Influence of Time and Space Correlations on Earthquake Magnitude (original) (raw)
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On the influence of time and space correlations on the next earthquake magnitude
2007
A crucial point in the debate on feasibility of earthquake prediction is the dependence of an earthquake magnitude from past seismicity. Indeed, whilst clustering in time and space is widely accepted, much more questionable is the existence of magnitude correlations. The standard approach generally assumes that magnitudes are independent and therefore in principle unpredictable. Here we show the existence of clustering in magnitude: earthquakes occur with higher probability close in time, space and magnitude to previous events. More precisely, the next earthquake tends to have a magnitude similar but smaller than the previous one. A dynamical scaling relation between magnitude, time and space distances reproduces the complex pattern of magnitude, spatial and temporal correlations observed in experimental seismic catalogs.
The earthquake magnitude is influenced by previous seismicity
Geophysical Research Letters, 2012
Seismic occurrence is characterized by clustering in space, time 3 and magnitude. Correlations between magnitudes of subsequent events have 4 been recently attributed to catalog incompleteness. Here we investigate the 5 effect of catalog completeness on the amplitude of magnitude correlations. 6 The analysis of two California regions with different levels of catalog accu-7 racy and different lower magnitude thresholds indicate that the amplitude 8 of correlations does not depend on catalog incompleteness. Conversely, cor-9 relations are controlled by the probability that two events belong to the same 10 mainshock-aftershock sequence. Numerical simulations of the ETAS model, 11
Dependence of earthquake recurrence times and independence of magnitudes on seismicity history
Tectonophysics, 2006
The fulfillment of a scaling law for earthquake recurrence-time distributions is a clear indication of the importance of correlations in the structure of seismicity. In order to characterize these correlations we measure conditional recurrence-time and magnitude distributions for worldwide seismicity as well as for Southern California during stationary periods. Disregarding the spatial structure, we conclude that the relevant correlations in seismicity are those of the recurrence time with previous recurrence times and magnitudes; in the latter case, the conditional distribution verifies a scaling relation depending on the difference between the magnitudes of the two events defining the recurrence time. In contrast, with our present resolution, magnitude seems to be independent on the history contained in the seismic catalogs (except perhaps for Southern California for very short time scales, less than about 30 min for the magnitude ranges analyzed).
Increased correlation range of seismicity before large events manifested by earthquake chains
Tectonophysics, 2006
Earthquake chains" are clusters of moderate-size earthquakes which extend over large distances and are formed by statistically rare pairs of events that are close in space and time ("neighbors"). Earthquake chains are supposed to be precursors of large earthquakes with lead times of a few months. Here we substantiate this hypothesis by mass testing it using a random earthquake catalog. Also, we study stability under variation of parameters and some properties of the chains. We found two invariant parameters: they characterize the spatial and energy scales of earthquake correlation. Both parameters of the chains show good correlation with the magnitudes of the earthquakes they precede. Earthquake chains are known as the first stage of the earthquake prediction algorithm reverse tracing of precursors (RTP) now tested in forward prediction. A discussion of the complete RTP algorithm is outside the scope of this paper, but the results presented here are important to substantiate the RTP approach.
Spatial and Temporal Properties for Same Series of Relatively Strong Earthquakes
AIP Conference Proceedings, 2010
Series of relatively large earthquakes in different regions of the Earth are studied. The regions chooses are of a high seismic activity and has a good contemporary network for recording of the seismic events along them. The main purpose of this investigation is the attempt to describe analytically the seismic process in the space and time. We are considering the statistical distributions the distances and the times between consecutive earthquakes (so called pair analysis). Studies conducted on approximating the statistical distribution of the parameters of consecutive seismic events indicate the existence of characteristic functions that describe them best. Such a mathematical description allows the distributions of the examined parameters to be compared to other model distributions.
