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Abstract:
Distributed Acoustic Sensing (DAS), with its high-spatial and temporal resolution, low cost-per-sensor, and versatility, could transform how future geophysical surveys and network monitoring are performed, both on land and subsea. DAS, however, is still a relatively new sensing technology and features such as sensor response, calibration, and impact of installation environment are active areas of research. The need to compare measurements across deployments and make the data reusable by others warrants a standardization of DAS metadata in a machine-readable format. The capability to facilitate high-performance computing in a cloud environment is also highly desirable.Long-standing metadata models developed for seismic data e.g., SEED, SAC or SEG-Y do not adapt well for DAS due to fundamental differences in sensor and data acquisition parameters. There may be thousands of measurement locations in one experiment, the installation environment can vary significantly along the cable, data acquisition equipment and parameters can be changed during an experiment or between repeated surveys. The optical fiber itself has intrinsic properties that influence measurements, and multiple fibers might be used over the course of an experiment or long-term monitoring project. We outline a proposed metadata model that could accommodate most deployment scenarios. The model can be represented in a JSON format but could be readily translated to XML and incorporated into hierarchical data formats such as HDF5 or Zarr. The model aims to enhance the interoperability of DAS data sets, maintain compatibility with more complex industry standards such as ProdML, and build reproducibility into DAS data products.
