SediNet: a configurable deep learning model for mixed qualitative and quantitative optical granulometry

Buscombe Daniel

摘要

Abstract >I describe a configurable machine‐learning framework to estimate a suite of continuous and categorical sedimentological properties from photographic imagery of sediment, and to exemplify how machine learning can be a powerful and flexible tool for automated quantitative and qualitative measurements from remotely sensed imagery. The model is tested on a dataset consisting of 409 images and associated detailed label data. The data are from a much wider sedimentological spectrum than previous optical granulometry studies, consisting of both well‐ and poorly sorted sediment, terrigenous, carbonate, and volcaniclastic sands and gravels and their mixtures, and grain sizes spanning over two orders of magnitude. I demonstrate the model framework by configuring it in several ways, to estimate two categories (describing grain shape and population, respectively) and nine numeric grain size percentiles in pixels from a single input image. Grain size is then recovered using the physical size of a pixel. Finally, I demonstrate that the model can be configured and trained to estimate equivalent sieve diameters directly from image features, without the need for area‐to‐mass conversion formulas and without even knowing the scale of one pixel. Thus it is the only optical granulometry method proposed to date that does not necessarily require image scaling. The flexibility of the model framework should facilitate numerous application in the spatiotemporal monitoring of the grain size distribution, shape, mineralogy and other quantities of interest of sedimentary deposits as they evolve, as well as other texture‐based proxies extracted from remotely sensed imagery. ? 2019 John Wiley & Sons, Ltd. </abstract> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> <div class="translation abstracttxt"> <span class="zhankaihshouqi fivelineshidden" id="abstract"> <span>机译：</span><Abstract Type =“Main”XML：Lang =“en”> <标题类型=“main”>抽象</ title> 我描述了一种可配置的机器学习框架来估算来自沉积物的摄影图像的延迟连续和分类的沉积学特性，并举例说明机器学习如何是一种功能强大而灵活的工具，可从远程感测图像自动定量和定性测量。该模型在由409个图像和相关的详细标签数据组成的数据集上进行测试。数据来自比先前的光学粒度研究更广泛，由良好的沉积物，土壤和碳酸盐和火山和砾石及其混合物组成，以及跨越两个数量级的晶粒尺寸。我通过多种方式配置模型框架，以从单个输入图像估计两个类别（分别描述晶粒形状和群体）和九个数字粒度百分比的百分比。然后使用像素的物理尺寸恢复晶粒尺寸。最后，我证明了该模型可以被配置和训练以直接从图像特征估计等效筛直径，而不需要对区域到质量转换公式，并且甚至不知道一个像素的比例。因此，它是迄今为止不一定需要图像缩放的唯一迄今为止的光学粒度测量方法。模型框架的灵活性应促进谷物尺寸分布，形状，矿物学和其他沉积沉积物的其他兴趣的时空监测的许多应用，以及从远程感测图像中提取的其他基于纹理的代理。还2019年John Wiley＆amp; SONS，LTD。</ p> </摘要> </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> </div> <div class="record"> <h2 class="all_title" id="enpatent33" >著录项</h2> <ul> <li> <span class="lefttit">来源</span> <div style="width: 86%;vertical-align: text-top;display: inline-block;"> <a href='/journal-foreign-22319/'>《Earth Surface Processes and Landforms: The journal of the British Geomorphological Research Group》</a> <b style="margin: 0 2px;">|</b><span>2020年第3期</span><b style="margin: 0 2px;">|</b><span>共14页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Buscombe Daniel&option=202" target="_blank" rel="nofollow">Buscombe Daniel;</a> </p> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zkzz" style="display: none;">展开▼</span> </div> </li> <li> <div style="display: flex;"> <span class="lefttit">作者单位</span> <div style="position: relative;margin-left: 3px;max-width: 639px;"> <div class="threelineshidden zhankaihshouqi" id="fOrgthree"> <p>Geosciences Division School of Earth and SustainabilityNorthern Arizona UniversityFlagstaff AZ USA;</p> </div> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zhdw" style="display: none;">展开▼</span> </div> </div> </li> <li > <span class="lefttit">收录信息</span> <span style="width: 86%;vertical-align: text-top;display: inline-block;"></span> </li> <li> <span class="lefttit">原文格式</span> <span>PDF</span> </li> <li> <span class="lefttit">正文语种</span> <span>eng</span> </li> <li> <span class="lefttit">中图分类</span> <span><a href="https://www.zhangqiaokeyan.com/clc/163.html" title="地球物理学">地球物理学;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=optical granulometry&option=203" rel="nofollow">optical granulometry;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=photosieving&option=203" rel="nofollow">photosieving;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=machine learning&option=203" rel="nofollow">machine learning;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=deep learning&option=203" rel="nofollow">deep learning;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=sedimentology&option=203" rel="nofollow">sedimentology;</a> </p> <div class="translation"> 机译：光学粒度计;照片;机器学习;深入学习;沉积学; 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