Deep Learning-Based DAS to Geophone Data Transformation

Seismic data are the primary way to study the subsurface structure and properties. A conventional seismic sensor like geophone or accelerometer measures particle velocity or acceleration at a local point only, while distributed acoustic sensing (DAS) measures dynamic strain along the fiber optic cable at densely spaced sample points, where strain rate is obtained over certain gauge length interval. Therefore, DAS measures subsurface properties with high sampling resolution and large coverage. When an optical fiber is installed in a well, DAS can provide continuous, dense downhole recording. However, currently, most of the seismic processing, imaging, and inversion techniques are developed for geophone data. These well-established techniques can be readily and properly utilized if DAS data are transformed into geophone measurements, such as particle velocity. In this study, we present a recurrent neural network (RNN) framework to perform this transformation. This effectiveness of the deep learning-based mapping is then demonstrated with a field measurement data, showing that DAS data can be transformed into particle velocity accurately and robustly using the proposed deep-learning approach.