A joint electromagnetic and seismic study of an active pockmark within the hydrate stability field at the Vestnesa Ridge, West Svalbard margin

We acquired coincident marine controlled-source electromagnetic (CSEM), high-resolution seismic reflection and ocean-bottom seismometer (OBS) data over an active pockmark in the crest of the southern part of the Vestnesa Ridge, to estimate fluid composition within an underlying fluid-migration chimney. Synthetic model studies suggest resistivity obtained from CSEM data can resolve gas or hydrate saturation greater than 5% within the chimney. Acoustic chimneys imaged by seismic reflection data beneath the pockmark and on the ridge flanks, were found to be associated with high-resistivity anomalies (+2-4 m). High-velocity anomalies (+0.3 km/s), within the gas hydrate stability zone (GHSZ) and low-velocity anomalies (-0.2 km/s) underlying the GHSZ, were also observed. Joint analysis of the resistivity and velocity anomaly indicates pore saturation of up to 52% hydrate with 28% free gas, or up to 73% hydrate with 4% free gas, within the chimney beneath the pockmark assuming a non-uniform and uniform fluid distribution respectively. Similarly, we estimate up to 30% hydrate with 4% free gas or 30% hydrate with 2% free gas within the pore space of the GHSZ outside the central chimney assuming a non-uniform and uniform fluid distribution respectively. High levels of free-gas saturation in the top part of the chimney are consistent with episodic gas venting from the pockmark.

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).