretation, facilitating improved understanding and optimization of hydraulic stimulation, going beyond its conventional qualitative role in fracturing diagnostics.

Seepage Anomaly Detection via Fiber Optic Distributed Temperature Sensing: Insights from Physical and Numerical Modeling

Seepage Anomaly Detection via Fiber Optic Distributed Temperature Sensing: Insights from Physical and Numerical Modeling

Novel transient-state analysis approach for distributed temperature sensing based on spontaneous Raman scattering

The light scattering phenomenon in optical fibres has been implemented in several applications for years. Nowadays, commercial distributed sensing systems for temperature measurements – distributed temperature sensing (DTS) – are gaining more attention and are being applied in many fields. The DTS systems are commonly based on optical time domain reflectometry or optical frequency domain reflectometry analysis approaches. This paper presents the simulation model and primary experimental results of a novel approach for data analysis for DTS systems based on the measurement of the backscattered Raman anti-Stokes radiation: the optical transient-state domain reflectometry. The novelty of this approach is the fast and accurate proposed analysis algorithm, as well as the cost-effective, secure and reliable set-up, whose main aspect is the implementation of long, low-power input laser pulses.

Modeling transient temperature coupled pressure behaviour for waterflooding well with induced fractures: Semi-analytical model, numerical model, and case studies

Waterflooding is a widely used technique for developing unconventional reservoirs. Long-term waterflooding can lead to the formation of multiple induced fractures. Accurately tracking the location, extent, and dynamic behavior of these induced fractures is crucial. To enhance monitoring capabilities, data obtained from distributed temperature sensors are used to track temperature variations within the wellbore and at the bottom of the well. Combining temperature and pressure inversion techniques helps mitigate the issue of multiple solutions while offering a more comprehensive characterization of multi-induced fractures from various perspectives. In this study, this work presents a model for temperature coupled pressure (TCP) transient analysis. Additionally, this work utilizes COMSOL Multiphysics software to develop and solve a dynamic wellbore temperature (DWT) model. This work proposes a workflow that describes the dynamic behaviour of multi-induced fractures. During model development and solution, this work employs both pressure and temperature transient analysis methods, examining time-dependent non-linear pressure drop and heat transfer variations. Our results reveal that induced fractures exhibit convective heat exchange with the wellbore, resulting in significantly higher temperatures at the induced fracture locations compared to other wellbore locations. By analyzing temperature-wellbore location curves, this work can accurately identify the locations of induced fractures. The TCP model successfully extracts characteristic parameters of induced fractures, while the DWT model allows for monitoring the locations and heights of these fractures. By combining the workflows of both models, this work achieves a comprehensive characterization of the physical properties of induced fractures. In conclusion, this advancement empowers engineers to manage the development of such fractures effectively, thereby averting potential adverse effects on production well performance and preventing the need for well abandonment.

Performance improvement of a distributed temperature sensor with kilometer length and centimeter spatial resolution based on polarization-sensitive OFDR

A method to improve the performance of distributed temperature sensors with kilometer sensing distance and centimeter spatial resolution is proposed based on polarization-sensitive optical frequency domain reflectometry (OFDR). This approach records the temperature change along polarization maintaining fiber (PMF) from the Rayleigh backscattering (RBS) spectral difference between two orthogonal polarization axes using the distributed autocorrelation algorithm, where PMF is used as the sensing fiber. In addition, a technique to calibrate population birefringence and local birefringence, and a method to calibrate the initial birefringence inhomogeneity of PMF are proposed to enhance the performance of the sensor. The birefringence of PMF is used for temperature sensing, which is distinct from the traditional cross-correlation method of single-mode optical fiber temperature sensing, providing us with an innovative scheme to solve real-world engineering problems. The final sensing performance is achieved, with a sensing distance of 1.5 km, a spatial resolution of 5 cm, and a temperature measurement uncertainty of ±0.2 °C, subject to environmental noise and system random noise.

