An Ising model for earthquake dynamics

This paper focuses on extracting the information contained in seismic space-time patterns and their dynamics. The Greek catalog recorded from 1901 to 1999 is analyzed. An Ising Cellular Automata representation technique is developed to reconstruct the history of these patterns. We find that there is strong correlation in the region, and that small earthquakes are very important to the stress transfers. Finally, it is demonstrated that this approach is useful for seismic hazard assessment and intermediate-range earthquake forecasting

Using earthquake intensities to forecast earthquake occurrence times

International audienceIt is well known that earthquakes do not occur randomly in space and time. Foreshocks, aftershocks, precursory activation, and quiescence are just some of the patterns recognized by seismologists. Using the Pattern Informatics technique along with relative intensity analysis, we create a scoring method based on time dependent relative operating characteristic diagrams and show that the occurrences of large earthquakes in California correlate with time intervals where fluctuations in small earthquakes are suppressed relative to the long term average. We estimate a probability of less than 1% that this coincidence is due to random clustering. Furthermore, we show that the methods used to obtain these results may be applicable to other parts of the world

3D multi-source model of elastic volcanic ground deformation

Developments in Interferometric Synthetic Aperture Radar (InSAR) and GNSS (Global Navigation Satellite System) during the past decades have promoted significant advances in geosciences, providing high-resolution ground deformation data with dense spatio-temporal coverage. This large dataset can be exploited to produce accurate assessments of the primary processes occurring in geologically active areas. We present a new, original methodology to carry out a multi-source inversion of ground deformation data to better understand the subsurface causative processes. A nonlinear approach permits the determination of location, size and three-dimensional configuration, without any a priori assumption as to the number, nature or shape of the potential sources. The proposed method identifies a combination of pressure bodies and different types of dislocation sources (dip-slip, strike-slip and tensile) that represent magmatic sources and other processes such as earthquakes, landslides or groundwater-induced subsidence through the aggregation of elemental cells. This approach has the following features: (1) simultaneous inversion of the deformation components and/or line-of-sight (LOS) data; (2) simultaneous determination of diverse structures such as pressure bodies or dislocation sources, representing local and regional effects; (3) a fully 3D context; and (4) no initial hypothesis about the number, geometry or types of the causative sources is necessary. This methodology is applied to Mt. Etna (Southern Italy). We analyze the ground deformation field derived from a large InSAR dataset acquired during the January 2009 – June 2013 time period. The application of the inversion approach models several interesting buried structures as well as processes related to the volcano magmatic plumbing system, local subsidence within the Valle del Bove and seaward motion of eastern flank of the volcano

Earthquake forecasting and its verification

No proven method is currently available for the reliable short time prediction of earthquakes (minutes to months). However, it is possible to make probabilistic hazard assessments for earthquake risk. In this paper we discuss a new approach to earthquake forecasting based on a pattern informatics (PI) method which quantifies temporal variations in seismicity. The output, which is based on an association of small earthquakes with future large earthquakes, is a map of areas in a seismogenic region ('hotspots'') where earthquakes are forecast to occur in a future 10-year time span. This approach has been successfully applied to California, to Japan, and on a worldwide basis. Because a sharp decision threshold is used, these forecasts are binary--an earthquake is forecast either to occur or to not occur. The standard approach to the evaluation of a binary forecast is the use of the relative (or receiver) operating characteristic (ROC) diagram, which is a more restrictive test and less subject to bias than maximum likelihood tests. To test our PI method, we made two types of retrospective forecasts for California. The first is the PI method and the second is a relative intensity (RI) forecast based on the hypothesis that future large earthquakes will occur where most smaller earthquakes have occurred in the recent past. While both retrospective forecasts are for the ten year period 1 January 2000 to 31 December 2009, we performed an interim analysis 5 years into the forecast. The PI method out performs the RI method under most circumstances

