Development of GNSS-A analysis tool “GARPOS” and its application to the seafloor geodesy around Japan

Abstract:

Global Navigation Satellite System – Acoustic ranging combined seafloor geodetic technique (GNSS-A) has extended the geodetic observation network into the ocean. The technique had been developed by several research groups in the United States and Japan since 1990s. The Japan Coast Guard practicalized and repeatedly operated the GNSS-A observation to detect the seafloor displacements due to the inter-, co-, and post-seismic crustal deformation in the Japan Trench and the Nankai Trough (e.g., Sato et al., 2011; Watanabe et al., 2014; Yokota et al., 2016). The key issue for analyzing the GNSS-A data is how to suppress the effect of sound speed variation in the seawater, without overestimation. Recent studies pointed out the importance to model the spatial gradient of sound speed perturbation (e.g., Yokota et al., 2019; Yasuda et al., 2017; Honsho et al., 2019). Thus, we first reconstruct the generalized observation equation which approximately includes the practical solutions in the previous studies as special cases. We also developed a method to directly extract the gradient sound speed structure by introducing the appropriate statistical properties in the observation equations, especially the data correlation term. In the proposed scheme, we calculate the posterior probability based on the empirical Bayes approach using the Akaike’s Bayesian Information Criterion (ABIC; Akaike, 1980) for model selection. This approach enabled us to suppress the overfitting of sound speed variables and thus to extract simpler sound speed field and stable seafloor positions from the GNSS-A dataset. We implemented the proposed method in the Python-based software “GARPOS” (GNSS-Acoustic Ranging combined POsitioning Solver). We will present the details of our method and features of “GARPOS” as well as the application to the actual data. The software “GARPOS” is planned to be published in the open repository.