Seismic anisotropy and upper mantle dynamics in Alaska:A review of shear wave splitting analyses

Shear wave splitting(SWS)is regarded as the most effective geophysical method to delineate mantle flow fields by detecting seismic azimuthal anisotropy in the earth's upper mantle,especially in tectonically active regions such as subduction zones.The Aleutian-Alaska subduction zone has a convergence rate of approximately 50 mm/yr,with a trench length reaching nearly 2800 km.Such a long subduction zone has led to intensive continental deformation and numerous strong earthquakes in southern and central Alaska,while northern Alaska is relatively inactive.The sharp contrast makes Alaska a favorable locale to investigate the impact of subduction on mantle dynamics.Moreover,the uniqueness of this subduction zone,including the unusual subducting type,varying slab geometry,and atypical magmatic activity and composition,has intrigued the curiosity of many geoscientists.To identify different sources of seismic anisotropy beneath the Alaska region and probe the influence of a geometrically varying subducting slab on mantle dynamics,extensive SWS analyses have been conducted in the past decades.However,the insufficient station and azimuthal coverage,especially in early studies,not only led to some conflicting results but also strongly limited the in-depth investigation of layered anisotropy and the estimation of anisotropy depth.With the completion of the Transportable Array project in Alaska,recent studies have revealed more detailed mantle structures and characteristics based on the dense station coverage and newly collected massive seismic data.In this study,we review significant regional-and continental-scale SWS studies in the Alaska region and conclude the mantle flow fields therein,to understand how a geometrically varying subducting slab alters the regional mantle dynamics.The summarized mantle flow mechanisms are believed to be conducive to the understanding of seismic anisotropy patterns in other subduction zones with a complicated tectonic setting.

A Spatio-Temporal Heterogeneity Data Accuracy Detection Method Fused by GCN and TCN

Spatio-temporal heterogeneous data is the database for decisionmaking in many fields,and checking its accuracy can provide data support for making decisions.Due to the randomness,complexity,global and local correlation of spatiotemporal heterogeneous data in the temporal and spatial dimensions,traditional detection methods can not guarantee both detection speed and accuracy.Therefore,this article proposes a method for detecting the accuracy of spatiotemporal heterogeneous data by fusing graph convolution and temporal convolution networks.Firstly,the geographic weighting function is introduced and improved to quantify the degree of association between nodes and calculate the weighted adjacency value to simplify the complex topology.Secondly,design spatiotemporal convolutional units based on graph convolutional neural networks and temporal convolutional networks to improve detection speed and accuracy.Finally,the proposed method is compared with three methods,ARIMA,T-GCN,and STGCN,in real scenarios to verify its effectiveness in terms of detection speed,detection accuracy and stability.The experimental results show that the RMSE,MAE,and MAPE of this method are the smallest in the cases of simple connectivity and complex connectivity degree,which are 13.82/12.08,2.77/2.41,and 16.70/14.73,respectively.Also,it detects the shortest time of 672.31/887.36,respectively.In addition,the evaluation results are the same under different time periods of processing and complex topology environment,which indicates that the detection accuracy of this method is the highest and has good research value and application prospects.