Forecasting the Locations of Future Large Earthquakes: An Analysis and Verification

Abstract

Abstract-The objective of this paper is to quantify the use of past seismicity to forecast the locations of future large earthquakes and introduce optimization methods for the model parameters. To achieve this the binary forecast approach is used where the surface of the Earth is divided into l°9 l°cells. The cumulative Benioff strain of m C m c earthquakes that occurred during the training period, DT tr , is used to retrospectively forecast the locations of large target earthquakes with magnitudes Cm T during the forecast period, DT for . The success of a forecast is measured in terms of hit rates (fraction of earthquakes forecast) and false alarm rates (fraction of alarms that do not forecast earthquakes). This binary forecast approach is quantified using a receiver operating characteristic diagram and an error diagram. An optimal forecast can be obtained by taking the maximum value of Pierce's skill score.

Assessing the potential improvement in short‐term earthquake forecasts from incorporation of GPS data

“…The range of probability gains in this study achieved with ≤50% false alarm rates is 2–4. The common ranges of probability gains for forecasting earthquakes using different methods and data are 1.8–4 [ Helmstetter and Sornette , ; Rhoades et al ., ; Shcherbakov et al ., ; Zechar and Nadeau , ; Zhang and Zhuang , ], 2–10 [ Newman and Turcotte , ; Shebalin et al ., , ], and ≥ 10 [ Zechar and Jordan , ]. Thus, the probability gains from GPS measurements lie in the common and lower range of such gains.…”

A statistical feature of anomalous seismic activity prior to large shallow earthquakes in Japan revealed by the pattern informatics method

“…Therefore, it is necessary to quantitatively compare the statistical performance of the spatiotemporal PI maps for different cutoff magnitudes, different change intervals, and different lower thresholds of PI value representing large seismicity change during the change interval or high earthquake occurrence probability during the prediction period. For this purpose, we used Molchan's error diagram (Kagan, 2007;Molchan, 1997;Shcherbakov et al, 2010) to examine the coherence between the spatiotemporal locations of target earthquakes and the fraction of grid cells occupied by the alarm area. Figures 9-11 present plots of miss rate versus fraction of grid cells occupied by the alarm area.…”

Complexity and Earthquakes

“…This type of forecast is referred to as a 'relative intensity' (RI) forecast (Holliday et al, 2005(Holliday et al, , 2006Shcherbakov et al, 2010). One approach to earthquake forecasting is to use the rate of occurrence of small earthquakes in the past.…”