地震学资料的综合性中期预报

论文简介了用于中期（1～2年）地震预报的一些地震学方法。其中有重复率曲线斜率γ值（译者注：即b值）、弱震活跃—平静法（RTE）、纵横波波速比（VP／VS）方法、中等地震（M≥3．0活跃—平静法及据此得到的前兆显示时间与震级之间的经验关系式、地震活跃迁移法及据此得到的震中距（R）及前兆持续时间（△T）与震级的经验关系式。文章最后给出了综合适用上述方法编制的最近1～2年内哈萨克斯坦及邻区（吉尔吉斯斯坦等）可能发生5＜M＜6．5级地震的中期预报危险区图。

Static corrections for low S/N ratio converted-wave seismic data

Converted waves have slow velocity and low signal-to-noise ratio. It is also difficult to pick first-breaks and bin the common-conversion-points （CCP）. Some statics methods, which work well for P-wave data, can＇t be effectively used for solving convertedwave statics problems. This has become the main obstacle to breakthroughs in convertedwave data processing. To improve converted-wave static corrections, first, a statics method based on the common-receiver-point （CRP） stack is used for the initial receiver static correction to enhance the coherency of the CRP stack. Second, a stack-power-maximization static correction which improves the continuity of the CCP stack is used for detailed receiver statics. Finally, a non-surface-consistent residual moveout correction of the CCP gathers is used to enhance the stack power of reflection signals from different depths. Converted-wave statics are solved by the joint use of the three correction methods.

Shallow Water Body Data Processing Based on the Seismic Oceanography

Physical properties of sea water,such as salinity,temperature,density and acoustic velocity,could be demarcated through degradation of energy caused by water absorption,attenuation and other factors.To overcome the challenging difficulties in the quick monitoring of these physical properties,we have explored the high resolution marine seismic survey to instantly characterize them.Based on the unique wavefield propagating in the sea water,we have developed a new approach to suppress the noise caused by the shallow sea water disturbance and obtain useful information for estimating the sea water structure.This approach improves seismic data with high signal-to-noise ratio and resolution.The seismic reflection imaging can map the sea water structure acoustically.Combined with the knowledge of local water body structure profile over years,the instant model for predicting the sea water properties could be built using the seismic data acquired from the specially designed high precision marine seismic acquisition.This model can also be updated with instant observation and the complete data processing system.The present study has the potential value to many applications,such as 3D sea water monitoring,engineering evaluation,geological disaster assessment and environmental assessment.

Seismic Data Processing Approaches for the Study of Gas Hydrates in the East China Sea

A comprehensive study of the data profiles, including the 2D seismic data, single channel seismic data, shallow sections, etc., reveals that gas hydrates occur in the East China Sea. A series of special techniques are used in the processing of seismic data, which include enhancing the accuracy of velocity analysis and resolution, estimating the wavelet, suppressing the multiple, preserving the relative amplitude, using the DMO and AVO techniques and some special techniques in dealing with the wave impedance. The existence of gas hydrates is reflected in the subbottom profiles in the appearance of BSRs, amplitude anomalies, velocity anomalies and AVO anomalies, etc. Hence the gas hydrates can be identified and predicted. It is pointed out that the East China Sea is a favorable area of the gas hydrates resource, and the Okinawa Trough is a target area of gas hydrates reservoir.

Anomaly detection of earthquake precursor data using long short-term memory networks

Earthquake precursor data have been used as an important basis for earthquake prediction.In this study,a recurrent neural network(RNN)architecture with long short-term memory(LSTM)units is utilized to develop a predictive model for normal data.Furthermore,the prediction errors from the predictive models are used to indicate normal or abnormal behavior.An additional advantage of using the LSTM networks is that the earthquake precursor data can be directly fed into the network without any elaborate preprocessing as required by other approaches.Furthermore,no prior information on abnormal data is needed by these networks as they are trained only using normal data.Experiments using three groups of real data were conducted to compare the anomaly detection results of the proposed method with those of manual recognition.The comparison results indicated that the proposed LSTM network achieves promising results and is viable for detecting anomalies in earthquake precursor data.

Analysis of Tianchi volcano activity in Changbai Mountain,NE China

Changbaishan volcano is the largest potential eruptive volcano in China.In this paper,seismic activity,horizontal displacement,vertical displacement and the fluid geochemistry data acquiring from Changbaishan Tianchi Volcano Observatory(TVO) in recent years are analyzed.The authors discussed the ability for the Changbaishan volcanic seismic monitoring and active level of Changbaishan volcano in recent years based on the fundamental monitoring results.The results show that Changbaishan volcano has experienced an unrest episode from 2002 to 2005,but its active level recovers to the background now.

