With the application of Distributed Acoustic Sensors(DAS)across various infrastructures,it will play a pivotal role in shaping smart cities in the future.However,the current single-source detection and identification ...With the application of Distributed Acoustic Sensors(DAS)across various infrastructures,it will play a pivotal role in shaping smart cities in the future.However,the current single-source detection and identification technology might struggle to meet the high precision needs in the intricate environmental conditions of mixed multi-source interference.We propose a new deep neural network-based multi-source signal separation method for DAS and accomplish the separation performance of this method under practical applications.In addition,a new evaluation metric for the separation method is proposed in conjunction with the separation and identification of DAS mixed signals.For mixed signals with different source numbers,the recognizable rate of separated signals can reach 98.33%on average.This study provides a promising solution to the multi-source mixed interference problem faced by DAS in complex environments.展开更多
The distributed acoustic sensor(DAS)uses a single optical cable as the sensing unit,which can capture the acoustic and vibration signals along the optical cable in real-time.So it is suitable for monitoring downhole p...The distributed acoustic sensor(DAS)uses a single optical cable as the sensing unit,which can capture the acoustic and vibration signals along the optical cable in real-time.So it is suitable for monitoring downhole production activities in the process of oil and gas development.The authors applied the DAS system in a gas production well in the South China Sea for in situ monitoring of the whole wellbore for the first time and obtained the distributed acoustic signals along the whole wellbore.These signals can clearly distinguish the vertical section,curve section,and horizontal production section.The collected acoustic signal with the frequency of approximately 50 Hz caused by the electric submersible pump exhibit a signal-to-noise ratio higher than 27 dB.By analyzing the acoustic signals in the production section,it can be located the layers with high gas production rates.Once an accurate physical model is built in the future,the gas production profile will be obtained.In addition,the DAS system can track the trajectory of downhole tools in the wellbore to guide the operation.Through the velocity analysis of the typical signals,the type of fluids in the wellbore can be distinguished.The successful application of the system provides a promising whole wellbore acoustic monitoring tool for the production of marine gas hydrate,with a good application prospect.展开更多
A distributed optical-fiber acoustic sensor is an acoustic sensor that uses the optical fiber itself as a photosensitive medium,and is based on Rayleigh backscattering in an optical fiber.The sensor is widely used in ...A distributed optical-fiber acoustic sensor is an acoustic sensor that uses the optical fiber itself as a photosensitive medium,and is based on Rayleigh backscattering in an optical fiber.The sensor is widely used in the safety monitoring of oil and gas pipelines,the classification of weak acoustic signals,defense,seismic prospecting,and other fields.In the field of seismic prospecting,distributed optical-fiber acoustic sensing(DAS)will gradually replace the use of the traditional geophone.The present paper mainly expounds the recent application of DAS,and summarizes recent research achievements of DAS in resource exploration,intrusion monitoring,pattern recognition,and other fields and various DAS system structures.It is found that the high-sensitivity and long-distance sensing capabilities of DAS play a role in the extensive monitoring applications of DAS in engineering.The future application and development of DAS technology are examined,with the hope of promoting the wider application of the DAS technology,which benefits engineering and society.展开更多
Distributed acoustic sensors(DASs)can effectively monitor acoustic fields along sensing fibers with high sensitivity and high response speed.However,their data processing is limited by the performance of electronic si...Distributed acoustic sensors(DASs)can effectively monitor acoustic fields along sensing fibers with high sensitivity and high response speed.However,their data processing is limited by the performance of electronic signal processing,hindering real-time applications.The time-wavelength multiplexed photonic neural network accelerator(TWM-PNNA),which uses photons instead of electrons for operations,significantly enhances processing speed and energy efficiency.Therefore,we explore the feasibility of applying TWMPNNA to DAS systems.We first discuss processing large DAS system data for compatibility with the TWM-PNNA system.We also investigate the effects of chirp on optical convolution in complex tasks and methods to mitigate its impact on classification accuracy.Furthermore,we propose a method for achieving an optical full connection and study the influence of pruning on the full connection to reduce the computational burden of the model.Experimental results indicate that decreasing the ratio of Δλ_(chirp)/Δλ or choosing push-pull modulation can eliminate the impact of chirp on recognition accuracy.In addition,when the full connection parameter retention rate is no less than 60%,it can still maintain a classification accuracy of over 90%.TWM-PNNA provides an innovative computational framework for DAS systems,paving the way for the all-optical fusion of DAS systems with computational systems.展开更多
