Fiber-optic vector acoustic receiver based on adaptive holographic interferometer

A mobile scalar–vector acoustic receiver is proposed, experimentally implemented and investigated. The key components of the receiver are (a) the six-channel fiber-optic coil-type sensor configured as to detect three projections of acoustic intensity vector, (b) the six-channel optical phase demodulator based on six-channel adaptive holographic interferometer configured with use of dynamic holograms multiplexed in a photorefractive crystal of cadmium telluride and (c) the signals recording ADC-based system combined with software package for data processing. Field tests of the developed receiver applied for obtaining scalar and vector parameters of acoustic waves generated by a stationary and moving acoustic source in open air and water area are carried out. Experimental results show perceptiveness of use of the fiber-optical adaptive interferometry system for bearing of weak acoustic sources in real conditions.

Field validation of imaging an adjacent borehole using scattered P-waves

Acoustic waves enter a rock formation from a borehole and are reflected or scattered upon encountering a geologic structure. Consequently, we obtain the structure location represented by the azimuth and distance from the borehole using the acoustic reflection or scattering. Downhole acoustic measurements with the azimuthal resolution are realized using an azimuthal acoustic receiver sonde composed of several arcuate phased array receivers. Eight sensors distributed evenly across the arcuate phased array receiver can record acoustic waves independently; this allows us to adopt the beamforming method. We use a supporting logging tool to conduct the downhole test in two adjacent fluid-filled boreholes, for validating the evaluation of the geologic structure using scattered P-waves. The test results show the multi-azimuth images of the target borehole and the azimuthal variation in scattering amplitudes. Thus, we obtain the precise location of the target borehole. Furthermore, the measured values of the target borehole are consistent with the actual values, indicating that we can accurately evaluate a near-borehole geologic structure with scattered P-waves.

Standardizing Biologging Data for LifeWatch: Camera Traps, Acoustic Telemetry and GPS Tracking

The Research Institute for Nature and Forest (INBO) is co-managing three biologging networks as part of a terrestrial and freshwater observatory for LifeWatch Belgium. The networks are a GPS tracking network for large birds, an acoustic receiver network for fish, and a camera trap network for mammals. As part of our mission at the Open science lab for biodiversity, we are publishing the machine observations these networks generate as standardized, open data. One of the challenges however, is finding the appropriate standards and platforms to do so. In this talk, we will present the three networks, the type of biologging data they collect and how we (plan to) standardize these to specific community standards and to Darwin Core (Wieczorek et al. 2012). Data from the bird tracking network have been published in 2014 as one of the first biologging datasets on the Global Biodiversity Information Facility (GBIF) (Stienen et al. 2014). We are now planning to upload the data to Movebank instead and contribute to a generic mapping between the Movebank format and Darwin Core. Data from the acoustic receiver network are being mapped using the Darwin Core guidelines proposed by the Machine Observations Interest Group of Biodiversity Information Standards (TDWG). Images generated by the camera trap network are managed in the annotation system Agouti, for which we plan to export the data in the Camera Trap Metadata Language (Forrester et al. 2016). We also aim to write a software package to deposit camera trap images and data on Zenodo and map the observation data to Darwin Core. We hope that our work will contribute to discussions and guidelines on how to best map biologging data to Darwin Core, which is one of the aims of the Machine Observations Interest Group of Biodiversity Information Standards (TDWG).