Irregularly sampled seismic data interpolation via wavelet-based convolutional block attention deep learning

Seismic data interpolation,especially irregularly sampled data interpolation,is a critical task for seismic processing and subsequent interpretation.Recently,with the development of machine learning and deep learning,convolutional neural networks(CNNs)are applied for interpolating irregularly sampled seismic data.CNN based approaches can address the apparent defects of traditional interpolation methods,such as the low computational efficiency and the difficulty on parameters selection.However,current CNN based methods only consider the temporal and spatial features of irregularly sampled seismic data,which fail to consider the frequency features of seismic data,i.e.,the multi-scale features.To overcome these drawbacks,we propose a wavelet-based convolutional block attention deep learning(W-CBADL)network for irregularly sampled seismic data reconstruction.We firstly introduce the discrete wavelet transform(DWT)and the inverse wavelet transform(IWT)to the commonly used U-Net by considering the multi-scale features of irregularly sampled seismic data.Moreover,we propose to adopt the convolutional block attention module(CBAM)to precisely restore sampled seismic traces,which could apply the attention to both channel and spatial dimensions.Finally,we adopt the proposed W-CBADL model to synthetic and pre-stack field data to evaluate its validity and effectiveness.The results demonstrate that the proposed W-CBADL model could reconstruct irregularly sampled seismic data more effectively and more efficiently than the state-of-the-art contrastive CNN based models.

Region stepwise reconstruction method based on two source–detector-separation groups for reconstructing background optical properties of two-layered slab sample

The accuracy of the background optical properties has a considerable effect on the quality of reconstructed images in near-infrared functional brain imaging based on continuous wave diffuse optical tomography（CW-DOT）. We propose a region stepwise reconstruction method in CW-DOT scheme for reconstructing the background absorption and reduced scattering coefficients of the two-layered slab sample with the known geometric information. According to the relation between the thickness of the top layer and source– detector separation, the conventional measurement data are divided into two groups and are employed to reconstruct the top and bottom background optical properties, respectively. The numerical simulation results demonstrate that the proposed method can reconstruct the background optical properties of two-layered slab sample effectively. The region-of-interest reconstruction results are better than those of the conventional simultaneous reconstruction method.

Social media and mobility landscape:Uncovering spatial patterns of urban human mobility with multi source data

In this paper,we present a three-step methodological framework,including location identification,bias modification,and out-of-sample validation,so as to promote human mobility analysis with social media data.More specifically,we propose ways of identifying personal activity-specific places and commuting patterns in Beijing,China,based on Weibo(China’s Twitter)check-in records,as well as modifying sample bias of check-in data with population synthesis technique.An independent citywide travel logistic survey is used as the benchmark for validating the results.Obvious differences are discerned from Weibo users’and survey respondents’activity-mobility patterns,while there is a large variation of population representativeness between data from the two sources.After bias modification,the similarity coefficient between commuting distance distributions of Weibo data and survey observations increases substantially from 23% to 63%.Synthetic data proves to be a satisfactory costeffective alternative source of mobility information.The proposed framework can inform many applications related to human mobility,ranging from transportation,through urban planning to transport emission modeling.