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 detection capacity of Dense Oceanfloor Network system for Earthquakes and Tsunamis (DONET)

We adopted the Probability-based Magnitude of Completeness (PMC) method and performed a case analysis of the Nankai Trough, a target region monitored for future megathrust earthquakes. JAMSTEC (Japan Agency for Marine-Earth Science and Technology) has created a seismicity catalog that includes events in this region observed by DONET. Using seismicity data for 2015-2019, we found spatiotemporal variability of completeness magnitude Mp. Mp was lower than 1 in one of the areas where stations are densely deployed, whereas Mp was larger than 2 at the periphery and outside of the DONET area. We then evaluated the temporal evolution of Mp, highlighting how the failure of sets of observing stations influenced Mp if not repaired. Stations are aggregated around the 12 science nodes (hubs that connect the stations) and connected through the two oceanfloor backbone cables to JAMSTEC. We explored the possible use of PMC as a tool with simulation computation of node malfunction. A simulation showed that completeness estimates in the area near failure nodes were about 1 magnitude larger. If such failure occurred for nodes near the region which straddles the rupture zones of the previous Tonankai and Nankai earthquakes in 1940's, it would most pronouncedly affect earthquake monitoring among nodes' failures. It is desirable to repair these nodes or replace with new ones when their malfunction occurs. We then demonstrated an example of how to use Mp information as prior knowledge to seismicity-related studies. We used the b value of the Gutenberg-Richter distribution, and computed it taking Mp into consideration. We found that the spatial and temporal changes in b were strongly correlated to the magnitude-6 class slow slip that grew over two years on the Nankai Trough plate boundary, indicating the b value as a proxy that can help to image stress heterogeneity when there is a slow slip event.Comment: 6 figure

Earthquake detection capability of the Swiss Seismic Network

A reliable estimate of completeness magnitudes is vital for many seismicity- and hazard-related studies. Here we adopted and further developed the Probability-based Magnitude of Completeness (PMC) method. This method determines network detection completeness (MP) using only empirical data: earthquake catalogue, phase picks and station information. To evaluate the applicability to low- or moderate-seismicity regions, we performed a case study in Switzerland. The Swiss Seismic Network (SSN) at present is recording seismicity with one of the densest networks of broad-band sensors in Europe. Based on data from 1983 January 1 to 2008 March 31, we found strong spatio-temporal variability of network completeness: the highest value of MP in Switzerland at present is 2.5 in the far southwest, close to the national boundary, whereas MP is lower than 1.6 in high-seismicity areas. Thus, events of magnitude 2.5 can be detected in all of Switzerland. We evaluated the temporal evolution of MP for the last 20 yr, showing the successful improvement of the SSN. We next introduced the calculation of uncertainties to the probabilistic method using a bootstrap approach. The results show that the uncertainties in completeness magnitudes are generally less than 0.1 magnitude units, implying that the method generates stable estimates of completeness magnitudes. We explored the possible use of PMC: (1) as a tool to estimate the number of missing earthquakes in moderate-seismicity regions and (2) as a network planning tool with simulation computations of installations of one or more virtual stations to assess the completeness and identify appropriate locations for new station installations. We compared our results with an existing study of the completeness based on detecting the point of deviation from a power law in the earthquake-size distribution. In general, the new approach provides higher estimates of the completeness magnitude than the traditional one. We associate this observation with the difference in the sensitivity of the two approaches in periods where the event detectability of the seismic networks is low. Our results allow us to move towards a full description of completeness as a function of space and time, which can be used for hazard-model development and forecast-model testing, showing an illustrative example of the applicability of the PMC method to regions with low to moderate seismicit

Pattern Informatics and Its Application for Optimal Forecasting of Large Earthquakes in Japan

Pattern informatics (PI) technique can be used to detect precursory seismic activation or quiescence and make earthquake forecast. Here we apply the PI method for optimal forecasting of large earthquakes in Japan, using the data catalogue maintained by the Japan Meteorological Agency. The PI method is tested to forecast large (magnitude m >= 5) earthquakes for the time period 1995-2004 in the Kobe region. Visual inspection and statistical testing show that the optimized PI method has forecasting skill, relative to the seismic intensity data often used as a standard null hypothesis. Moreover, we find a retrospective forecast that the 1995 Kobe earthquake (m = 7.2) falls in a seismically anomalous area. Another approach to test the forecasting algorithm is to create a future potential map for large (m >= 5) earthquake events. This is illustrated using the Kobe and Tokyo regions for the forecast period 2000-2009. Based on the resulting Kobe map we point out several forecasted areas: the epicentral area of the 1995 Kobe earthquake, the Wakayama area, the Mie area, and the Aichi area. The Tokyo forecasted map was created prior to the occurrence of the Oct. 23, 2004 Niigata earthquake (m = 6.8) and the principal aftershocks with m >= 5.0. We find that these events occurred in a forecasted area in the Tokyo map. The PI technique for regional seismicity observation substantiates an example showing considerable promise as an intermediate-term earthquake forecasting in Japan.Comment: 36 pages, 6 figures, 1 tabl

