SeisGuard: A Software Platform to Establish Automatically an Earthquake Forecasting Model

SeisGuard, a system for analyzing earthquake precursory data, is a software platform to search for earthquake precursory information by processing geophysical data from different sources to establish automatically an earthquake forecasting model. The main function of this system is to analyze and process the deformation, fluid, electromagnetic and other geophysical field observing data from ground-based observation, as well as space-based observation. Combined station and earthquake distributions, geological structure and other information, this system can provide a basic software platform for earthquake forecasting research based on spatiotemporal fusion. The hierarchical station tree for data sifting and the interaction mode have been innovatively developed in this SeisGuard system to improve users’ working efficiency. The data storage framework designed according to the characteristics of different time series can unify the interfaces of different data sources, provide the support of data flow, simplify the management and usage of data, and provide foundation for analysis of big data. The final aim of this development is to establish an effective earthquake forecasting model combined all available information from ground-based observations to space-based observations.

Identification of reservoir types in deep carbonates based on mixedkernel machine learning using geophysical logging data

Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces.Traditional cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency,which cannot accurately reflect the nonlinear relationship between reservoir types and logging data.Recently,the kernel Fisher discriminant analysis(KFD),a kernel-based machine learning technique,attracts attention in many fields because of its strong nonlinear processing ability.However,the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well,especially for highly complex data cases.To address this issue,in this study,a mixed kernel Fisher discriminant analysis(MKFD)model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin,China.The MKFD model was trained and tested with 453 datasets from 7 coring wells,utilizing GR,CAL,DEN,AC,CNL and RT logs as input variables.The particle swarm optimization(PSO)was adopted for hyper-parameter optimization of MKFD model.To evaluate the model performance,prediction results of MKFD were compared with those of basic-kernel based KFD,RF and SVM models.Subsequently,the built MKFD model was applied in a blind well test,and a variable importance analysis was conducted.The comparison and blind test results demonstrated that MKFD outperformed traditional KFD,RF and SVM in the identification of reservoir types,which provided higher accuracy and stronger generalization.The MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates.

Mass movements in small canyons in the northeast of Baiyun deepwater area, north of the South China Sea

The process of mass movements and their consequent turbidity currents in large submarine canyons has been widely reported, however, little attention was paid to that in small canyons. In this paper, we document mass movements in small submarine canyons in the northeast of Baiyun deepwater area, north of the South China Sea （SCS）, and their strong effects on the evolution of the canyons based on geophysical data. Submarine canyons in the study area arrange closely below the shelf break zone which was at the depth of -500 m. Within submarine canyons, seabed surface was covered with amounts of failure scars resulted from past small-sized landslides. A complex process of mass transportation in the canyons is indicated by three directions of mass movements. Recent mass movement deposits in the canyons exhibit translucent reflections or parallel reflections which represent the brittle deformation and the plastic deformation, respectively. The area of most landslides in the canyons is less than 3 km2. The trigger mechanisms for mass movements in the study area are gravitational overloading, slope angle and weak properties of soil. Geophysical data indicate that the genesis of submarine canyons is the erosion of mass movements and consequent turbidity currents. The significant effects of mass movements on canyon are incision and sediment transportation at the erosion phases and fillings supply at the fill phases. This research will be helpful for the geological risk assessments and understanding the sediment transportation in the northern margin of the SCS.

Trace-Norm Regularized Multi-Task Learning for Sea State Bias Estimation

Sea state bias(SSB)is an important component of errors for the radar altimeter measurements of sea surface height(SSH).However,existing SSB estimation methods are almost all based on single-task learning(STL),where one model is built on the data from only one radar altimeter.In this paper,taking account of the data from multiple radar altimeters available,we introduced a multi-task learning method,called trace-norm regularized multi-task learning(TNR-MTL),for SSB estimation.Corresponding to each individual task,TNR-MLT involves only three parameters.Hence,it is easy to implement.More importantly,the convergence of TNR-MLT is theoretically guaranteed.Compared with the commonly used STL models,TNR-MTL can effectively utilize the shared information between data from multiple altimeters.During the training of TNR-MTL,we used the JASON-2 and JASON-3 cycle data to solve two correlated SSB estimation tasks.Then the optimal model was selected to estimate SSB on the JASON-2 and the HY-270-71 cycle intersection data.For the JSAON-2 cycle intersection data,the corrected variance(M)has been reduced by 0.60 cm^2 compared to the geophysical data records(GDR);while for the HY-2 cycle intersection data,M has been reduced by 1.30 cm^2 compared to GDR.Therefore,TNR-MTL is proved to be effective for the SSB estimation tasks.

New methods for processing and interpreting marine magnetic anomalies:Application to structure,oil and gas exploration,Kuril forearc,Barents and Caspian seas

New methods are presented for processing and interpretation of shallow marine differential magnetic data, including constructing maps of offshore total magnetic anomalies with an extremely high reso- lution of up to 1-2 nT, mapping weak anomalies of 5-10 nT caused by mineralization effects at the contacts of hydrocarbons with host rocks, estimating depths to upper and lower boundaries of anom- alous magnetic sources, and estimating thickness of magnetic layers and boundaries of tectonic blocks. Horizontal dimensions of tectonic blocks in the so-called ＂seismic gap＂ region in the central Kuril Arc vary from 10 to 100 km, with typical dimensions of 25-30 km. The area of the ＂seismic gap＂ is a zone of intense tectonic activity and recent volcanism. Deep sources causing magnetic anomalies in the area are similar to the ＂magnetic belt＂ near Hokkaido. In the southern and central parts of Barents Sea, tectonic blocks with widths of 30-100 kin, and upper and lower boundaries of magnetic layers ranging from depths of 10 to 5 km and 18 to 30 km are calculated. Models of the magnetic layer underlying the Mezen Basin in an inland part of the White Sea-Barents Sea paleorift indicate depths to the lower boundary of the layer of 12-30 km. Weak local magnetic anomalies of 2-5 nT in the northern and central Caspian Sea were identified using the new methods, and drilling confirms that the anomalies are related to concentrations of hydrocarbon. Two layers causing magnetic anomalies are identified in the northern Caspian Sea from magnetic anomaly spectra. The upper layer lies immediately beneath the sea bottom and the lower layer occurs at depths between 30-40 m and 150-200 m.

Effect of regularization parameters on geophysical reconstruction

In this paper we discuss the edge-preserving regularization method in the reconstruction of physical parameters from geophysical data such as seismic and ground-penetrating radar data. In the regularization method a potential function of model parameters and its corresponding functions are introduced. This method is stable and able to preserve boundaries, and protect resolution. The effect of regularization depends to a great extent on the suitable choice of regularization parameters. The influence of the edge-preserving parameters on the reconstruction results is investigated and the relationship between the regularization parameters and the error of data is described.