Spatial-derivative-based compression approach for distributed temperature data

The new generation of distributed optical sensors with improved interrogation, multiplexing, and acquisition techniques with the possibility of performing measurements with high spatial resolution over tens of kilometers of optical fiber has led to the accumulation of a vast volume of data that can present a big challenge to process and store all this data. Looking for simple solutions to this problem, we present in this paper a data compression method for distributed temperature sensors. This compression approach performs the spatial derivative of the temperature signal, constituting a simple and effective method to remove redundant information. Also, this compression methodology is suitable for temperature data, as it follows thermal variations over time and can be applied to any temperature profile with multiple thermal events along the sensing fiber, whether in heating or cooling circumstances. Tests performed with a large amount of experimental data showed that an average compression ratio of 1.5× can be obtained by removing redundant spatial temperature variations without losing spatial resolution.