Probing Volcanic Systems with Machine Learning for Quantitative Petrography
利用机器学习探测火山系统进行定量岩相学
基本信息
- 批准号:2592963
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:英国
- 项目类别:Studentship
- 财政年份:2021
- 资助国家:英国
- 起止时间:2021 至 无数据
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
This project centres on the development of machine learning techniques for the quantification of igneous rock properties from optical and electron microscopy. Following on from our recent successes in conversion of SEM maps into textural properties and quantification of a crystallisation timescale for the gigantic Laki eruption in Iceland (Neave et al., 2017), we plan to further explore the possibilities for machine learning in image analysis of igneous rocks (Andrew, 2018 - one of our partners from Zeiss). We have also recently demonstrated the power of Electron Backscatter Diffraction (EBSD) analysis of volcanic crystals (Wieser et al., 2020) and aim to realise the potential of this novel approach. Partners in Zeiss are also exploring merging 3D and 2D imaging in geological applications, and this may also be a focus of study.The student will acquire and process co-registered optical and electron microscope images of volcanic rocks from Iceland. It may also be desirable for the student to carry out field sampling in Iceland to obtain suitable samples for image analysis (e.g. mushy nodules with glassy ground mass). They will then develop image processing techniques, including machine learning, to automate phase identification and crystal segmentation. They will then develop further processing steps to extract useful textural information from the segmented images: crystal size and shape distributions, clustering analysis, quantification of contact angles, characterisation of internal deformation. These properties will then be compared with parameterisations of natural and experimental data to extract information about the physical processes involved (e.g. timescales magma rise under active volcanoes).
该项目的重点是开发机器学习技术,用于从光学和电子显微镜中量化火成岩的性质。在我们最近成功地将SEM图转换为冰岛巨大的拉基火山喷发的纹理特性和结晶时间尺度的量化之后(Neave等人,2017年),我们计划进一步探索机器学习在火成岩图像分析中的可能性(Andrew, 2018年-蔡司的合作伙伴之一)。我们最近还展示了电子后向散射衍射(EBSD)分析火山晶体的能力(Wieser等人,2020),并旨在实现这种新方法的潜力。蔡司的合作伙伴也在探索在地质应用中融合3D和2D成像,这也可能是研究的重点。该学生将获取并处理冰岛火山岩的光学和电子显微镜图像。学生也可能希望在冰岛进行实地采样,以获得适合图像分析的样本(例如,具有玻璃状地面质量的糊状结节)。然后,他们将开发图像处理技术,包括机器学习,以自动识别相位和晶体分割。然后,他们将开发进一步的处理步骤,从分割图像中提取有用的纹理信息:晶体尺寸和形状分布、聚类分析、接触角的量化、内部变形的表征。然后将这些特性与自然和实验数据的参数化进行比较,以提取有关物理过程的信息(例如,岩浆在活火山下上升的时间尺度)。
项目成果
期刊论文数量(0)
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其他文献
Internet-administered, low-intensity cognitive behavioral therapy for parents of children treated for cancer: A feasibility trial (ENGAGE).
针对癌症儿童父母的互联网管理、低强度认知行为疗法:可行性试验 (ENGAGE)。
- DOI:
10.1002/cam4.5377 - 发表时间:
2023-03 - 期刊:
- 影响因子:4
- 作者:
- 通讯作者:
Differences in child and adolescent exposure to unhealthy food and beverage advertising on television in a self-regulatory environment.
在自我监管的环境中,儿童和青少年在电视上接触不健康食品和饮料广告的情况存在差异。
- DOI:
10.1186/s12889-023-15027-w - 发表时间:
2023-03-23 - 期刊:
- 影响因子:4.5
- 作者:
- 通讯作者:
The association between rheumatoid arthritis and reduced estimated cardiorespiratory fitness is mediated by physical symptoms and negative emotions: a cross-sectional study.
类风湿性关节炎与估计心肺健康降低之间的关联是由身体症状和负面情绪介导的:一项横断面研究。
- DOI:
10.1007/s10067-023-06584-x - 发表时间:
2023-07 - 期刊:
- 影响因子:3.4
- 作者:
- 通讯作者:
ElasticBLAST: accelerating sequence search via cloud computing.
ElasticBLAST:通过云计算加速序列搜索。
- DOI:
10.1186/s12859-023-05245-9 - 发表时间:
2023-03-26 - 期刊:
- 影响因子:3
- 作者:
- 通讯作者:
Amplified EQCM-D detection of extracellular vesicles using 2D gold nanostructured arrays fabricated by block copolymer self-assembly.
使用通过嵌段共聚物自组装制造的 2D 金纳米结构阵列放大 EQCM-D 检测细胞外囊泡。
- DOI:
10.1039/d2nh00424k - 发表时间:
2023-03-27 - 期刊:
- 影响因子:9.7
- 作者:
- 通讯作者:
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