Examining the spatial and temporal distribution of seismic activity holds significant importance for seismic risk assessment, particularly in regions prone to frequent and intense earthquakes such as the Sichuan-Yunnan region in China. It is widely recognized that earthquakes exhibit non-random patterns in both spatial and temporal dimensions.

Early scientists endeavored to predict earthquakes using statistical principles, leading to the development of various forecasting methods. Among these, the Relative Intensity(RI)and Pattern Informatics(PI)methods emerged as statistical approaches to earthquake prediction modeling. Essentially, both methods fall under the category of smoothing seismic activity models. They employ techniques to quantify temporal changes in seismic activity graphs, generating maps that highlight areas(hot spots)where earthquakes may occur during specific future periods. While the RI algorithm’s theory is straightforward, its forecasting efficacy is robust, particularly notable in predicting major earthquakes, demonstrating similar advantages to the PI algorithm. Widely adopted globally for proactive predictions across diverse tectonic systems, it has shown commendable results in seismic forecasting practices both domestically and internationally. Over years of development, its predictive performance has gained prominence. However, further research is needed to assess its suitability for predicting minor seismic events in low-seismicity zones. Additionally, its successful application hinges on background seismic activity and the selection of target magnitudes.

To aid seismic activity prediction in the Sichuan-Yunnan region and identify potential future seismic source areas, a comprehensive parameter analysis was conducted using the Relative Intensity(RI)algorithm with the parameter traversal test(PTT). The RI algorithm operates on the premise that the predicted intensity of future earthquakes in a given region closely mirrors the intensity of past earthquakes. While it may not explicitly consider the “active” and “quiet” characteristics of seismic activity, as a fundamental prediction algorithm, it often yields improved prediction outcomes when applied to assess seismic probability in regions with high seismic activity, such as the Sichuan-Yunnan region.

In this study, the statistical-based Relative Intensity(RI)algorithm is employed to calculate the relative intensity of earthquakes based on quantitative earthquake characteristics. The study involves gridding the investigated area and statistically analyzing historical earthquake occurrences within each grid unit under specific magnitude conditions to inform predictions of future earthquake frequencies. The research focuses on evaluating the influence of four key model parameters: grid size, length of the anomalous learning window, starting point of the prediction window, and length of the prediction window, on the algorithm’s prediction efficiency. Furthermore, the study investigates the applicability of the RI algorithm to the Sichuan-Yunnan regions in China. The results yield two significant findings:

(1)The integration of the Relative Intensity(RI)algorithm with the Parameter Traversal Test(PTT)yielded significantly improved results compared to random guessing, primarily due to its optimized parameter selections. These parameters include the grid size, length of the anomalous learning time window, starting time of the prediction time window, and length of the prediction time window.

(2)The parameters of the prediction model exhibit a degree of stability and demonstrate predictive capability for seismic activity in the Sichuan-Yunnan region over the next 1-5 years. The study revealed specific rules and effective parameter intervals applicable to earthquake-prone areas in Sichuan-Yunnan.

The findings suggest that the integration of the Relative Intensity(RI)algorithm with the Parameter Traversal Test(PTT)holds promise for predicting seismic activities in the Sichuan-Yunnan region. This approach enhances the pool of references available for predicting earthquake trends in regions prone to frequent and intense earthquakes. Further research on the RI algorithm is anticipated to yield a more refined numerical model for earthquake trend prediction, contributing to enhanced forecasting accuracy and preparedness in earthquake-prone areas.

Several earthquakes above M_S6.0 occurred in Jiashi, Xinjiang region in China in history. A new M_S6.5 event occurred in this area on Jan. 19, 2020, for which a ‘virtual scientific investigation’ was carried out by China Earthquake Administration in a short time after the earthquake. In this ‘virtual investigation’, a vital important question is that which fault controls the occurrence process of this event, and what is the correlation between this event with other previous earthquakes. To understand the solutions of these questions well, the seismologists analyzed different types of monitoring data, the source parameters, focal mechanisms, seismic waves, InSAR data, regional tectonics, seismic activity including the aftershock sequence, abnormalities of CH4 and GPS TEC, etc. Some conclusions can be drawn on the features of this earthquake and the potential of large aftershocks based on analysis of these kinds of data.
Statistical seismology tools may provide a significant constraint in the case that some earthquakes cannot be described very well through traditional approaches. Concerning the seismicity in the Jiashi area, whether this earthquake is independent background seismicity, or has a certain triggering relationship to the other previous events, is a main question to be well answered during this research. So, to explore the features of the background events and the triggering ability of the events in this area, we used the spatial-temporal epidemic type aftershock sequence(ETAS)model to fit the seismicity using the earthquake events from Jan. 1, 1970 to Jun. 1, 2020 to obtain the spatial and temporal distribution of total seismicity rate, background seismicity rate and clustering seismicity rate. Then the stochastical declustering method based on ETAS model was used to separate all the events into background events and clustering events. The result shows that the clustering seismicity has a main contribution to the total seismicity in this region. The north and south part of the study area show different features of background and triggering seismicity. The north part shows a more homogenous spatial distribution of background seismicity, while the south part shows a high level of triggering or clustering seismicity. Through the calculation of the ETAS algorithm, this M_S6.5 event has a 99%probability of being a triggered event, in which the main contribution is from an M_S5.3 event that occurred 1 day before this event. On the other hand, we find that among all of the events which have contribution to others, an M_L4.1 event that occurred on Apr. 21, 2020 has the highest ability to ‘disturb’ the other events, the probability reaches 0.505, but this needs to be confirmed by other methods. As generally recognized by seismologists, this M_S6.5 event and other previous large earthquakes are mainly influenced by the western Himalayan syntaxis in this region.
As everyone knows, to find the statistical solutions for these questions based on the stochastic or statistical theory, we need focus on the analysis of large group events, rather than a single one. Some algorithms and methods of statistical seismology seem to provide us an opportunity to analyze the group features of seismicity in the study region. Through the analysis using spatial-temporal ETAS model, we can use the statistical methods to describe the spatial and temporal behavior of the background and triggered events, and obtain some special information which cannot be obtained effectively with other traditional tools in some cases. In addition, the traditional ETAS models have been rapidly expanded and developed in recent years, such as 3D-ETAS model incorporating focal depth information, and the finite element ETAS model incorporating the element of spatial morphology of the seismogenic faults. In this view, we can suggest that statistical seismology approaches may have a chance to supply a significant balance point between the pure scientific research and the earthquake consultation work in the future, especially between the conventional seismic research and operational work in China.

