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Crevasse is an important characteristic of ice shelf internal structure, and also an important index to measure the stability of ice shelf. This study aims to detect crevasses from ICESat-2 data and obtain three-dimensional features of crevasse. Firstly, the change of along-track surface slope and the depth threshold is combined to obtain the crevasse bottom points and the crevasse edge points; Secondly, the distance between the crevasse bottom points and the fitted ice shelf surface by crevasse edge points is used as the crevasse bottom points depth; Thirdly, the crevasse direction is judged by the strong and weak laser position and the width is calculated by the crevasse edge points and crevasse direction. The Amery Ice Shelf(AIS) is chosen as the test area to validate the method by ATL06 data, combined with the Landsat-8 optical image. The depth of the crevasses in the Amery Ice Shelf is about range from 2.0m to 60.0m, and the width is mainly 400 to 1600m, and the width and length of L3 rift has dramatically change which is an indicator that the AIS is in a new calving cycle. The method in this paper can accurately obtain three-dimensional information of the crevasses, find crevasses with abnormal changes in depth and width, and provide effective help for predicting the calving of the ice shelf.

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Satellite laser altimetry can obtain submeter or even centimeter-level surface elevation information over a large range. However, the laser will inevitably be affected by clouds during transmission through the atmosphere, which seriously affects the accuracy of altimetry. In this paper, based on laser altimetry data, cloud optical depth inversion was realized by using the Fernald method. The influence of clouds on the echo waveform data was analyzed with actual data, and a method of cloud scattering error correction was proposed. The existing error correction methods are mostly based on the results of semi-analytical Monte Carlo simulations. In observations, it is difficult to synchronously obtain the parameters required for simulation, which significantly limits the method. Therefore, a method for correcting the cloud scattering error of satellite laser altimetry data based on an exponential model is also proposed. The experimental results show that when the cloud optical depth is 0 − 2 , the root mean square error of the model is 0.05, which can correct the height measurement deviation caused by the cloud to within 5 cm and improve the availability of the laser height measurement data affected by the cloud scattering.

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The Xujiaweizi fault depression is located in the northern part of the Songliao Basin, China. The Yingcheng Formation of the Xujiaweizi fault depression is a fractured tight volcanic reservoir. Many primary pores exist in the tight volcanic reservoirs of the Yingcheng Formation, but their connectivity is very poor. The degree of development of tectonic fractures determines the reservoir quality and the probability of hydrocarbon accumulation. To elucidate the fracture characteristics and their effects on hydrocarbon migration and accumulation, we analyze the fracture genetic types, characteristics, and controlling factors using data from cores, image logs, and thin sections. Then, we evaluate the matching relationship between tectonic fractures and hydrocarbon migration and accumulation by combining the evolution of the source rocks, analysis of the gas-source fault activity period and evolution of the cap rock sealing ability. We find two types of fractures developed in tight volcanic rocks: primary fractures and secondary fractures. Primary fractures mainly include cooling contraction fractures and cryptoexplosive fractures. Secondary fractures could be further divided into tectonic fractures, dissolution fractures, and weathering fractures. Among them, tectonic fractures are dominant. The distribution of tectonic fractures is controlled by lithology, lithofacies, faults, rock anisotropy, and an unconformity. Tectonic fractures are mainly formed in three phases. The time when the second phase of tectonic fractures formed (the Late Quantou-Qingshankou period) coincided with the peak hydrocarbon generation of the source rocks of the Shahezi Formation. Also at that time, the gas-source faults were active and the cap rock had a good top-seal capacity. Thus, the Late Quantou-Qingshankou period was the main period of natural gas accumulation.

In November 2019, the GaoFen-7(GF-7) satellite was equipped with China's first laser altimeter with full waveform recording capability, which obtains high-precision long-range three-dimensional coordinates. The influence of clouds is noticeable for laser transmission, and a footprint camera is used to determine laser pointing and to image the ground. However, the cloud inevitably appears in the laser footprint image. In this study, the authors propose a cloud detection scheme for footprint images based on deep learning. First, an adaptive pooling model is proposed according to the characteristics of the cloud region. Next, model fusion was performed based on the SegNet and U-Net training results. Finally, test time augmentation was used to enhance the data and to improve cloud detection accuracy. The experimental results show that the fusion result of the model was approximately 5% better than that of the traditional cloud detection algorithm, which improved the shortcomings of the traditional algorithm, such as poor detection effect for thin clouds and complex underlying cloud surfaces. The related conclusions have certain reference significance for GF-7 data processing and related research on footprint images.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.