Earthquake detection probabilities and completeness magnitude in the northern margin of the Ordos Block

The assessment of the completeness of earthquake catalogs is a prerequisite for studying the patterns of seismic activity.In traditional approaches,the minimum magnitude of completeness(MC)is employed to evaluate catalog completeness,with events below MC being discarded,leading to the underutilization of the data.Detection probability is a more detailed measure of the catalog's completeness than MC;its use results in better model compatibility with data in seismic activity modeling and allows for more comprehensive utilization of seismic observation data across temporal,spatial,and magnitude dimensions.Using the magnitude-rank method and Maximum Curvature(MAXC)methods,we analyzed temporal variations in earthquake catalog completeness,finding that MC stabilized after 2010,which closely coincides with improvements in monitoring capabilities and the densification of seismic networks.Employing the probability-based magnitude of completeness(PMC)and entire magnitude range(EMR)methods,grounded in distinct foundational assumptions and computational principles,we analyzed the 2010-2023 earthquake catalog for the northern margin of the Ordos Block,aiming to assess the detection probability of earthquakes and the completeness of the earthquake catalog.The PMC method yielded the detection probability distribution for 76 stations in the distance-magnitude space.A scoring metric was designed based on station detection capabilities for small earthquakes in the near field.From the detection probabilities of stations,we inferred detection probabilities of the network for diff erent magnitude ranges and mapped the spatial distribution of the probability-based completeness magnitude.In the EMR method,we employed a segmented model fitted to the observed data to determine the detection probability and completeness magnitude for every grid point in the study region.We discussed the sample dependency and low-magnitude failure phenomena of the PMC method,noting the potential overestimation of detection probabilities for lower magnitudes and the underestimation of MC in areas with weaker monitoring capabilities.The results obtained via the two methods support these hypotheses.The assessment results indicate better monitoring capabilities on the eastern side of the study area but worse on the northwest side.The spatial distribution of network monitoring capabilities is uneven,correlating with the distribution of stations and showing significant diff erences in detection capabilities among diff erent stations.The truncation eff ects of data and station selection aff ected the evaluation results at the edges of the study area.Overall,both methods yielded detailed descriptions of the earthquake catalog,but careful selection of calculation parameters or adjustments based on the strengths of diff erent methods is necessary to correct potential biases.

Research on Pedestrian Detection Technology Based on MSR and Faster R-CNN

In order to avoid the problem of poor illumination characteristics and inaccurate positioning accuracy, this paper proposed a pedestrian detection algorithm suitable for low-light environments. The algorithm first applied the multi-scale Retinex image enhancement algorithm to the sample pre-processing of deep learning to improve the image resolution. Then the paper used the faster regional convolutional neural network to train the pedestrian detection model, extracted the pedestrian characteristics, and obtained the bounding boxes through classification and position regression. Finally, the pedestrian detection process was carried out by introducing the Soft-NMS algorithm, and the redundant bounding box was eliminated to obtain the best pedestrian detection position. The experimental results showed that the proposed detection algorithm achieves an average accuracy of 89.74% on the low-light dataset, and the pedestrian detection effect was more significant.