Surface waves, usually referred to as Rayleigh waves and also called ground roll, generated near the surface are a kind of strong coherent noise to disturbs the accuracy of subsurface images with the recorded data from a seismic survey. In the case of the further challenges posed by rugged topography and variable near-surface velocities, the surface waves and other near-surface-related noises in the field data exhibit nonstationary characteristics, and their amplitude, frequency, and velocity vary with recorded time. Currently, the conventional methods to suppress this type of surface wave noise are based on steady-state assumptions. This article develops a nonstationary surface wave suppression method utilizing a deep neural network constrained by physical information. This method leverages the physical characteristics of surface waves and data-driven labels to constrain neural network training jointly. It enables effective training under small-sample conditions and effectively suppresses nonstationary surface waves in each prestack seismic data gathered. It also effectively decreases near-surface-related nonstationary interferences such as single-frequency noise and linear noise. Examples for both numerical and field datasets demonstrate that this near-surface-related noise suppression technology can precisely eliminate nonstationary interference signals such as surface waves, reconstructing effective waves with high efficiency, robustness, and generalization capability. Comparisons between the processing results of field data and those obtained using some typical commercial software confirm that the proposed deep neural network method surpasses traditional label-constrained network training limitations, offering superior suppression of surface waves, single-frequency noise, and linear interference in field data efficiently and stably with much less cost.

A modified version of Neural Decision Tree (NDT) which was initially proposed by Biau is presented in this paper with significantly extended statistical consistency theoretical results. Numerical experiments are performed to investigate several key features of this alternative NDT. Our experimental results demonstrate the attractiveness of NDT over the traditional artificial neural networks by adopting a strategically selected network structure with appropriate initial weights. Moreover, our results indicate that in all cases we have tested, NDT provides higher classification accuracy than a traditional neural network with a comparable degree of freedom. Empirical evidence also suggests that NDT is more stable and robust with respect to hyper-parameters commonly involved in the training of neural networks.

Calibration and validation (Cal/Val) toolkits are very critical for both satellite pre-launch end-to-end testing and post-launch real data quality check-out. In this paper, we present a AIRS-MODIS-VIIRS band mapping toolkit, which is designed for cross comparison and cross calibration of AIRS-MODIS-VIIRS thermal infrared bands based on the band-mapping approach we proposed. The main functionalities of this toolkit include spatial match-up between AIRS and MODIS foot prints, spectral simulation of VIIRS and MODIS thermal infrared bands with AIRS measurements, visual and statistical analyses among VIIRS, MODIS and AIRS thermal infrared bands for stratified scene characteristics, channels and scan angles. It also provides basic data manipulation and file format conversion capabilities. This toolkit can provide supports for VIIRS calibration and can be further enhanced into a NPOESS/NPP post-launch toolkit for real-time cross-instrument calibration and data quality check.

Approaches for establishing the absolute calibration of a newly deployed, satellite-borne radiometer have varied from aircraft under flights with previously calibrated sensors to vicarious calibration over known, benign backgrounds, utilizing radiative transfer models to generate top-of-atmosphere radiances. In this paper, we demonstrate the efficacy of this approach by presenting results of the cross comparison of two sensors that are known to be well calibrated, Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS). We focus on the results of the cross-comparison between MODIS and AIRS for the ranges of atmospheric and surface conditions embodied in a variety of common Earth scenes in this paper. We also investigate the dependence of the quality of the cross-calibration process as a function of the surface emissivity spectrum, phenomenology, and atmospheric conditions, identifying under what conditions the cross-calibration process is effective.