Abstract: In accordance with embodiments of the present disclosure, systems and methods for downsampling DAS data in a way that enables accurate interpretation of acoustic events occurring in the data are provided. Such methods may be particularly useful when interpreting large sets of data, such as DAS VSP data collected during hydrocarbon recovery operations. The methods generally involve identifying data channels affected by noise from a DAS data set, and then interpolating from the surrounding data. This may improve the quality of the resulting downsampled data, with respect to the signal to noise ratio, compared to what would have occurred by merely decimating unwanted data channels. In addition, a priori information about channel fading, the desired downsampling rate, and the slowest expected elastic waves may be used to filter the DAS data. This may achieve a higher signal-to-noise ratio in the downsampled data.

Abstract: In accordance with embodiments of the present disclosure, systems and methods for downsampling DAS data in a way that enables accurate interpretation of acoustic events occurring in the data are provided. Such methods may be particularly useful when interpreting large sets of data, such as DAS VSP data collected during hydrocarbon recovery operations. The methods generally involve identifying data channels affected by noise from a DAS data set, and then interpolating from the surrounding data. This may improve the quality of the resulting downsampled data, with respect to the signal to noise ratio, compared to what would have occurred by merely decimating unwanted data channels. In addition, a priori information about channel fading, the desired downsampling rate, and the slowest expected elastic waves may be used to filter the DAS data. This may achieve a higher signal-to-noise ratio in the downsampled data.