A distributed-temperature-sensing-based soil temperature profiler

Abstract. Storage change in heat in the soil is one of the main components of the energy balance and is essential in studying the land–atmosphere heat exchange. However, its measurement proves to be difficult due to (vertical) soil heterogeneity and sensors easily disturbing the soil. Improvements in the precision and resolution of distributed temperature sensing (DTS) equipment has resulted in its widespread use in geoscientific studies. Multiple studies have shown the added value of spatially distributed measurements of soil temperature and soil heat flux. However, due to the spatial resolution of DTS measurements (∼30 cm), soil temperature measurements with DTS have generally been restricted to (horizontal) spatially distributed measurements. This paper presents a device which allows high-resolution measurements of (vertical) soil temperature profiles by making use of a 3D-printed screw-like structure. A 50 cm tall probe is created from segments manufactured with fused-filament 3D printing and has a helical groove to guide and protect a fiber-optic (FO) cable. This configuration increases the effective DTS measurement resolution and will inhibit preferential flow along the probe. The probe was tested in the field, where the results were in agreement with the reference sensors. The high vertical resolution of the DTS-measured soil temperature allowed determination of the thermal diffusivity of the soil at a resolution of 2.5 cm, many times better than what is feasible using discrete probes. A future improvement in the design could be the use of integrated reference temperature probes, which would remove the need for DTS calibration baths. This could, in turn, support making the probes “plug and play” into the shelf instruments without the need to splice cables or experience in DTS setup design. The design can also support the integration of an electrical conductor into the probe and allow heat tracer experiments to derive both the heat capacity and the thermal conductivity over depth at high resolution.

A Novel Approach to Raman Distributed Temperature-Sensing System for Short-Range Applications.

A novel approach to the development of Distributed Temperature-Sensing (DTS) systems based on Raman Scattering in Multimode optical fibers operating at around 800 nm is presented, focusing on applications requiring temperature profile measurement in the range of a few hundreds of meters. In contrast to the standard Raman DTS systems, which aim to shorten the pulse space width as much as possible to improve the precision of measurement, the novel approach studied in this work is based on the use of pulses with a space width that is approximately equal to the distance covered by the fiber under test. The proposed technique relies on numerical post-processing to obtain the temperature profile measurement with a precision of about ±3 °C and a spatial resolution of 8 m, due to the transaction phases of the optical pulses. This solution simplifies the electronic circuit development, also minimizing the required laser peak power needed compared to the typical narrow pulse techniques.

Fiber optics passive monitoring of groundwater temperature reveals three-dimensional structures in heterogeneous aquifers

Alluvial aquifers often exhibit highly conductive embedded formations that can act as preferential pathways for the transport of solutes. In this context, a detailed subsurface characterization becomes crucial for an effective monitoring of groundwater quality and early detection of contaminants. However, small-scale heterogeneities are seldom detected by traditional nondestructive investigations. Heat propagation in porous media can be a relatively inexpensive tracer for groundwater flow, potentially offering valuable information in various applications. In this study, we applied passive Fiber Optics Distributed Temperature Sensing (FO-DTS) to a group of observation wells in a highly heterogeneous phreatic aquifer to uncover structures with different hydraulic conductivity, relying on their response to temperature fluctuations triggered by natural and anthropogenic forcings. A comprehensive data analysis approach, combining statistical methods and physics-based numerical modeling, allowed for a three-dimensional characterization of the subsurface at the experimental site with unprecedentedly high resolution.

Distributed Temperature Sensing through Network Analysis Frequency-Domain Reflectometry.

In this paper, we propose and demonstrate a network analysis optical frequency domain reflectometer (NA-OFDR) for distributed temperature measurements at high spatial (down to ≈3 cm) and temperature resolution. The system makes use of a frequency-stepped, continuous-wave (cw) laser whose output light is modulated using a vector network analyzer. The latter is also used to demodulate the amplitude of the beat signal formed by coherently mixing the Rayleigh backscattered light with a local oscillator. The system is capable of attaining high measurand resolution (≈50 mK at 3-cm spatial resolution) thanks to the high sensitivity of coherent Rayleigh scattering to temperature. Furthermore, unlike the conventional optical-frequency domain reflectometry (OFDR), the proposed system does not rely on the use of a tunable laser and therefore is less prone to limitations related to the laser coherence or sweep nonlinearity. Two configurations are analyzed, both numerically and experimentally, based on either a double-sideband or single-sideband modulated probe light. The results confirm the validity of the proposed approach.