Soil-rock mixture deposit is an extremely heterogeneous loose rock-soil deposit formed since Quaternary, which is composed of blocks, fine-grained soil and pore with a certain engineering scale and high strength and has a certain stone content. These soil-rock mixtures accumulated on slopes have been completely destroyed and their mechanical strength is very low. They are widely distributed in the mountainous areas of Southwest China, which poses a great threat to the engineering. Earthquakes occur frequently in Southwest China, and the instability of soil-rock mixture deposit under seismic load is one of the important factors causing the damage to this type of deposit. The dynamic response of soil-rock mixture deposit under seismic load is an important index to study its instability mechanism under seismic load.
Based on indoor shaking table model test, the influence of rock content and slope gradient on dynamic response characteristics of soil-rock mixture deposit was studied. In model tests, rock content is 30%, 40% and 50%respectively, and slope gradient varies from 20°, 30° and 40°. Two different seismic loading frequencies and three different excitation strengths were given. The peak acceleration(PGA)amplification coefficients in horizontal and vertical directions of soil-rock mixture deposit were analyzed under the change of rock content and slope gradient. The permanent displacement and deformation law of the top and foot of the slope of soil-rock mixture deposit were analyzed by model test. The experimental results show that the dynamic acceleration response characteristics of the soil-rock mixture deposits at the top and foot of the slope are different under different slope gradients and rock content conditions, and the horizontal PGA amplification coefficients of the soil-rock mixture deposits are also different. With the same seismic frequency and excitation intensity, the horizontal PGA amplification coefficient increases with increased slope gradient, and the rate gets faster. With the increase of stone content, the magnification coefficient of horizontal PGA decreases, and the higher the stone content, the slower the decrease rate of horizontal PGA magnification coefficient. When the slope gradient of soil-rock mixture deposit increases, the corresponding horizontal and vertical PGA amplification coefficients increase with the same seismic frequency and excitation intensity. The amplification coefficients of PGA in the vertical direction are different, but the overall magnification is weaker than that in the horizontal direction. The vertical PGA amplification coefficients of the foot, middle and lower parts of the slope are larger, while the vertical PGA amplification coefficients of the upper and middle parts of the slope tend to decrease. The higher the frequency of seismic wave is, the smaller the vertical PGA amplification coefficient corresponding to the same elevation will be, which indicates that the vertical PGA amplification coefficient is negatively correlated with the elevation. The variation trend of PGA magnification coefficient of soil-rock mixed deposit in vertical direction is different with the change of stone content. Under the same excitation intensity, the larger the slope gradient is, the larger the permanent displacement at the top of the slope will be, and the larger the rock content, the smaller the corresponding displacement at the top of the slope. The permanent displacement of the top of the slope is obviously larger than that of the foot of the slope, which indicates that the magnification effect of the top of the slope is obvious. After the vibration process and sliding of the landslide, the large-sized particles in the soil-rock mixture deposit move downward faster and slip on the surface of the deposit body. There was a very obvious phenomenon of particle sorting in the pile-up at the foot of the landslide body. The results of this study are of practical significance for the analysis of the dynamic response law of soil-rock mixture deposit under seismic load due to the change of rock content and slope gradient.