2013
It is now accepted that the active tectonic grain comprises a self-organized complex system, therefore its expression (seismicity) should be manifested in the temporal and spatial statistics of energy release rates, and exhibit memory due to long-range interactions in a fractal-like space-time. Such attributes can be properly understood in terms of Non-Extensive Statistical Physics. In addition to energy release rates expressed by the magnitude M, measures of the temporal and spatial interactions are the time (Δt) and hypocentral distance (Δd) between consecutive events. Recent work indicated that if the distributions of M, Δt and Δd are independent so that the joint probability p(M, Δt, Δd) factorizes as p(M) p(Δt) p(Δd), earthquake frequency is related to M, Δt and Δd by well defined power-laws consistent with NESP. The present work applies these concepts to investigate the self-organization and temporal/spatial dynamics of North Californian seismicity. The results indicate that the statistical behaviour of seismicity in this area is consistent with NESP predictions and has attributes of universality, as its holds for a very broad range of spatial, temporal and magnitude scales. They also indicate that the expression of the regional active tectonic grain comprises a mixture of processes significantly dependent on Δd, which include near (<100km) and far (>400km) field interactions.
Scientific reports, 2018
Since the beginning of the 1980s, when Mandelbrot observed that earthquakes occur on 'fractal' self-similar sets, many studies have investigated the dynamical mechanisms that lead to self-similarities in the earthquake process. Interpreting seismicity as a self-similar process is undoubtedly convenient to bypass the physical complexities related to the actual process. Self-similar processes are indeed invariant under suitable scaling of space and time. In this study, we show that long-range dependence is an inherent feature of the seismic process, and is universal. Examination of series of cumulative seismic moment both in Italy and worldwide through Hurst's rescaled range analysis shows that seismicity is a memory process with a Hurst exponent H ≈ 0.87. We observe that H is substantially space- and time-invariant, except in cases of catalog incompleteness. This has implications for earthquake forecasting. Hence, we have developed a probability model for earthquake occur...
Space-time correlation of earthquakes
Geophysical Journal International, 2008
Seismicity is a complex process featuring non-trivial space-time correlations in which several forms of scale invariance have been identified. A frequently used method to detect scaleinvariant features is the correlation integral, which leads to the definition of a correlation dimension separately in space and time. In this paper, we generalize this method with the definition of a space-time combined correlation integral. This approach allows us to analyse medium-strong seismicity as a point process, without any distinction among main, after or background shocks. The analyses performed on the catalogue of worldwide seismicity and the corresponding reshuffled version strongly suggest that earthquakes of medium-large magnitude are time clustered inside specific space-time regions. On the basis of this feature, we recognize a space-time domain statistically characterized by sequences' behaviour and a domain of temporal randomness. Then, focusing on the spatial distribution of hypocentres, we find another domain confined to short distances and characterized by a relatively high degree of spatial correlation. This spatial domain slowly increases with time: we interpret this as the 'afterevent' zone representing the set of all subsequent events located very near (about 30 km) to each reference earthquake and embedded on specific seismogenic structures such as faults planes.
Since the beginning of the 1980s, when Mandelbrot observed that earthquakes occur on 'fractal' self-similar sets, many studies have investigated the dynamical mechanisms that lead to self-similarities in the earthquake process. Interpreting seismicity as a self-similar process is undoubtedly convenient to bypass the physical complexities related to the actual process. Self-similar processes are indeed invariant under suitable scaling of space and time. In this study, we show that long-range dependence is an inherent feature of the seismic process, and is universal. Examination of series of cumulative seismic moment both in Italy and worldwide through Hurst's rescaled range analysis shows that seismicity is a memory process with a Hurst exponent H ≈ 0.87. We observe that H is substantially space-and time-invariant, except in cases of catalog incompleteness. This has implications for earthquake forecasting. Hence, we have developed a probability model for earthquake occurrence that allows for long-range dependence in the seismic process. Unlike the Poisson model, dependent events are allowed. This model can be easily transferred to other disciplines that deal with self-similar processes.
Analysis of the Spatial Distribution Between Successive Earthquakes
Physical Review Letters, 2005
The earthquake spatial distribution is being studied, using earthquake catalogs from different seismic regions (California, Canada, Central Asia, Greece, and Japan). The quality of the available catalogs, taking into account the completeness of the magnitude, is examined. Based on the analysis of the catalogs, it was determined that the probability densities of the inter-event distance distribution collapse into single distribution when the data is rescaled. The collapse of the data provides a clear illustration of earthquake-occurrence self-similarity in space.