BackgroundSuicide clustering occurs when multiple suicide incidents take place in a small area or/and within a short period of time. In spite of the multi-national research attention and particular efforts in preparing guidelines for tackling suicide clusters, the broader picture of epidemiology of suicide clustering remains unclear. This study aimed to develop techniques in using scan statistics to detect clusters, with the detection of suicide clusters in Australia as example.Methods and FindingsScan statistics was applied to detect clusters among suicides occurring between 2004 and 2008. Manipulation of parameter settings and change of area for scan statistics were performed to remedy shortcomings in existing methods. In total, 243 suicides out of 10,176 (2.4%) were identified as belonging to 15 suicide clusters. These clusters were mainly located in the Northern Territory, the northern part of Western Australia, and the northern part of Queensland. Among the 15 clusters, 4 (26.7%) were detected by both national and state cluster detections, 8 (53.3%) were only detected by the state cluster detection, and 3 (20%) were only detected by the national cluster detection.ConclusionsThese findings illustrate that the majority of spatial-temporal clusters of suicide were located in the inland northern areas, with socio-economic deprivation and higher proportions of indigenous people. Discrepancies between national and state/territory cluster detection by scan statistics were due to the contrast of the underlying suicide rates across states/territories. Performing both small-area and large-area analyses, and applying multiple parameter settings may yield the maximum benefits for exploring clusters.

Two months after the 2016 Amatrice earthquake (AE), a strong (~M6) earthquake (Visso earthquake, VE) struck the town Visso, Italy, 20 km north of the AE epicenter. Between these two events, the aftershocks migrated gradually toward to the VE epicenter at a rate of ~0.4 km/d, indicating propagation of pore pressure front. We use finite element models to simulate the postseismic fully coupled poroelastic response. The results show that the pore fluid flows (up to 50 nm/s) both horizontally and vertically into the VE hypocenter since the AE and destabilized the area with extra ~70% of Coulomb failure stress. Majority of nearby aftershocks (>80%) tend to cluster within the zones of coseismic depressurization where fluid flow converges. A maximum ΔCFS of ~35 kPa is calculated at the VE hypocenter during its rupture by a crustal permeability, 10–16 ± 0.7 m2, suggesting that an intermediately fractured crust allows maximum rupture tendency for the VE during poroelastic fluid recovery.

We derive a coseismic slip model of the 2015 Mw7.8 Gorkha earthquake on the basis of GPS and line‐of‐sight displacements from ALOS‐2 descending interferograms, using Green's functions calculated with a 3‐D finite element model (FEM). The FEM simulates a nonuniform distribution of elastic material properties and a precise geometric configuration of the irregular topographical surface. The rupturing fault is modeled as a low‐angle and north dipping surface within the Main Frontal Thrust along the convergent margin of the Himalayas. The optimal model that inherits heterogeneous material properties provides a significantly better solution than that in a homogenous domain at the 95% confidence interval. The best fit solution for the domain having a nonuniform distribution of material properties reveals a rhombus‐shaped slip zone of three composite asperities. Slip is primarily concentrated at a depth of 15 km with both dip‐slip (maximum 6.54 m) and strike‐slip (maximum 2.0 m) components, giving rise to a geodetic‐based moment of 1.09 × 1021 Nm in general agreement with the seismological estimate. The optimal relative weights among GPS and interferometric synthetic aperture radar (InSAR) are deduced from a new method, MC‐HVCE which combines a Monte Carlo search and a Helmert Method of Variance Components Estimation. This method determines the relative weights in a systemic approach which preserves the intrinsic solution smoothness. The joint solution is significantly better than those inverted from each individual data set. This methodology allows us to integrate multiple data sets of geodetic observations with seismic tomography, in an effort to achieve a better understanding of seismic ruptures within crustal heterogeneity.

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The eruption cycle of a volcano is controlled in part by the upward migration of magma. The characteristics of the magma flux produce a deformation signature at the Earth's surface. Inverse analyses use geodetic data to estimate strategic controlling parameters that describe the position and pressurization of a magma chamber at depth. The specific distribution of material properties controls how observed surface deformation translates to source parameter estimates. Seismic tomography models describe the spatial distributions of material properties that are necessary for accurate models of volcano deformation. This study investigates how uncertainties in seismic tomography models propagate into variations in the estimates of volcano deformation source parameters inverted from geodetic data. We conduct finite element model‐based nonlinear inverse analyses of interferometric synthetic aperture radar (InSAR) data for Okmok volcano, Alaska, as an example. We then analyze the estimated parameters and their uncertainties to characterize the magma chamber. Analyses are performed separately for models simulating a pressurized chamber embedded in a homogeneous domain as well as for a domain having a heterogeneous distribution of material properties according to seismic tomography. The estimated depth of the source is sensitive to the distribution of material properties. The estimated depths for the homogeneous and heterogeneous domains are 2666 ± 42 and 3527 ± 56 m below mean sea level, respectively (99% confidence). A Monte Carlo analysis indicates that uncertainties of the seismic tomography cannot account for this discrepancy at the 99% confidence level. Accounting for the spatial distribution of elastic properties according to seismic tomography significantly improves the fit of the deformation model predictions and significantly influences estimates for parameters that describe the location of a pressurized magma chamber.