The Southeast Indian Ridge between 88°E and 118°E: Variations in crustal accretion at constant spreading rate

The temperature of the mantle and the rate of melt production are parameters which play important roles in controlling the style of crustal accretion along mid-ocean ridges. To investigate the variability in crustal accretion that develops in response to variations in mantle temperature, we have conducted a geophysical investigation of the Southeast Indian Ridge (SEIR) between the Amsterdam hotspot and the Australian-Antarctic Discordance (88 degrees E-118 degrees E). The spreading center deepens by 2100 m from west to east within the study area. Despite a uniform, intermediate spreading rate (69-75 mm yr-l), the SEIR exhibits the range in axial morphology displayed by the East Pacific Rise and the Mid-Atlantic Ridge (MAR) and usually associated with variations in spreading rate. The spreading center is characterized by an axial high west of 102 degrees 45'E, whereas an axial valley is prevalent east of this longitude. Both the deepening of the ridge axis and the general evolution of axial morphology from an axial high to a rift valley are not uniform. A region of intermediate morphology separates axial highs and MAR-like rift valleys. Local transitions in axial morphology occur in three areas along the ridge axis. The increase in axial depth toward the Australian-Antarctic Discordance may be explained by the thinning of the oceanic crust by similar to 4 km and the change in axial topography. The long-wavelength changes observed along the SEIR can be attributed to a gradient in mantle temperature between regions influenced by the Amsterdam and Kerguelen hot spots and the Australian-Antarctic Discordance. However, local processes, perhaps associated with an heterogeneous mantle or along-axis asthenospheric flow, may give rise to local transitions in axial topography and depth anomalies.

The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis

Statistical approaches to study extreme events require, by definition, long time series of data. In many scientific disciplines, these series are often subject to variations at different temporal scales that affect the frequency and intensity of their extremes. Therefore, the assumption of stationarity is violated and alternative methods to conventional stationary extreme value analysis (EVA) must be adopted. Using the example of environmental variables subject to climate change, in this study we introduce the transformed-stationary (TS) methodology for non-stationary EVA. This approach consists of (i) transforming a non-stationary time series into a stationary one, to which the stationary EVA theory can be applied, and (ii) reverse transforming the result into a non-stationary extreme value distribution. As a transformation, we propose and discuss a simple time-varying normalization of the signal and show that it enables a comprehensive formulation of non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with a constant shape parameter. A validation of the methodology is carried out on time series of significant wave height, residual water level, and river discharge, which show varying degrees of long-term and seasonal variability. The results from the proposed approach are comparable with the results from (a) a stationary EVA on quasi-stationary slices of non-stationary series and (b) the established method for non-stationary EVA. However, the proposed technique comes with advantages in both cases. For example, in contrast to (a), the proposed technique uses the whole time horizon of the series for the estimation of the extremes, allowing for a more accurate estimation of large return levels. Furthermore, with respect to (b), it decouples the detection of non-stationary patterns from the fitting of the extreme value distribution. As a result, the steps of the analysis are simplified and intermediate diagnostics are possible. In particular, the transformation can be carried out by means of simple statistical techniques such as low-pass filters based on the running mean and the standard deviation, and the fitting procedure is a stationary one with a few degrees of freedom and is easy to implement and control. An open-source MAT-LAB toolbox has been developed to cover this methodology, which is available at https://github.com/menta78/tsEva/(Mentaschi et al., 2016).