ORCID Profile
0000-0002-0714-1741
Current Organisation
Northeastern University
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Publisher: Elsevier BV
Date: 02-2018
Publisher: IEEE
Date: 06-2012
Publisher: Copernicus GmbH
Date: 06-03-2023
Publisher: Springer Science and Business Media LLC
Date: 14-05-2022
Publisher: IEEE
Date: 07-2016
Publisher: IEEE
Date: 07-2011
Publisher: MDPI AG
Date: 24-10-2022
DOI: 10.3390/RS14215314
Abstract: The spatial characteristics of discontinuity traces play an important role in evaluations of the quality of rock masses. Most researchers have extracted discontinuity traces through the gray attributes of two-dimensional (2D) photo images or the geometric attributes of three-dimensional (3D) point clouds, while few researchers have paid attention to other important attributes of the original 3D point clouds, that is, the color attributes. By analyzing the color changes in a 3D point cloud, discontinuity traces in the smooth areas of a rock surface can be extracted, which cannot be obtained from the geometric attributes of the 3D point cloud. At the same time, a necessary filtering step has been designed to identify redundant shadow traces caused by sunlight on the rocks’ surface, and a multiscale spatial local binary pattern (MS-LBP) algorithm was proposed to eliminate the influence of shadows. Next, the geometric attributes of the 3D point cloud were fused to extract the potential discontinuity trace points on the rocks’ surface. For cases in which the potential discontinuity trace points are too scattered, a local line normalization thinning algorithm was proposed to refine the potential discontinuity trace points. Finally, an algorithm for establishing a two-way connection between a local vector buffer algorithm and a connectivity judgment algorithm was used to connect the discontinuity trace points to obtain the discontinuity traces of the rock mass’s surface. In addition, three datasets were used to compare the results extracted by existing methods. The results showed that the proposed method can extract the discontinuity traces of rock masses with higher accuracy, thereby providing data support for evaluations of the quality of rock masses and stability analyses.
Publisher: Copernicus GmbH
Date: 02-2022
Abstract: Abstract. Unlike some other well-known challenges such as facial recognition, where machine learning and inversion algorithms are widely developed, the geosciences suffer from a lack of large, labelled data sets that can be used to validate or train robust machine learning and inversion schemes. Publicly available 3D geological models are far too restricted in both number and the range of geological scenarios to serve these purposes. With reference to inverting geophysical data this problem is further exacerbated as in most cases real geophysical observations result from unknown 3D geology, and synthetic test data sets are often not particularly geological or geologically erse. To overcome these limitations, we have used the Noddy modelling platform to generate 1 million models, which represent the first publicly accessible massive training set for 3D geology and resulting gravity and magnetic data sets (0.5281/zenodo.4589883, Jessell, 2021). This model suite can be used to train machine learning systems and to provide comprehensive test suites for geophysical inversion. We describe the methodology for producing the model suite and discuss the opportunities such a model suite affords, as well as its limitations, and how we can grow and access this resource.
Publisher: IEEE
Date: 07-2011
Publisher: Elsevier BV
Date: 03-2018
Publisher: MDPI AG
Date: 17-02-2016
DOI: 10.3390/IJGI5020017
Publisher: Elsevier BV
Date: 12-2019
Publisher: Elsevier BV
Date: 04-2021
Publisher: Elsevier BV
Date: 11-2020
Publisher: IEEE
Date: 07-2010
Publisher: Elsevier BV
Date: 04-2021
Publisher: IEEE
Date: 2009
Publisher: Elsevier BV
Date: 12-2018
Publisher: IEEE
Date: 06-2012
Publisher: Copernicus GmbH
Date: 28-09-2021
Abstract: Abstract. Unlike some other well-known challenges such as facial recognition, where Machine Learning and Inversion algorithms are widely developed, the geosciences suffer from a lack of large, labelled datasets that can be used to validate or train robust Machine Learning and inversion schemes. Publicly available 3D geological models are far too restricted in both number and the range of geological scenarios to serve these purposes. With reference to inverting geophysical data this problem is further exacerbated as in most cases real geophysical observations result from unknown 3D geology, and synthetic test datasets are often not particularly geological, nor geologically erse. To overcome these limitations, we have used the Noddy modelling platform to generate one million models, which represent the first publicly accessible massive training set for 3D geology and resulting gravity and magnetic datasets. This model suite can be used to train Machine Learning systems, and to provide comprehensive test suites for geophysical inversion. We describe the methodology for producing the model suite, and discuss the opportunities such a model suit affords, as well as its limitations, and how we can grow and access this resource.
Publisher: Copernicus GmbH
Date: 20-04-2023
DOI: 10.5194/GMD-2023-11
Abstract: Abstract. Boreholes are one of the main tools for high-precision urban geology exploration and large-scale geological investigations. At present, machine learning based 3D geological modelling methods for borehole data have difficulty building a finer and more complex model and analysing the modelling results with uncertainty. In this paper, a semisupervised learning algorithm using pseudolabels for 3D geological modelling from borehole data is proposed. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang, and the modelling results are compared with implicit surface modelling and traditional machine learning modelling methods. Finally, an uncertainty analysis of the model is made. The results show that the method effectively expands the s le space, the modelling results perform well in terms of spatial morphology and geological semantics, and the proposed modelling method can achieve good modelling results for more complex geological regions.
Publisher: Copernicus GmbH
Date: 06-03-2023
DOI: 10.5194/GMD-2023-1
Abstract: Abstract. The three-dimensional (3D) visualization of geological structures and the dynamic simulation of geologic evolutionary processes are helpful when studying the formation of renowned geologic features. However, most of the existing 3D modelling software is based on raster models, which is unable to generate smooth geologic boundaries. This work proposes a three-dimensional and temporally dynamic (i.e., 4D) modelling method using parametric functions and vector data structures, which can dynamically build geologic evolutionary vector models of well-known geologic features. First, we extract the typical features of different kinds of geologic formations and represent them using different parameters. Next, appropriate parametric functions are selected to simulate these geologic formations according to the characteristics of the in idual structures. Then, we designed and developed a 4D vector modelling software to simulate the geologic evolution of these features. Finally, we simulated an area with complex geologic structures and selected six real-world geologic features, such as the Piqiang Fault in China and the Eye of the Sahara in the Sahara Desert, as case studies. The modelling results show that a regional geologic evolutionary model that contains smooth boundaries can be established quickly using this method. This work will support studies into the formation of these renowned geological features and make the representation of geologic processes more intuitive.
Publisher: Elsevier BV
Date: 06-2017
Start Date: 2022
End Date: 2025
Funder: National Natural Science Foundation of China
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