Assessing the Potential Earthquake Precursory Information in ULF Magnetic Data Recorded in Kanto, Japan during 2000-2010: Distance and Magnitude Dependences

Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates

Hawkes processes are used in statistical modeling for event clustering and causal inference, while they also can be viewed as stochastic versions of popular compartmental models used in epidemiology. Here we show how to develop accurate models of COVID-19 transmission using Hawkes processes with spatial-temporal covariates. We model the conditional intensity of new COVID-19 cases and deaths in the U.S. at the county level, estimating the dynamic reproduction number of the virus within an EM algorithm through a regression on Google mobility indices and demographic covariates in the maximization step. We validate the approach on both short-term and long-term forecasting tasks, showing that the Hawkes process outperforms several models currently used to track the pandemic, including an ensemble approach and an SEIR-variant. We also investigate which covariates and mobility indices are most important for building forecasts of COVID-19 in the U.S.

Temporal Variation of b Value with Statistical Test in Wenchuan Area, China Prior to the 2008 Wenchuan Earthquake

The Gutenberg–Richter b value describes the ratio between large and small events. A number of studies have suggested that the b value decreases before large earthquakes. In this study, we investigate the temporal variation of the b value of an area along the main rupture zone of the 2008 Wenchuan earthquake (M8.0) prior to the great event. Before estimating b values, we tested the earthquake catalog to make sure that we use the reliable frequency–magnitude distribution by the calculation of MC (completeness of magnitude). We define parameter P (ΔAIC ≧ 2) values to examine the significance level of b-value changes in the temporal variation by combining a boostrap method with Akaike’s Information Criterion (AIC). The b value in the main rupture zone shows a long-term decrease trend. We then focus on a smaller area where the initial rupture starts. The results show that b values significantly changed about 3 months before the 2008 Wenchuan earthquake in the initial rupture area, indicating that the b value has a potential capability to monitor and detect precursory phenomena of great earthquakes.

Characteristics of Local Geomagnetic Field Variations and the Tectonic Stress Field Adjacent to the 21 May 2021, Ms 6.4 Yangbi Earthquake, Yunnan, China

The tectonic processes leading up to an earthquake and the occurrence of the earthquake itself will cause local changes in the geophysical field (geomagnetic field, stress field, etc.). In this paper, the variation characteristics of the tectonic stress field (TSF) and local geomagnetic field (LGF) before and after the Yangbi Ms 6.4 earthquake are studied. The regional stress tensor damping inversion method was used to invert the TSF using focal mechanism solutions (FMSs). The change characteristics of the TSF before and after the earthquake were analyzed. An annual variation model of the LGF was constructed, and the variation of the horizontal vector was analyzed. The azimuth and plunge of the maximum principal compressive stress axis of the TSF in the epicentral region before and after the earthquake were −4.4° and 2.7°, 172.7° and 6.6°, respectively. The variations in the declination, inclination and total intensity of the epicenter one year before and one month after the earthquake were −0.20′ (0.07′), 0.29′ (−0.12′), and −1.7 nT (−1.9 nT), respectively. The epicenter is located at the boundary of the “weak variation region” of the horizontal vector. This research is of great significance concerning the TSF background and incubation mechanism of earthquakes.

Autonomous Instrumentation for Measuring Electromagnetic Radiation from Rocks in Mine Conditions-A Functional Analysis

This paper analyses the function of an innovative integrated receiver for the measurement of electromagnetic field emissions. The autonomous receiver measures and registers the elevated emission levels of both components of the EM field originating from rocks subjected to increased mechanical stress. The receiver’s sensitivity of 60 µV/m, its dynamic range of 98 dB, and its impulse response of 0.23 V/µs were determined in laboratory conditions. Real EM field signals from hard coal samples subjected to crushing force were recorded using an autonomous receiver. The observed and recorded results confirm that the receiver operates in the full range of amplitudes of the EM field signal emitted from the rock. The results determine the band of characteristic signals for EM field emission from hard coal. The system created on the basis of autonomous EM receivers can support the existing seismic safety systems in real mine conditions by predicting the possibility of mine collapse hazards.

