SeaGap equips various positioning functions both for the array positioning and individual transponder positioning. The details of the functions are written in Tomita and Kido, 2024.
static_individual(): A function to estimate individual seafloor transponder positions and to model the sound speed fluctuation using 3d B-spline functions based on the concept of NTD (Nadir Total Delay). Then, static_individual estimates coefficients for the fixed number of the 3d B-spline functions as well as the individual seafloor transponder positions during each campaign through the Gauss-Newton method.
kinematic_array():
A function to estimate a horizontal array displacement for each shot group (kinematic array positioning) by Gauss-Newton method. The original kinematic array positioning method (Kido et al. (2006) and Kido et al. (2008)) estimates an array position for each acoustic ping; kinematic_array() enables us to estimate an array displacement for an user's defined shot group.
kinematic_array_3d():
A function to estimate a 3D array displacement for each shot group (kinematic array positioning) by Gauss-Newton method. This method corresponds to "the conventional method estimating uplift" in Tomita et al. (2019).
static_array():
A function to estimate static array position and temporal sound speed fluctuation (static array positioning). The temporal sound speed fluctuation modeled as NTD is generally modeled by a kind of flexible functions, such as 3d B-spline functions (e.g., Honsho & Kido, 2017). Then, static_array estimates coefficients for the fixed number of the 3d B-spline functions as well as the array position averagely during each campaign through the Gauss-Newton method. The optimial number of the 3d B-spline functions can be determined by statictical evaluation (AIC or BIC). static_array() function automatically returns the AIC and BIC values for a given number of the bases, and static_array_AICBIC() function returns the AIC and BIC values for various number of the bases.
static_array_grad():
A function to estimate static array position, temporal sound speed fluctuation (NTD), and the deep gradients (in EW & NS components). Coefficients for the fixed number of the 3d B-spline functions, the array position averagely during each campaign, and the deep gradients are estimated by the Gauss-Newton method.
static_array_mcmcgrad():
A function to estimate static array position, temporal sound speed fluctuation (NTD), the shallow gradients (in EW & NS components), and the gradient depth (equal in EW & NS components). The shallow gradients and the gradient depth are temporally constant. These parameters and hyper-parameters controlling the observational errors are estimated by MCMC. The method is orginally introduced in Tomita & Kido (2022). Note that this function is outdated from SeaGap version 1.1.0. and static_array_mcmcgradv() is recommended to be used.
static_array_mcmcgradc():
A function to estimate static array position, temporal sound speed fluctuation (NTD), the shallow gradients (in EW & NS components), and the gradient depth (equal in EW & NS components). The shallow gradients and the gradient depth are temporally constant. The shallow gradients and the gradient depth can be optionally constrained using prior distributions. These parameters and hyper-parameters controlling the observational errors are estimated by MCMC. The method is orginally introduced in Tomita & Kido (2024). Note that this function is outdated from SeaGap version 1.1.0. and static_array_mcmcgradv() is recommended to be used.
static_array_mcmcgradv():
A function to estimate static array position, temporal sound speed fluctuation (NTD), the shallow gradients (in EW & NS components), and the gradient depths (in EW & NS components). Temporal variations of the shallow gradients and the gradient depth are modeled by 3d B-Spline functions. The sound speed parameters expressing the NTD and the gradients are constrained by various prior distributions. These parameters and hyper-parameters controlling the observational errors and temporal variations of sound speed parameters are estimated by MCMC. The method is introduced in the upcoming paper.