Signal loss detection method for distributed acoustic sensing systems

Abstract

A signal loss detection method detects the location of undesired levels of signal loss for DAS systems. Raw data is received from a DAS system with a dual photodetector. Statistics are obtained from t

Description

Technical Field The present invention relates to a method that detects the location of undesired levels of signal loss for DAS systems Background Rayleigh-Scattering based DAS (Distributed Acoustic Sensing) systems use fiber optic cables to provide distributed strain sensing over large distances. The optical fiber cable becomes an acoustic sensor that provides spatio temporal data over a specific line of area. These systems allow monitoring of activities over distances up to 50 km with a laser source. The locations on fiber cable are named as channels to keep track of the environment. A channel represents a certain amount of length on the fiber cable and numbered according to the distance of current fiber cable part compared to the DAS system. In the DAS system, received light from the far distances is very weak, requiring a high power amplifier to be used. On the other hand, with high gain the light received from close-in distances are amplified too much to the point of saturating the system. To overcome this problem, the light power is divided into two parts with optical coupler, 90% of the light is used for far distances and 10% of the light is used for close-in distances. Fiber cable signal has losses from absorption and back reflection of the light caused by impurities in the glass. The signal power decreases linearly with distance depending on the mode and the wavelength of the fiber. The spatio temporal data obtained by the DAS system is used to get the statistics of the acoustic signal at each channel. In order to get rid of possible anomalies in the signal, the data is taken for a period of time. Those statistics represents the signal-to-noise ratio (SNR) of each channel. The SNR of data on fiber cable can be modelled as attenuating linearly with distance to the source. A Gaussian distribution model, with linearly decreasing mean with respect to distance, is fitted to the calculated statistics. The application numbered JP2004163294A describes a structure displacement / abnormality detector to detect and measure the presence of a relative displacement in a revetment structure, generation of abnormality therein and a generation position thereof, by laying preliminarily a general and commercially available optical fiber sensor for communication along the line-like revetment structure. This detector is constituted using an optical fiber cable, and is provided with the optical fiber cable, a displacement / abnormality sensor and a displacement / abnormality management device. The displacement / abnormality sensor has a locking part, a base plate, the optical fiber cable and a detection signal. The displacement / abnormality management device has an OTDR (Optical Time-Domain Reflectometer) measuring instrument. OTDR is used to assess the integrity of the fiber link infrastructure. It is an effective method to identify the power loss within the fiber cable, yet in order to test the fiber optic cable, it requires the DAS) system to shut down for a period of time. The OTDR meters are also expensive. Summary The purpose of this invention is to detect the location of undesired levels of signal loss for DAS systems. In proposed method, raw data is received from a DAS system with dual photodetector. Statistics are obtained from the received raw data, then processed according to the dual photodetector. The obtained statistical data are reconstructed to remove noise and the reconstructed signal is used to form the power statistics of the DAS signal in each channel. The power statistics are expected to be linearly decreasing with the distance from the sensor. A change detection algorithm is developed to detect the possible undesired levels of signal loss and to find the location of the signal loss. Brief Description of the Figures Figure 1 shows block diagram of the present invention. Figure 2 shows received raw data from a DAS system with dual photodetector. Figure 3 shows the power statistics and the detection of power loss on channel 2150. Detailed Description Data Processing Data pre-processing step comprises of the first five blocks in the Figure 1. In the collect DAS data block, the DAS data of C channels from one time frame is collected. In the down sample collected DAS data block, Ni of the data obtained is down sampled. In the collect down sampled DAS data block, N2 of the down sampled data is collected, meaning a total of (N1 x N2) data is collected and used up to this point. In the create statistics of collected data block, the standard deviation of each channel is calculated to get a statistical power data from N2 data obtained. In the remove optical coupler effect on statistical data block, the effect of the coupler is negated by amplifying the merged signals at close-in distances. This is possible because of the Gaussian assumption. If a gaussian random variable is multiplied with a constant, the new random variable is also a Gaussian random variable with its standard deviation multiplied with the constant. Signal Loss Detection Signal loss detection step comprises of the remaining blocks. In the perform line fitting from each channel to both ends of DAS system block, the aim is to find if an abrupt change in the power levels with respect to channels occurs or not. The assumption here is, if a finite sample yi,... ,yN assumed to have a probability density p(0) and for each i, 1 <i <N, the samples obtained reside in this probability distribution, there is no change in the distribution of the samples. However, for an index k, if there can be found 0o and 01 that 0 = 0o for 1 <i <k, 0 = 01 for k <i <N, it can be said that there is a change in the distribution at position k. Let yk be a sequence of independent random observations with Gaussian distribution of (Uk,o) where Uk = a + pk, 1 <k <N. In this case 0 = (a, P), 0o = (ao, Po), 01 = (ai, Pi). This distribution explains the power statistics obtained by the last block with k being the channel and yk being the power of the kth channel. Therefore, at each channel k, maximum likelihood estimation can be used to find 0o and 01, which ultimately equals to fitting two lines for its lower channels (from 0 to k) and higher channels (from kto N), respectively. In the select the best channel with minimum fit errors block, a maximum likelihood estimation is used to find the best channel that minimizes the errors when the two lines are fit to explain the data. Since the distribution is gaussian, it is equal to calculating sum of squared errors at each channel compared to lines. In the calculate the difference between two line fits on the best channel block, the two parameters 0o and 01 used at the best channel Cbest and the difference between the lower channels fit and higher channels fit are calculated. In the perform decision rule with threshold block, the calculated difference is compared with a threshold, and if the threshold is exceeded, the system gives an alarm that there is a significant signal loss at the specified channel Cbest. Work Principle The received raw data from the DAS system with dual photodetector can be seen in Figure 2. Using this raw data, the data that would be obtained by the sensor without the dual photodetector can be reconstructed, but this will not be necessary for the algorithm hence it will not be calculated. Instead, statistics are obtained from the received raw data, then processed according to the dual photodetector. Although the amplitude of the raw data gives information about the power level of the channels, it is highly affected from the interior and exterior noises. To get rid of those effects, the fluctuations in the signal at a specific channel with respect to time can be used. Then the obtained statistical data are reconstructed into a signal that would be obtained if the raw signal didn’t put through a photodetector. The reconstructed statistical data is used to form the power statistics of the DAS signal in each channel. The power statistics are expected to be linearly decreasing with the distance from the sensor. A change detection algorithm is developed to detect the possible undesired levels of signal loss and to find the location of the signal loss. It can be seen that there is a significant power loss in channel 2150 in Figure 3. The algorithm chooses the hypothesis that there is a significant power loss in the system. The lines correspond to the optimum Gaussian distribution means with (ao + iPo), (ai + iPi), where i is the channel number.

Claims

1. Signal loss detection method for DAS systems, comprises steps of:• collecting DAS data of channels from one time frame,• down sampling the collected DAS data and collecting the down sampled DAS data,• calculating standard deviation of each channel to get a statistical power data from collected down sampled DAS data,• removing optical coupler effect on the statistical power data,• performing line fitting from each channel to both ends of DAS system to find if an abrupt change in the power levels with respect to channels occurs or not,• selecting best channel with minimum fit errors using a maximum likelihood estimation,• calculating difference between two line fits on the best channel,• comparing the calculated difference with a threshold,• giving an alarm that there is a significant signal loss at the best channel if the threshold is exceeded.

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