Comparison of Gas Signature and Void Fraction in Water- and Oil-Based Muds Using Fiber-Optic Distributed Acoustic Sensor, Distributed Temperature Sensor, and Distributed Strain Sensor

Comparison of Gas Signature and Void Fraction in Water- and Oil-Based Muds Using Fiber-Optic Distributed Acoustic Sensor, Distributed Temperature Sensor, and Distributed Strain Sensor

Prediction of Production-Inflow Profile of a Well Producing Single-Phase Flow of Slightly Compressible Fluid from Multilayer Systems by Temperature and/or Pressure Transient Data

Summary This study focuses on the prediction of the production-inflow profile of a well producing a single-phase flow of slightly compressible fluid (water or oil flow) in a multilayered system using the layer permeability and skin values estimated by history matching spatial and temporal temperature and/or pressure data sets along the completion interval. Such data may be acquired by wireline formation testing, production-logging-tool (PLT), or distributed temperature sensing (DTS) fiber-optic cables. We use an in-house thermal, transient coupled reservoir/wellbore simulator developed during this study. It solves transient mass, momentum, and energy conservation equations simultaneously for both reservoir and wellbore. The effects of the Joule-Thomson (J-T), adiabatic expansion, conduction, and convection are all included for predicting the flow profiles across the wellbore. The results from our in-house simulator are verified with the results from a commercial simulator for the single-phase fluid flow of a vertical well producing geothermal brine and oil in a two-zone multilayer system. We also compare the results from our rigorous transient coupled wellbore/reservoir model with the results from a model assuming steady-state thermal wellbore model used in the previous studies. We find that the steady-state thermal wellbore model used in the previous studies that ignore accumulation terms in mass, momentum, and thermal energy balances is a reasonably accurate model for predicting wellbore pressures and temperatures when it is coupled with a nonisothermal reservoir model for slightly compressible fluid because the transient effect in the wellbore is less important with the slightly compressible fluid. We investigate the nonlinear parameter estimation problem based on the use of single or multiple observed temperature and/or pressure (if available) profiles recorded spatially inside the wellbore and at the sandface. The purpose is to identify if the wellbore or sandface data profiles are more useful to accurately estimate the permeability and skin information and predict a production-inflow profile of the well depending on the representation of an actual multilayer system by a reduced-layered or fine-layered model. We show that using an upscaled-layered model (e.g., representing each heterogeneous layer with a lumped single layer with uniform permeability and skin) provides estimates that are more toward the thickness-average permeability and skin factors of the layers and may not provide a good prediction of the well’s production-inflow profile. We show that including the sandface temperature data in regression worsens, while the use of wellbore temperature data sets improves the quality of parameter estimation if an upscaled multilayered model is used. We also show that regressing on multiple temperature profiles, preferably at the sandface, alone could be used to predict the production-inflow profile accurately if a “fine” multilayered heterogeneous model is used. We also investigate if including or excluding the temperature and/or pressure measurements at the nonperforated sections along the completion interval could help enhance the parameter estimation problem. The results show that when multiple profiles of temperatures including the data at nonperforated zones at different production rates are regressed, reliable estimates of the individual layer properties that predict the production-inflow profile accurately can be obtained, though layer permeability and skin factors may often exhibit wide 95% confidence intervals and high correlations among them. Adding sandface or wellbore pressure data, if available, into observed data sets in history matching always improves the quality of parameter estimation.

