A way to synchronize models with seismic faults for earthquake forecasting: Insights from a simple stochastic model

Numerical models are starting to be used for determining the future behaviour of seismic faults and fault networks. Their final goal would be to forecast future large earthquakes. In order to use them for this task, it is necessary to synchronize each model with the current status of the actual fault or fault network it simulates (just as, for example, meteorologists synchronize their models with the atmosphere by incorporating current atmospheric data in them). However, lithospheric dynamics is largely unobservable: important parameters cannot (or can rarely) be measured in Nature. Earthquakes, though, provide indirect but measurable clues of the stress and strain status in the lithosphere, which should be helpful for the synchronization of the models.The rupture area is one of the measurable parameters of earthquakes. Here we explore how it can be used to at least synchronize fault models between themselves and forecast synthetic earthquakes. Our purpose here is to forecast synthetic earthquakes in a simple but stochastic (random) fault model. By imposing the rupture area of the synthetic earthquakes of this model on other models, the latter become partially synchronized with the first one. We use these partially synchronized models to successfully forecast most of the largest earthquakes generated by the first model. This forecasting strategy outperforms others that only take into account the earthquake series. Our results suggest that probably a good way to synchronize more detailed models with real faults is to force them to reproduce the sequence of previous earthquake ruptures on the faults. This hypothesis could be tested in the future with more detailed models and actual seismic data.     

Radiomagnetotelluric mapping, groundwater numerical modelling and 18-Oxygen isotopic data as combined tools to determine the hydrogeological system of a landslide prone area

Three methods were combined to determine the groundwater recharge and transfer processes of a landslide prone area. First, the radiomagnetotelluric method was used to investigate the distribution of electrical resistivity (ρ) of the subsurface and build a three-dimensional model of permeability (k), through an experimental relation between ρ and k. Second, this structural model of permeability and additional climatologic data were used to fix boundary and recharge conditions to perform a three-dimensional and transient numerical simulation of the groundwater flow. Finally 18-Oxygen time series observed at the main springs were used to validate the model. This association of methods led to an improved characterization of the groundwater flow system at local scale and a better understanding of the role of this system on the landslide phenomenon. This structured approach is thought to be useful to design specific remediation strategies to drain the unstable mass.     

Bayesian Spatial Modelling of Gamma Ray Count Data

Gamma ray logging is a method routinely employed by geophysicists and environmental engineers in site geology evaluations. Modelling of gamma ray data from individual boreholes assists in the local identification of major lithological changes; modelling these data from a network of boreholes assists with lithological mapping and spatial stratigraphic correlation. In this paper we employ Bayesian spatial partition models to analyse gamma ray data spatially. In particular, a spatial partition is defined via a Voronoi tessellation and the mean intensity is assumed constant in each cell of the partition. The number of vertices generating the tessellation as well as the locations of vertices are assumed unknown, and uncertainty about these quantities is described via a hierarchical prior distribution. We describe the advantages of the spatial partition modelling approach in the context of smoothing gamma ray count data and describe an implementation that may be extended to the fitting of a more general model than a constant mean within each cell of the partition. As an illustration of the methodology we consider a data set collected from a network of eight boreholes, which is part of a geophysical study to assist in mapping the lithology of a site. Gamma ray logs are linked with geological information from cores and the spatial analysis of log data assists with predicting the lithology at unsampled locations.     

Re-evaluation of focal depths and source mechanisms of selected earthquakes in the Afar depression

We present a stepwise inversion procedure to assess the focal depth and model earthquake source complexity of seven moderate-sized earthquakes  (6.2 > M _w > 5.1)  that occurred in the Afar depression and the surrounding region. The Afar depression is a region of highly extended and intruded lithosphere, and zones of incipient seafloor spreading. A time-domain inversion of full moment tensor was performed to model direct P and SH waves of teleseismic data. Waveform inversion of the selected events estimated focal depths in the range of 17–22 km, deeper than previously published results. This suggests that the brittle–ductile transition zone beneath parts of the Afar depression extends more than 22 km. The effect of near-source velocity structure on the moment tensor elements was also investigated and was found to respond little to the models considered. Synthetic tests indicate that the size of the estimated, non-physical, non-isotropic source component is rather sensitive to incorrect depth estimation. The dominant double couple part of the moment tensor solutions for most of the events indicates that their occurrence is mainly due to shearing. Parameters associated with source directivity (rupture velocity and azimuth) were also investigated. Re-evaluation of the analysed events shows predominantly normal faulting consistent with the relative plate motions in the region.     

Driving forces of tropical deforestation: The role of remote sensing and spatial models

Remote sensing technologies are increasingly used to monitor landscape change in many parts of the world. While the availability of extensive and timely imagery from various satellite sensors can aid in identifying the rates and patterns of deforestation, modelling techniques can evaluate the socioeconomic and biophysical forces driving deforestation processes. This paper briefly reviews some emerging spatial methodologies aimed at identifying driving forces of land use change and applies one such methodology to understand deforestation in Mexico. Satellite image classification, change analysis and econometric modelling are used to identify the rates, hotspots and drivers of deforestation in a case study of the southern Yucatán peninsular region, an enumerated global hotspot of biodiversity and tropical deforestation. In particular, the relative roles of biophysical and socioeconomic factors in driving regional deforestation rates are evaluated. Such methodological approaches can be applied to other regions of the forested tropics and contribute insights to conservation planning and policy.     

Occurrence of salt water above fresh water in dynamic equilibrium in a coastal groundwater flow system near De Panne, Belgium

A salt water lens is found above fresh water under the shore between Dunkerque (France) and Nieuwpoort (Belgium). This inverse density distribution is in a dynamic equilibrium. It develops due to the infiltration of salt water on the back shore during high tide. Under this salt water lens, water infiltrated in the adjacent dune area flows towards the sea and discharges at the seabed. This water quality distribution differs from the classic salt water wedge under fresh water described in the literature. Here, the evolution to this water quality distribution is simulated with a density dependent numerical model. A large tidal range, shore morphology and a permeable groundwater reservoir are the main conditions for the observed water quality distribution. By altering these conditions, intermediate water quality distributions between the classic salt water wedge and the one discussed here develop. Based on these simulations, it is expected that similar kinds of inverse density distribution could be present in a number of coastal areas, which have tides, a gently sloping shore and a permeable substratum.