Perspectives on Clustering and Declustering of Earthquakes

Clustering is a fundamental feature of earthquakes that impacts basic and applied analyses of seismicity. Events included in the existing short-duration instrumental catalogs are concentrated strongly within a very small fraction of the space–time volume, which is highly amplified by activity associated with the largest recorded events. The earthquakes that are included in instrumental catalogs are unlikely to be fully representative of the long-term behavior of regional seismicity. We illustrate this and other aspects of space–time earthquake clustering, and propose a quantitative clustering measure based on the receiver operating characteristic diagram. The proposed approach allows eliminating effects of marginal space and time inhomogeneities related to the geometry of the fault network and regionwide changes in earthquake rates, and quantifying coupled space–time variations that include aftershocks, swarms, and other forms of clusters. The proposed measure is used to quantify and compare earthquake clustering in southern California, western United States, central and eastern United States, Alaska, Japan, and epidemic-type aftershock sequence model results. All examined cases show a high degree of coupled space–time clustering, with the marginal space clustering dominating the marginal time clustering. Declustering earthquake catalogs can help clarify long-term aspects of regional seismicity and increase the signal-to-noise ratio of effects that are subtler than the strong clustering signatures. We illustrate how the high coupled space–time clustering can be decreased or removed using a data-adaptive parsimonious nearest-neighbor declustering approach, and emphasize basic unresolved issues on the proper outcome and quality metrics of declustering. At present, declustering remains an exploratory tool, rather than a rigorous optimization problem, and selecting an appropriate declustering method should depend on the data and problem at hand.

314Data-adaptive methods for high-dimensional mediation analysis: Application to a randomised trial of tuberculosis vaccination

Focus of Presentation Statistical methods for causal mediation analysis are useful for understanding the pathways by which a certain treatment or exposure impacts health outcomes. Existing methods necessitate modelling of the distribution of the mediators, which quickly becomes infeasible when mediators are high-dimensional (e.g., biomarkers). We propose novel data-adaptive methods for estimating the indirect effect of a randomised treatment that acts via a pathway represented by a high-dimensional set of measurements. This work was motivated by the Melbourne Infant Study: BCG for Allergy and Infection Reduction (MIS BAIR), a randomised controlled trial investigating the effect of neonatal tuberculosis vaccination on clinical allergy and infection outcomes, and its mechanisms of action. Findings The proposed methods are doubly robust, which allows us to achieve (uniformly) valid statistical inference, even when machine learning algorithms are used for the two required models. We illustrate these in the context of the MIS BAIR study, investigating the mediating role of immune pathways represented by a high-dimensional vector of cytokine responses under various stimulants. We confirm adequate performance of the proposed methods in an extensive simulation study. Conclusions/Implications The proposed methods provide a feasible and flexible analytic strategy for examining high-dimensional mediators in randomised controlled trials. Key messages Data-adaptive methods for mediation analysis are desirable in the context of high-dimensional mediators, such as biomarkers. We propose novel doubly robust methods, which enable valid statistical inference when using machine learning algorithms for estimation.

In situ measurements of directional wave spectra from an unmanned aerial system

This Coastal and Hydraulics Engineering Technical Note (CHETN) describes the ability to measure the directional-frequency spectrum of sea surface waves based on the motion of a floating unmanned aerial system (UAS). The UAS used in this effort was custom built and designed to land on and take off from the sea surface. It was deployed in the vicinity of an operational wave sensor, the 8 m* array, at the US Army Engineer Research and Development Center (ERDC), Field Research Facility (FRF) in Duck, NC. While on the sea surface, an inertial navigation system (INS) recorded the response of the UAS to the incoming ocean waves. Two different INS signals were used to calculate one-dimensional (1D) frequency spectra and compared against the 8 m array. Two-dimensional (2D) directional-frequency spectra were calculated from INS data using traditional single-point-triplet analysis and a data adaptive method. The directional spectrum compared favorably against the 8 m array.