The initiation and development of fractures in rocks is the key part of many problems from academic to industrial, such as faulting, folding, rock mass engineering, reservoir characterization, etc. Conventional ways of evaluating the fracture historical deformations depend on the geologists' visual interpretation of indicating structures such as fault striations, fault steps, plumose structures, etc. on the fracture surface produced by previous deformations, and hence suffer from problems like subjectivity and the absence of obvious indicating structures. In this study, we propose a quantitative method to derive historical shear deformations of rock fractures from digital outcrop models (DOMs) based on the analysis of effects of fault striations and fault steps on the shear strength parameter of the fracture surface. A theoretical model that combines effects of fault striations, fault steps and isotropic base shear strength is fitted to the shear strength parameter. The amount of fault striations and fault steps and their occurrences are estimated, and the historical shear deformations can be inferred. The validity and the effectiveness of the proposed method was proved by testing it on a constructed fracture surface with idealized striations and a fracture surface with clear fault steps. The application of this method on an example outcrop shows an intuitive idea of how the rock mass was deformed and that the distribution, occurrence and mode of new fractures are strictly controlled by preexisting fractures, and hence emphasizes the importance of preexisting fractures in modeling the development of fracture systems.

Precipitation change, which is closely related to drought and flood disasters in China, affects billions of people every year, and the demand for subseasonal forecasting of precipitation is even more urgent. Subseasonal forecasting, which is more difficult than weather forecasting, however, has remained as a blank area in meteorological service for a long period of time. To improve the accuracy of subseasonal forecasting of China precipitation, this work introduces the machine learning method proposed by Hwang et al. in 2019 to predict the precipitation in China 2–6 weeks in advance. The authors used a non-linear regression model called local linear regression together with multitask feature election (MultiLLR) model and chosen 21 meteorological elements as candidate predictors to integrate diverse meteorological observation data. This method automatically eliminates irrelevant predictors so as to establish the forecast equations using multitask feature selection process. The experiments demonstrate that the pressure and Madden–Julian Oscillation (MJO) are the most important physical factors. The average prediction skill is 0.11 during 2011–2016, and there are seasonal differences in forecasting skills, evidenced by higher forecast skills of winter and spring seasons than summer and autumn seasons. The proposed method can provide effective and indicative guidance for the subseasonal prediction of precipitation in China. By adding another three factors, Arctic Oscillation (AO) index, Western North Pacific Monsoon (WNPM) index and Western North Pacific Subtropical High (WNPSH) index into the MultiLLR model, the authors find that AO can improve the forecast skill of China precipitation to the maximum extent from 0.11 to 0.13, followed by WNPSH. Moreover, the ensemble skill of our model and CFSv2 is 0.16. This work shows that our subseasonal prediction of China precipitation should be benefited from the MultiLLR model.

Satellite Image Time Series (SITS) is a data set that includes satellite images across several years with a high acquisition rate. Radiometric normalization is a fundamental and important preprocessing method for remote sensing applications using SITS due to the radiometric distortion caused by noise between images. Normalizing the subject image based on the reference image is a general strategy when using traditional radiometric normalization methods to normalize multi-temporal imagery (usually two or three scenes in different time phases). However, these methods are unsuitable for calibrating SITS because they cannot minimize the radiometric distortion between any pair of images in SITS. The existing relative radiometric normalization methods for SITS are based on linear assumptions, which cannot effectively reduce nonlinear radiometric distortion caused by continuously changing noise in SITS. To overcome this problem and obtain a more accurate SITS, we propose a nonlinear radiometric normalization model (NMAG) for SITS based on Artificial Neural Networks (ANN) and Greedy Algorithm (GA). In this method, GA is used to determine the correction order of SITS and calculate the error between the image to be corrected and normalized images, which avoids the selection of a single reference image. ANN is used to obtain the optimal solution of error function, which minimizes the radiometric distortion between different images in SITS. The SITS composed of 21 Landsat-8 images in Tianjin, China, from October 2017 to January 2019 was selected to test the method. We compared NMAG with other two contrast methods (Contrast Method 1 (CM1) and Contrast Method 2 (CM2)), and found that the average root mean square error (μRMSE) of NMAG (497.22) is significantly smaller than those of CM1 (641.39) and CM2 (543.47), and the accuracy of normalized SITS obtained using NMAG increases by 22.4% and 8.5% compared with CM1 and CM2, respectively. These experimental results confirm the effectiveness of NMAG in reducing radiometric distortion caused by continuously changing noise between images in SITS.

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