Distributed underwater acoustic sensor networks(UASNs)are envisioned in real-time ocean current velocity estimation.However,UASNs at present are still dominated by post-processing partially due to the complexity of on...Distributed underwater acoustic sensor networks(UASNs)are envisioned in real-time ocean current velocity estimation.However,UASNs at present are still dominated by post-processing partially due to the complexity of on-line detection for travel times and lack of dedicated medium access control(MAC)protocols.In this study,we propose a dedicated MAC protocol package for real-time ocean current velocity estimation using distributed UASNs.First,we introduce the process and requirements of ocean current velocity estimation.Then,we present a series of spatial reuse time division multiple access(TDMA)protocols for each phase of real-time ocean current field estimation using distributed UASNs,followed by numerical analysis.We divide UASNs into two categories according to their computing ability:feature-complete and feature-incomplete systems.The feature-complete systems that have abundant computing ability carry out the presented MAC protocol package in three phases,whereas the feature-incomplete ones do not have enough computing ability and the presented MAC protocol package is reduced to two phases plus an additional downloading phase.Numerical analysis shows that feature-complete systems using mini-slot TDMA have the best real-time performance,in comparison with feature-incomplete systems and other feature-complete counterparts.Feature-incomplete systems are more energy-saving than feature-complete ones,owing to the absence of in-network data exchange.展开更多
文摘With the application of Distributed Acoustic Sensors(DAS)across various infrastructures,it will play a pivotal role in shaping smart cities in the future.However,the current single-source detection and identification technology might struggle to meet the high precision needs in the intricate environmental conditions of mixed multi-source interference.We propose a new deep neural network-based multi-source signal separation method for DAS and accomplish the separation performance of this method under practical applications.In addition,a new evaluation metric for the separation method is proposed in conjunction with the separation and identification of DAS mixed signals.For mixed signals with different source numbers,the recognizable rate of separated signals can reach 98.33%on average.This study provides a promising solution to the multi-source mixed interference problem faced by DAS in complex environments.
基金jointly supported by the Science and Technology Program of Guangzhou (202103040003)the offshore NGHs production test projects under the Marine Geological Survey Program initiated by the China Geological Survey (DD20190226, DD20190218 and DD20221706)+2 种基金the Key Program of Marine Economy Development Special Foundation of Department of Natural Resources of Guangdong Province (GDNRC [2020] 045)the financial support from China Geological Survey (DD20221703)the National Natural Science Foundation of China (NSFC) (6210030553)。
文摘The distributed acoustic sensor(DAS)uses a single optical cable as the sensing unit,which can capture the acoustic and vibration signals along the optical cable in real-time.So it is suitable for monitoring downhole production activities in the process of oil and gas development.The authors applied the DAS system in a gas production well in the South China Sea for in situ monitoring of the whole wellbore for the first time and obtained the distributed acoustic signals along the whole wellbore.These signals can clearly distinguish the vertical section,curve section,and horizontal production section.The collected acoustic signal with the frequency of approximately 50 Hz caused by the electric submersible pump exhibit a signal-to-noise ratio higher than 27 dB.By analyzing the acoustic signals in the production section,it can be located the layers with high gas production rates.Once an accurate physical model is built in the future,the gas production profile will be obtained.In addition,the DAS system can track the trajectory of downhole tools in the wellbore to guide the operation.Through the velocity analysis of the typical signals,the type of fluids in the wellbore can be distinguished.The successful application of the system provides a promising whole wellbore acoustic monitoring tool for the production of marine gas hydrate,with a good application prospect.