Many small earthquakes occurred intensively and continuously and formed an earthquake sequence after the M_L3.8 earthquake happened at Rushan County, Shandong Province on October 1, 2013. Up to March, 2017, more than 13 000 events have been recorded, with 3 429 locatable shocks, of which 31 events with M_L ≥ 3.0. This sequence is rarely seen in East China for its extraordinary long duration and the extremely high frequency of aftershocks. To track the developing tendency of the earthquake sequence accurately, 20 temporary seismometers were arranged to monitor the sequence activities around the epicenter of the sequence since May 6, 2014. Firstly, this paper adopts double difference method to relocate the 1 418 earthquakes of M_L ≥ 1.0 recorded by temporary seismometers in the Rushan earthquake sequence (May 7, 2014 to December 31, 2016), the result shows that the Rushan earthquake sequence mainly extends along NWW-SEE and forms a rectangular activity belt of about 4km long and 3km wide. In addition, the seismogenic fault of Rushan earthquake sequence stretches along NWW-SEE with nearly vertical strike-slip movement and a small amount of thrust component. Then we apply the P-wave initial motion and CAP to invert the focal mechanism of earthquakes with M_L ≥ 1.5 in the study area. The earthquakes can be divided into several categories, including 3 normal fault earthquakes (0.9%), 3 normal-slip earthquakes (0.9%), 229 strike-slip earthquakes (65.8%), 18 thrust fault earthquakes (5.2%), 37 thrust-slip earthquakes (10.6%)and 58 undefined (16.6%). Most earthquakes had a strike-slip mechanism in Rushan (65.8%), which is one of the intrinsic characteristics of the stress field. According to the focal mechanism solutions, we further utilized the LSIB method (Linear stress inversion bootstrap)to invert the stress tensor of Rushan area. The result shows that the azimuth and plunge of three principal stress (σ₁, σ₂, σ₃) axes are 25°, 10°; 286°, 45°; 125°, 43°, respectively. Based on the stress field inversion results, we calculated the focal mechanism solutions consistency parameter (θ)and the angle (θ₁)between σ₁ and P axis. The trend lines of θ and θ₁ were relatively stable with small fluctuation near the average line over time. Furthermore, the earthquake sequence can be divided into three stages based on θ and θ₁ values. The first stage is before September 16, 2014, and the variation of the θ and θ₁ values is relatively smooth with short period. All focal mechanism solutions of the three M_L ≥ 3.0 earthquakes exhibited consistence. The second stage started from September 16, 2014 to July 1, 2015, the fluctuation range of θ and θ₁ values is larger than that of the first stage with a relative longer period. The last stage is after July 1, 2015, values of θ and θ₁ gradually changed to a periodic change, three out of the four M_L ≥ 3.0 earthquakes (strike-slip type)displayed a good consistency. Spatially, earthquakes occurred mainly in green, yellow-red regions, and the focal mechanism parameters consistency θ was dominant near the green region (around the average value), which presents a steady state, and the spatial locations are concordant with the distribution of θ value. Moreover, all of M_L ≥ 3.0 earthquakes are located in the transitional region from the mean value to lower value area or region below the mean value area, which also indicates the centralized stress field of the region.

The topography, occurrence mechanism and lithology are the important factors of landslide movement. The lithology, seismic intensity, geological structure and topography in the travel path of 215 incomplete obstruction landslides with volumes more than 10⁴m³ induced by Wenchuan earthquake were studied. Based on the classification of the factors established, we studied the factors influencing the movement distance of incomplete obstruction landslides. The following results are drawn. In the influence factors of topography in the travel path, the distance is largest in the straight valley topography, followed by the concave, ladder, turning valley, slope toe-type and slope-type landslides, in turn. The topography not only has remarkable influence on the distance of large-scale landslides, but also on the medium and small-scale landslides as well, which is the most important factor influencing the distance. The formation mechanism controlled by lithology, seismic intensity and geological structure has little influence on the mobility of medium-and small-scale landslides. For the large-scale landslides with volume more than 10⁶m³, the distance of landslide with medium hard rock is larger than landslides with hard and soft rock. In the seismic intensity Ⅸ to Ⅺ areas, the landslide distance decreases with intensity increasing, contrary to the distribution of landslide-point density and landslide-area density. The geological structure has influence on the slide aspect of landslides and occurrence mechanism, but the influence is not remarkable to the landslide movement distance.

The terrain near the Changbaishan Tianchi volcano caldera is complex and highly covered with vegetation,so it is difficult to determine the precise spatial distribution extent of the volcanic eruptive products only using the geological or visible remote sensing technology.In this paper,based on the analysis of spectrum and texture information from the IKONOS image of the study area,eight types of training samples are selected and the spectral angle technology(SAM)is applied to perform classification. These eight types of surface include water body,shadow,thick pumice,thin pumice,trachyte, soil,vegetation and forest.The primary results of such classification are not satisfactory through confusion matrix evaluation,which may be due to a serious loss of spectrum information in the synthesizing process.However,the texture information of the image is abundant.Therefore,we introduce the texture analysis of the ENVI probability matrix method which takes advantage of dissimilarity texture feature to establish the symbol of texture interpretation.Our final results indicate that the accuracy of classification may be greatly improved by using the combination of spectrum and texture analysis. Taking the widely distributed pumice as an example of classification,we obtain the information of both the extent and the relative thickness distribution of pumice near the Tianchi caldera region.In the future, more work should be concentrated on the field investigation to confirm the terrain features of eight types which are obtained from the satellite image interpretation before that remote sensing technology will truly become one of the useful tools for geological mapping and volcanic hazards evaluation.