Predictability study on the aftershock sequence following the 2011 Tohoku-Oki, Japan, earthquake: first results

Although no deterministic and reliable earthquake precursor is known to date, we are steadily gaining insight into probabilistic forecasting that draws on space–time characteristics of earthquake clustering. Clustering-based models aiming to forecast earthquakes within the next 24 hours are under test in the global project ‘Collaboratory for the Study of Earthquake Predictability’ (CSEP). The 2011 March 11 magnitude 9.0 Tohoku-Oki earthquake in Japan provides a unique opportunity to test the existing 1-day CSEP models against its unprecedentedly active aftershock sequence. The original CSEP experiment performs tests after the catalogue is finalized to avoid bias due to poor data quality. However, this study differs from this tradition and uses the preliminary catalogue revised and updated by the Japan Meteorological Agency (JMA), which is often incomplete but is immediately available. This study is intended as a first step towards operability-oriented earthquake forecasting in Japan. Encouragingly, at least one model passed the test in most combinations of the target day and the testing method, although the models could not take account of the megaquake in advance and the catalogue used for forecast generation was incomplete. However, it can also be seen that all models have only limited forecasting power for the period immediately after the quake. Our conclusion does not change when the preliminary JMAcatalogue is replaced by the finalized one, implying that the models perform stably over the catalogue replacement and are applicable to operational earthquake forecasting. However, we emphasize the need of further research on model improvement to assure the reliability of forecasts for the days immediately after the main quake. Seismicity is expected to remain high in all parts of Japan over the coming years. Our results present a way to answer the urgent need to promote research on time-dependent earthquake predictability to prepare for subsequent large earthquakes in the near future in Japan.Published653-6583.1. Fisica dei terremotiJCR Journalrestricte

Primary proton spectrum between 200 TeV and 1000 TeV observed with the Tibet burst detector and air shower array

Since 1996, a hybrid experiment consisting of the emulsion chamber and burst detector array and the Tibet-II air-shower array has been operated at Yangbajing (4300 m above sea level, 606 g/cm^2) in Tibet. This experiment can detect air-shower cores, called as burst events, accompanied by air showers in excess of about 100 TeV. We observed about 4300 burst events accompanied by air showers during 690 days of operation and selected 820 proton-induced events with its primary energy above 200 TeV using a neural network method. Using this data set, we obtained the energy spectrum of primary protons in the energy range from 200 to 1000 TeV. The differential energy spectrum obtained in this energy region can be fitted by a power law with the index of -2.97 $\pm$ 0.06, which is steeper than that obtained by direct measurements at lower energies. We also obtained the energy spectrum of helium nuclei at particle energies around 1000 TeV.Comment: 25 pages, 22 figures, Accepted for publication in Phys. Rev.

Detection of Multi-TeV Gamma Rays from Markarian 501 during an Unforeseen Flaring State in 1997 with the Tibet Air Shower Array

In 1997, the BL Lac Object Mrk 501 entered a very active phase and was the brightest source in the sky at TeV energies, showing strong and frequent flaring. Using the data obtained with a high density air shower array that has been operating successfully at Yangbajing in Tibet since 1996, we searched for gamma-ray signals from this source during the period from February through August in 1997. Our observation detected multi-TeV $\gamma$-ray signals at the 3.7-Sigma level during this period. The most rapid increase of the excess counts was observed between April 7 and June 16 and the statistical significance of the excess counts in this period was 4.7-Sigma. Among several observations of flaring TeV gamma-rays from Mrk 501 in 1997, this is the only observation using a conventional air shower array. We present the energy spectrum of gamma-rays which will be worthy to compare with those obtained by imaging atmospheric Cerenkov telescopes.Comment: 9 pages, 7 figures, To appear in Ap