Possibility of short-term probabilistic forecasts for large earthquakes making good use of the limitations of existing catalogs

Abstract

Earthquakes are quite hard to predict. One of the possible reasons can be the fact that the existing catalogs of past earthquakes are limited at most to the order of 100 years, while their characteristic time scale is sometimes greater than that time span. Here we rather use these limitations positively and characterize some large earthquake events as abnormal events that are not included there. When we constructed probabilistic forecasts for large earthquakes in Japan based on similarity and difference to their past patterns-which we call known and unknown abnormalities, respectively-our forecast achieved probabilistic gains of 5.7 and 2.4 against a time-independent model for main shocks with the magnitudes of 7 or above. Moreover, the two abnormal conditions covered 70% of days whose maximum magnitude was 7 or above.

Robustness and versatility of a nonlinear interdependence method for directional coupling detection from spike trains

“…Making use of the modularity of the approach based on the measure L can give complementary information about interdependence, and this may be useful to discriminate real from spurious or indirect connections. Moreover, while here we have focused on neuronal signals, the measure L seems to be a very promising tool also for applications outside neuroscience, for example in climatology [56,57] or earthquake prediction [58].…”

Twenty years after “The Impact of Chaos”: what have been achieved and what should be answered

“…Hence, a series of events can be found universally, while there were not good ways for analyzing them. Thus, we have created an approach [21][22][23], where we prepare a time window of fixed length. Second, we slide the time window by a fixed amount of time repeatedly to decide the positions of the time window.…”

Earthquake alerting based on spatial geodetic data by spatiotemporal information transformation learning

“…Some studies attempted to alert for earthquakes by using time series of seismic electric signal data ( 6 , 15 ). However, while most of these neural network methods use various kinds of features as inputs to predict the timing and/or magnitude of earthquakes, few of them consider the spatial relations such as surface deformations of multiple regions ( 16 ).…”