Earthquake Statistics in Models and Data

Earthquake Statistics in Models and Dat

A simple metric to quantify seismicity clustering

The Thirulamai-Mountain (TM) metric was first developed to study ergodicity in fluids and glasses (Thirumalai and Mountain, 1993) using the concept of effective ergodicity, where a large but finite time interval is considered. Tiampo et al. (2007) employed the TM metric to earthquake systems to search for effective ergodic periods, which are considered to be metastable equilibrium states that are disrupted by large events. The physical meaning of the TM metric for seismicity is addressed here in terms of the clustering of earthquakes in both time and space for different sets of data. It is shown that the TM metric is highly dependent not only on spatial/temporal seismicity clustering, but on the past seismic activity of the region and the time intervals considered as well, and that saturation occurs over time, resulting in a lower sensitivity to local clustering. These results confirm that the TM metric can be used to quantify seismicity clustering from both spatial and temporal perspectives, in which the disruption of effective ergodic periods are caused by the agglomeration of events

Gravity changes from a stress evolution earthquake simulation of California

The gravity signal contains information regarding changes in density at all depths and can be used as a proxy for the strain accumulation in fault networks. A stress evolution time-dependent model was used to create simulated slip histories over the San Andreas Fault network in California. Using a linear sum of the gravity signals from each fault segment in the model, via coseismic gravity Green's functions, a time-dependent gravity model was created. The steady state gravity from the long-term plate motion generates a signal over 5 years with magnitudes of ±~2 μGal; the current limit of portable instrument observations. Moderate to large events generate signal magnitudes in the range of ~10 to ~80 μGal, well within the range of ground-based observations. The complex fault network geometry of California significantly affects the spatial extent of the gravity signal from the three events studied.Peer reviewe

Using earthquake intensities to forecast earthquake occurrence times

It is well known that earthquakes do not occur randomly in space and time. Foreshocks, aftershocks, precursory activation, and quiescence are just some of the patterns recognized by seismologists. Using the Pattern Informatics technique along with relative intensity analysis, we create a scoring method based on time dependent relative operating characteristic diagrams and show that the occurrences of large earthquakes in California correlate with time intervals where fluctuations in small earthquakes are suppressed relative to the long term average. We estimate a probability of less than 1% that this coincidence is due to random clustering. Furthermore, we show that the methods used to obtain these results may be applicable to other parts of the world

Detection of displacements on Tenerife Island, Canaries, using radar interferometry

Tenerife is one of the most well monitored islands of the Canaries, but the surveillance generally is centred on Las Canadas Caldera, where the Teide volcano is located. In the last 180 000 yr, the eruptions on Tenerife Island have never occurred in the same volcanic structure, except for the Teide and Pico Viejo central volcanic system, so that a complete monitoring network would have to cover the whole island. As a result, Synthetic Aperture Radar Interferometry (InSAR) is being used on Tenerife, because this space technique can provide a displacement map of the surface of the earth with centimetre precision. This paper presents the results obtained on Tenerife Island using 18 SAR images acquired by the ERS-1 and ERS-2 satellites during the period 1992-2000. Two important results have been obtained: no deformation on Las Canadas Caldera, coinciding with results obtained using terrestrial techniques, and two subsidence episodes outside monitoring areas in the NW of the island, in the region of the last historic eruptions. These results show that InSAR is a useful technique for monitoring the entire island, thus allowing us to discover deformations in areas that are not routinely or easily monitored. This technique has been used in combination with Global Positioning System (GPS) observation of a global network on the island to define a new geodetic monitoring system. The possible causes of the deformations observed have been studied in an endeavour to discern if they might be of natural origin, in particular linked to a reactivation of prior volcanic activity. Examination of the geophysical observations on the island, human activities underway and the results of the modelling seem to indicate that at least part of the deformations may be caused by changes in the groundwater level and therefore are not linked to a volcanic reactivation. This result is important because it implies that, if geodetic volcano monitoring is to be performed on the island, the system used must be capable of discerning between various possible origins of the deformation by analysing their patterns and ancillary information from other sources. In this regard, InSAR is a basic tool on account of its unpaired wide area coverage and spatial density