Fibre optic sensing technology for field assessment of thermal conductivity of aquatic sediments

PurposeThis study explores the in situ variability of sediment thermal conductivity (K) in a pond, integrating field-deployed fibre optic sensing with laboratory analyses of sediment properties to enhance our understanding and management of aquatic systems.Materials and methodsA 20-m cable setup, consisting of a fibre optic cable (FOC) and a heating tape, was buried at two depths within a channel-shaped section of a pond. Induced temperatures along the FOC were recorded during several heating and cooling periods using distributed temperature sensing (DTS). Thermal conductivity (K) was estimated at five locations along the FOC during the heating periods using the heat conduction theory for an infinite line source. Sediment core samples collected from these locations were analyzed to determine dry bulk density (DBD), organic matter content (OM), and particle size distribution (PSD), exploring their effects on K variability.ResultsAnalysis of core samples identified three distinct layers, each with varying PSD, OM, and DBD. The study revealed substantial spatial differences in the thermal conductivity of sediments, even over very short distances along the FOC, attributed to variations in sediment properties. Through a combination of field and laboratory results, we developed quadratic regression models (R2 > 0.9) to characterize the influence of DBD and OM on K. These models enabled detailed vertical and horizontal characterization of K within specific sediment contexts.ConclusionThe study demonstrates the effectiveness of active DTS in detecting in-situ variations in K, emphasizing the impact of OM and DBD on temperature propagation. This study highlights the necessity of considering sediment property variability in modelling heat transfer for accurate water resource management and environmental assessments.

Use of distributed temperature sensing (DTS) coupled to ground source heat exchangers for geological thermo-stratigraphic correlation

The thermal characterisation of a geosonde field, consisting of four boreholes at the ENEA-Casaccia Research Centre (Rome, Italy), was carried out by processing the temperature values measured by DTS (Distributed Temperature Sensing) fibre optics positioned vertically in each well. By correlating the vertical temperature profiles, it was possible to estimate the thermal conductivity of each stratigraphic level and the contribution of the groundwater on the heat exchange between ground and geothermal probes. The theoretical model has been confirmed by the experimental data obtained through direct measurement of thermal conductivity on soil/rock samples collected at different depths.In the first 10 m of depth, temperature variations are influenced by seasonal climatic fluctuations, the amplitude of which decreases with depth, to regularise and assume a linear trend, compatible with the movement of groundwater.The work carried out has shown that the area investigated is affected by the upwelling of hydrothermal fluids from the deep aquifer, which infiltrate the fractured lavas reaching the most superficial layers of the subsurface. The results of this research could be useful for estimating the geothermal potential of the areas within the ‘Cesano geothermal field’, whose lithotypes belong to the Sabatini volcanic province.

Slickline-Deployed Fiber-Optic Cable Provides First Production Profile for High-Temperature Gas Well

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 215512, “Slickline-Deployed Fiber-Optic Cable Provides First-Ever Production Profile for High-Temperature Gas Well,“ by Stuart Berry, SPE, Expro, and Danu Dirya, SPE, and Graeme Cowie, SPE, TotalEnergies, et al. The paper has not been peer reviewed. _ Distributed fiber-optic sensing (DFOS) allowed the continuous gathering of flow-profile information from a high-temperature, high-rate gas well. The objective of the case study described in the complete paper is to demonstrate that thermal inversion modeling can be used to produce a production-flow profile in an environment where conventional production logging is not possible. As a result of deploying DFOS, data can be acquired at more-realistic rates. Through performing of thermal inversion of distributed temperature sensing (DTS) data and analysis of distributed acoustic sensing (DAS) data, a more-accurate flow profile was achieved. Introduction Culzean is a high-pressure/high-temperature gas condensate field in the central North Sea. The main productive interval for the field is the Triassic Joanne sandstone deposited in a semiarid continental fluvial environment. The Jurassic Pentland sandstone deposited by meandering fluvial systems also contains significant resources. Initial reservoir pressure was 936 bar; the temperature was 170°C. The development consists of six production wells, five from the Joanne and one from the Pentland. While the reservoir interval is relatively thick, the depositional environment created a very layered reservoir with significant variability in reservoir quality. Flow-profile data over the reservoir has traditionally been acquired using production-logging tools with spinner velocity and pressure and temperature measurements. Production-log deployment can be problematic in high-rate wells, particularly when tubing is restricted because of pipe deformation or deposits in the tubing. In the Culzean field, several wells were selected for flow-profile monitoring. Previous slickline operations indicated that conventional production logging would be ineffective. Modeling showed significant tool-lift risk, meaning that the well could only be flowed at approximately 25% of its normal operating rate. A previous attempted production-logging campaign in the well resulted in no valid data acquisition but incurred a direct cost of $700,000 and a deferral of 10 days production estimated at a cost of $10.8 million. An alternative acquisition method was investigated to obtain production-profile data while minimizing risks associated with conventional production-log deployment. DFOS provides a continuous measurement over the entire length of the well for temperature and acoustic noise. A high-temperature slickline DFOS wire could allow Culzean well data acquisition under flowing conditions. Technology Selection Data acquisition was divided into a shut-in phase and a flowing phase. For the shut-in phase, a simplified production-logging toolstring was used to measure any crossflow over the reservoir section. The flowing phase would consist only of DAS data over the reservoir section, with a simple pressure/temperature memory tool in the sump of the well. The DFOS data would gather shut-in data before opening the well up to flow. For the conventional tool shut-in string, an inline caged spinner was selected.