Random seismic noise attenuation by learning-type overcomplete dictionary based on K-singular value decomposition algorithm

The transformation of basic functions is one of the most commonly used techniques for seismic denoising,which employs sparse representation of seismic data in the transform domain. The choice of transform base functions has an influence on denoising results. We propose a learning-type overcomplete dictionary based on the K-singular value decomposition( K-SVD) algorithm. To construct the dictionary and use it for random seismic noise attenuation,we replace fixed transform base functions with an overcomplete redundancy function library. Owing to the adaptability to data characteristics,the learning-type dictionary describes essential data characteristics much better than conventional denoising methods. The sparsest representation of signals is obtained by the learning and training of seismic data. By comparing the same seismic data obtained using the learning-type overcomplete dictionary based on K-SVD and the data obtained using other denoising methods,we find that the learning-type overcomplete dictionary based on the K-SVD algorithm represents the seismic data more sparsely,effectively suppressing the random noise and improving the signal-to-noise ratio.

Towards a Virtual Asian-Pacific Seismological Network

Regional international cooperation in seismic data exchange and joint seismological analysis in the Asian-Pacific regions plays an important role in the reduction of earthquake disasters in these regions as well as the development of global seismology. Based on the up-to-date development of new technology and digital broadband seismology, we discussed the Asian Seismological Commission (ASC) Proposal “Asian-Pacific Seismological Data Center”. We applied the concept of virtual seismological network (Ottemoeller and Havskov, 1999) to the proposed ASC data exchange program. Based on the development of digital seismology, we introduced a concept of “modern earthquake catalogues” which include not only location parameters and magnitudes as in the conventional earthquake catalogues but also new parameters of earthquakes such as CMT, radiated energy, STF, and earthquake rupture process. We recommended a web-based regional international data exchange program between the ASC members with the purpose of forming a virtual Asian-Pacific seismological network, and the interpretation and analysis of broadband digital seismic waveforms conducted at distributed “data centers”.

State Vectors—A New Method for Seismicity Analysis

The concept of state vector stems from statistical physics, where it is usually used to describe the evolution of a continuum field in its way of coarse-graining. In this paper, the state vector is employed to depict the evolution of seismicity quantitatively, and some interesting results are presented. The authors investigated some famous earthquake cases (e.g., the Haicheng earthquake, the Tangshan earthquake, the west Kunlun Mountains earthquake, etc.) and found that the state vectors evidently change prior to the occurrence of large earthquakes. Thus it is believed that the state vector can be used as a kind of precursor to predict large earthquakes.

An Introduction to the Latest-generation Spatial Database of Active Tectonics of China

Based on ArcGIS and MapInfo software, we digitized the active tectonics map (1:4,000,000) of China, which was compiled and revised by academician Deng Qidong, and built the spatial database of active tectonics of China. The database integrates rich active tectonic data, such as a catalogue of earthquakes with magnitude above 6.0, active faults, Quaternary basins, active folds and their associated attribute parameters, and implements scientific and effective management to this data. At the same time, the spatial database joins the spatial map data and the associated attribute data together, which implements the data query between spatial properties and attribute parameters and also makes it possible to perform spatial analysis with different data layers. These provide much convenience for earthquake study and allows engineering construction institutions to use this data in practical applications.

Research on seismic fluid identification driven by rock physics

Seismic fluid identification works as an effective approach to characterize the fluid feature and distribution of the reservoir underground with seismic data. Rock physics which builds bridge between the elastic parameters and reservoir parameters sets the foundation of seismic fluid identification, which is also a hot topic on the study of quantitative characterization of oil/gas reservoirs. Study on seismic fluid identification driven by rock physics has proved to be rewarding in recognizing the fluid feature and distributed regularity of the oil/gas reservoirs. This paper summarizes the key scientific problems immersed in seismic fluid identification, and emphatically reviews the main progress of seismic fluid identification driven by rock physics domestic and overseas, as well as discusses the opportunities, challenges and future research direction related to seismic fluid identification. Theoretical study and practical application indicate that we should incorporate rock physics, numerical simulation, seismic data processing and seismic inversion together to enhance the precision of seismic fluid identification.