The correctness of the earthquake source spectra derived from array data with an iteratively stacking method is checked by analyzing the expressions of iterative stacking in each step. The expression of the finally derived source spectra term shows that it has nothing of the station term, but will be affected by the path term dependent on the source-receiver configuration, which is further confirmed by numerical simulations with iteratively stacking method. Considering stress drop might be wrongly estimated when stations or events are unevenly distributed, the paper provides a strategy to derive the correct stress drop in typical conditions of stationevent configurations. It will be helpful to correctly acquire seismic source information from seismic data.

Theoretical analysis quantitatively shows that high velocity anomaly near source, low velocity anomaly near receiver and the lateral velocity variation above the target inversion area have the influence of the same dimension of anomaly on the traditional inversion of 1-D wave velocity by triplicated wave arrival times. A quantitative computation scheme is proposed to remove the smearing effects with the help of regional or global tomography results when using 1-D inversion by triplicated wave arrival times. Tests imply that the velocity smearing could be eliminated to great extent and the real 1-D structure might be recovered.

An attention-based image recognition model is proposed. When analyze complex visual field or pattern, visual attention mechanism is used to detect saliency features in the image and drive the fixation point to scan the saliency features sequentially. During each fixation, the local pattern at the fixation point is memorized or matched. There are two parts in the memory of a complex pattern, the memory of local patterns that constitute the complex pattern and the memory of space relations between local patterns. Corresponding to memory process, the recognition process also contains two parts, the matching of local patterns and the matching of space relations between local patterns. An object is recognized only when there are enough numbers of local patterns is matched and the space relations between these local patterns are correct. This model is used in face detection in complex background. The results shows that the model can solves the problem of invariant recognition with respect to shift, rotation and scale, and the computing is fast and robust. This model likes human's vision system and is applicable.