Acoustic propagation modeling using sound velocity profiles estimated from high resolution temperature data

An accurate sound velocity profile (SVP) is an important input for underwater acoustic propagation modeling. The SVP is largely driven by the temperature of the water column, especially at shallower depths. Measuring the SVP in high spatial and temporal resolution is challenging with traditional instrumentation, however techniques exist to produce high resolution temperature measurements that can be used to derive estimates of the SVP. Fiber optic distributed temperature sensing (DTS) offers an improvement of several orders of magnitude over traditional singular point measurement devices and has the ability to measure temperature over large range and depth scales in an efficient manner. A DTS system was towed for 172 nm off the coast of New England over several days. The measured temperature data was used to estimate the SVP across the range and depth and will be compared to traditional environmental data sets, such as HYCOM. A comparison of modeled acoustic propagation using different resolution SVPs will be presented.

Multiphysics monitoring of cementation operation: implications for wellbore integrity and hydrogeological characterization

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.

Photon counting fibre optic distributed temperature sensing with a CMOS SPAD array.

Time-resolved fibre optic Raman distributed temperature sensing (DTS) measurements experience long measurement times due to a weak backscattered Raman signal inside optical fibres or limited detector count rates. Here, improvements to previous work based on individual detectors are demonstrated using a 512 pixel complementary-metal-oxide semiconductor (CMOS) single-photon avalanche diode (SPAD) line sensor array with integrated (on-chip) timing electronics. Multiplexed single photon counting increases count rate and decreases measurement time for practical applications. This allows temperature to be measured every 0.5 m with 0.7 °C accuracy and a 10 s measurement time using a 13.0 m optical fibre, performance over longer distance is also investigated.

A novel CNN-based method for localization of temperature anomalies in RDTS system

In this study, the effect of convolutional neural network combined with cosine similarity approach on the localization of temperature anomalous events was investigated by designing Raman-based Distributed Temperature Sensing (RDTS) experiments. We first constructed convolutional neural network (CNN) models to extract temperature anomaly features. RDTS experiments were designed to obtain the dataset for training the CNN models. Subsequently, the cosine similarity between the anomalous features and the original signal was calculated to overcome the disadvantage of feature offset. Finally, the feature with the highest cosine similarity was used as the final temperature anomaly event feature. Moreover, another experiment was designed to verify the performance of the proposed method. Based on optical fiber data with a resolution of 1 m, we cut the original signal into data segments with a length of 100 sampling points and used them as input to the CNN model. The main results show that the proposed method has an accuracy of 98.37 %, a precision of 93.53 %, a recall of 75.54 %, and an overall evaluation index F1 score of 0.84 (0.81 for the Copula-based outlier detection method, and 0.75 for the quartile method). The proposed method shows excellent generalization performance on datasets with different signal-to-noise ratios, providing a novel and effective solution for locating temperature abnormal events in RDTS systems.