“…For example, establishing a physically reasonable framework that combines the pattern informatics (PI) prediction method [16,17] with the load/unload response ratio (LURR), state vector (SV), and accelerating moment release (AMR) method [18] could be considered. Alternatively, the LURR method could be combined with other methods for seismic hazard assessment [19]. Scholars have also attempted to use the LURR method to study the electromechanical coupling process before large earthquakes, using geoelectric data and Benioff strain data from small earthquakes as input [20], and the results were both well.…”
The application of certain mathematical–statistical methods can quantitatively identify and extract the abnormal characteristics from the observation data, and the comprehensive analysis of seismic multi-parameters can study and judge the risk of the tectonic regions better than a single parameter. In this study, the machine learning-based detection of seismic multi-parameters using the sliding extreme value relevancy method, based on the earthquake-corresponding relevancy spectrum, was calculated in the tectonic regions in the western Chinese mainland, and the R-value evaluation was completed. Multi-parameter data included the b value, M value (missing earthquakes), ƞ value (the relationship between seismic magnitude and frequency), D value (seismic hazard), Mf value (intensity factor), N value (earthquake frequency), and Rm value (modulation parameter). The temporal results showed that the high-value anomalies appeared before most target earthquakes during the training period. Moreover, some target earthquakes also occurred during the advantageous extrapolation period with high-value anomalies. The spatial results showed that some months before the target earthquakes, there was indeed a significant abnormal enhancement area that appeared near the epicenter, and the anomaly gradually disappeared after the earthquakes. This study demonstrated that machine learning techniques for detecting earthquake anomalies using seismic multi-parameter data were feasible.
“…For example, establishing a physically reasonable framework that combines the pattern informatics (PI) prediction method [16,17] with the load/unload response ratio (LURR), state vector (SV), and accelerating moment release (AMR) method [18] could be considered. Alternatively, the LURR method could be combined with other methods for seismic hazard assessment [19]. Scholars have also attempted to use the LURR method to study the electromechanical coupling process before large earthquakes, using geoelectric data and Benioff strain data from small earthquakes as input [20], and the results were both well.…”
The application of certain mathematical–statistical methods can quantitatively identify and extract the abnormal characteristics from the observation data, and the comprehensive analysis of seismic multi-parameters can study and judge the risk of the tectonic regions better than a single parameter. In this study, the machine learning-based detection of seismic multi-parameters using the sliding extreme value relevancy method, based on the earthquake-corresponding relevancy spectrum, was calculated in the tectonic regions in the western Chinese mainland, and the R-value evaluation was completed. Multi-parameter data included the b value, M value (missing earthquakes), ƞ value (the relationship between seismic magnitude and frequency), D value (seismic hazard), Mf value (intensity factor), N value (earthquake frequency), and Rm value (modulation parameter). The temporal results showed that the high-value anomalies appeared before most target earthquakes during the training period. Moreover, some target earthquakes also occurred during the advantageous extrapolation period with high-value anomalies. The spatial results showed that some months before the target earthquakes, there was indeed a significant abnormal enhancement area that appeared near the epicenter, and the anomaly gradually disappeared after the earthquakes. This study demonstrated that machine learning techniques for detecting earthquake anomalies using seismic multi-parameter data were feasible.
“…When the rock is in the elastic stage, the responses to the loading and unloading are nearly the same, and LURR = 1.0; when the rock is stressed to the dilatant state, the responses are quite different, generally, LURR > 1.0. The LURR value is therefore served as a useful tool to analyze the criticality of earthquakes, and, the anomalous increases in the time series of LURR preceding a large earthquake has been reported by many publications 16 , 17 . Figure 1 Schematic diagram of the rock constitutive relationship.…”
The observation of electromechanical coupling might be used as an important tool to detect pre-seismic changes associated with the preparation of earthquakes. This paper attempts to study the electromechanical coupling process before the large earthquakes by using the load/unload response ratio (LURR) approach in which the geo-electric data and Benioff strain of small earthquakes were adopted as the data input. The variation of Coulomb failure stress induced by earth tides on the fault surface of the mainshock is applied to differentiate the loading and unloading stages. Using this technique, we test the geo-electric data recorded at the Hotan observatory near the epicenter of 2020 Yutian Ms 6.4 earthquake. Results show that the LURR time sequence fluctuated around 1.0 for many years and reached significant high peaks at the beginning of 2020. More importantly, this evolution correlates well with the LURR time series calculated by using the Benioff strain of small earthquakes within the circular region of 300 km radius centered at the epicenter. The underlying physics of the changes should be caused by the fluid infiltration derived from pre-seismic rock dilatancy. The corresponding volume variations in the crust could be found in the geophysical observation time series in the same neighborhoods.
“…For the first time for Sakhalin, such a detailed analysis was made for a continuous time period and on the territory of the entire island. It is very important that after a number of works on this topic [3][4][5] the authors have systematized the materials, bringing all the calculations to a single database of seismological data according to the catalog of the Sakhalin branch of the Geophysical Survey of the Russian Academy of Sciences. It should be noted that this study aroused a great interest not only among the predictors, a theoretical review of the LURR method, for example, was published almost immediately [6].…”
A retrospective analysis of the seismicity of Sakhalin from 1997 to 2019 was performed to demonstrate the possibilities of the LURR technique recently in previous our work. The following results were obtained: 84 % of earthquakes (16 out of 19, with M ≥ 5) are predicted, 25% alarms (4 out of 15 predicted areas) were false. This paper proposes an analytical dependence to describe the forecast effectiveness (Ke) for this research. The extremes of Ke were found at the value of the alarm period of 12 and 24 months. At the same time, Ke is significantly higher for the alarm period of 24 months and decreases after a two-year alarm period. Another way to prove the results obtained is the random spatio-temporal distributions of the predicted objects (19 earthquakes with M ≥ 5). 10 such random sets have been assigned to 15 predicted areas, the result shows a significant advantage of a real sample over random ones, and also practically confirms the reliability of the algorithm for using the LURR technique. The methodology and results of this work can serve as practical recommendations for working with the LURR method for seismologists.
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