基金supported by the Science and Technology Development Plan of Jilin Province(No.20180201036GX)
文摘A distributed optical-fiber acoustic sensor is an acoustic sensor that uses the optical fiber itself as a photosensitive medium,and is based on Rayleigh backscattering in an optical fiber.The sensor is widely used in the safety monitoring of oil and gas pipelines,the classification of weak acoustic signals,defense,seismic prospecting,and other fields.In the field of seismic prospecting,distributed optical-fiber acoustic sensing(DAS)will gradually replace the use of the traditional geophone.The present paper mainly expounds the recent application of DAS,and summarizes recent research achievements of DAS in resource exploration,intrusion monitoring,pattern recognition,and other fields and various DAS system structures.It is found that the high-sensitivity and long-distance sensing capabilities of DAS play a role in the extensive monitoring applications of DAS in engineering.The future application and development of DAS technology are examined,with the hope of promoting the wider application of the DAS technology,which benefits engineering and society.
基金supported by the National Natural Science Foundation of China(NSFC)(Grant Nos.U2001601,62175100,62175103,and 61775094)the Equipping Preresearch Project(Grant No.30601010104)+1 种基金the Fundamental Research Funds for the Central Universities(Grant Nos.2024300447,0213-14380211,and 0213-14380265)the Jiangsu Innovation Teams and AI&AI for Science Project of Nanjing University.
文摘Distributed acoustic sensors(DASs)can effectively monitor acoustic fields along sensing fibers with high sensitivity and high response speed.However,their data processing is limited by the performance of electronic signal processing,hindering real-time applications.The time-wavelength multiplexed photonic neural network accelerator(TWM-PNNA),which uses photons instead of electrons for operations,significantly enhances processing speed and energy efficiency.Therefore,we explore the feasibility of applying TWMPNNA to DAS systems.We first discuss processing large DAS system data for compatibility with the TWM-PNNA system.We also investigate the effects of chirp on optical convolution in complex tasks and methods to mitigate its impact on classification accuracy.Furthermore,we propose a method for achieving an optical full connection and study the influence of pruning on the full connection to reduce the computational burden of the model.Experimental results indicate that decreasing the ratio of Δλ_(chirp)/Δλ or choosing push-pull modulation can eliminate the impact of chirp on recognition accuracy.In addition,when the full connection parameter retention rate is no less than 60%,it can still maintain a classification accuracy of over 90%.TWM-PNNA provides an innovative computational framework for DAS systems,paving the way for the all-optical fusion of DAS systems with computational systems.
基金This work was supported by the National Natural Science Foundation of China(No.61531017)the Science and Technology Bureau of Zhoushan(No.2018C41029)the Science and Technology Department of Zhejiang Province(Nos.2018R52046 and LGG18F010005).
文摘Distributed underwater acoustic sensor networks(UASNs)are envisioned in real-time ocean current velocity estimation.However,UASNs at present are still dominated by post-processing partially due to the complexity of on-line detection for travel times and lack of dedicated medium access control(MAC)protocols.In this study,we propose a dedicated MAC protocol package for real-time ocean current velocity estimation using distributed UASNs.First,we introduce the process and requirements of ocean current velocity estimation.Then,we present a series of spatial reuse time division multiple access(TDMA)protocols for each phase of real-time ocean current field estimation using distributed UASNs,followed by numerical analysis.We divide UASNs into two categories according to their computing ability:feature-complete and feature-incomplete systems.The feature-complete systems that have abundant computing ability carry out the presented MAC protocol package in three phases,whereas the feature-incomplete ones do not have enough computing ability and the presented MAC protocol package is reduced to two phases plus an additional downloading phase.Numerical analysis shows that feature-complete systems using mini-slot TDMA have the best real-time performance,in comparison with feature-incomplete systems and other feature-complete counterparts.Feature-incomplete systems are more energy-saving than feature-complete ones,owing to the absence of in-network data exchange.