Crash Diagnosis and Price Rebound Prediction in NYSE Composite Index Based on Visibility Graph and Time-Evolving Stock Correlation Network

This study proposes a framework to diagnose stock market crashes and predict the subsequent price rebounds. Based on the observation of anomalous changes in stock correlation networks during market crashes, we extend the log-periodic power-law model with a metric that is proposed to measure network anomalies. To calculate this metric, we design a prediction-guided anomaly detection algorithm based on the extreme value theory. Finally, we proposed a hybrid indicator to predict price rebounds of the stock index by combining the network anomaly metric and the visibility graph-based log-periodic power-law model. Experiments are conducted based on the New York Stock Exchange Composite Index from 4 January 1991 to 7 May 2021. It is shown that our proposed method outperforms the benchmark log-periodic power-law model on detecting the 12 major crashes and predicting the subsequent price rebounds by reducing the false alarm rate. This study sheds light on combining stock network analysis and financial time series modeling and highlights that anomalous changes of a stock network can be important criteria for detecting crashes and predicting recoveries of the stock market.

Assessing Earthquake Forecast Performance Based on b Value in Yunnan Province, China

Many studies have shown that b values tend to decrease prior to large earthquakes. To evaluate the forecast information in b value variations, we conduct a systematic assessment in Yunnan Province, China, where the seismicity is intense and moderate-large earthquakes occur frequently. The catalog in the past two decades is divided into four time periods (January 2000-December 2004, January 2005-December 2009, January 2010-December 2014, and January 2015-December 2019). The spatial b values are calculated for each 5-year span and then are used to forecast moderate-large earthquakes (M ≥ 5.0) in the subsequent period. As the fault systems in Yunnan Province are complex, to avoid possible biases in b value computation caused by different faulting regimes when using the grid search, the hierarchical space-time point-process models (HIST-PPM) proposed by Ogata are utilized to estimate spatial b values in this study. The forecast performance is tested by Molchan error diagram (MED) and the efficiency is quantified by probability gain (PG) and probability difference (PD). It is found that moderate-large earthquakes are more likely to occur in low b regions. The MED analysis shows that there is considerable precursory information in spatial b values and the forecast efficiency increases with magnitude in the Yunnan Province. These results suggest that the b value might be useful in middle- and long-term earthquake forecasts in the study area.

Evaluation of Pre-Earthquake Anomalies of Borehole Strain Network by Using Receiver Operating Characteristic Curve

In order to monitor temporal and spatial crustal activities associated with earthquakes, ground- and satellite-based monitoring systems have been installed in China since the 1990s. In recent years, the correlation between monitoring strain anomalies and local major earthquakes has been verified. In this study, we further evaluate the possibility of strain anomalies containing earthquake precursors by using Receiver Operating Characteristic (ROC) prediction. First, strain network anomalies were extracted in the borehole strain data recorded in Western China during 2010–2017. Then, we proposed a new prediction strategy characterized by the number of network anomalies in an anomaly window, Nano, and the length of alarm window, Talm. We assumed that clusters of network anomalies indicate a probability increase of an impending earthquake, and consequently, the alarm window would be the duration during which a possible earthquake would occur. The Area Under the ROC Curve (AUC) between true predicted rate, tpr, and false alarm rate, fpr, is measured to evaluate the efficiency of the prediction strategies. We found that the optimal strategy of short-term forecasts was established by setting the number of anomalies greater than 7 within 14 days and the alarm window at one day. The results further show the prediction strategy performs significantly better when there are frequent enhanced network anomalies prior to the larger earthquakes surrounding the strain network region. The ROC detection indicates that strain data possibly contain the precursory information associated with major earthquakes and highlights the potential for short-term earthquake forecasting.