CN113518912A - 用于土壤碳绘制的中子伽马分析的扫描模式应用 - Google Patents
用于土壤碳绘制的中子伽马分析的扫描模式应用 Download PDFInfo
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- CN113518912A CN113518912A CN201980091442.2A CN201980091442A CN113518912A CN 113518912 A CN113518912 A CN 113518912A CN 201980091442 A CN201980091442 A CN 201980091442A CN 113518912 A CN113518912 A CN 113518912A
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- Measurement Of Radiation (AREA)
Abstract
一种用于分析田地土壤含量的系统,该系统包括:数据采集单元,被配置为检测多个土壤样本中每一个的伽马光谱,其中田地的表面区域被划分成多个部分,并且多个土壤样本包括来自多个部分中每一个的至少一个土壤样本;导航单元,被配置为检测多个土壤样本中每一个的地理坐标;数据分析单元,被配置为将多个土壤样本中的每一个的检测到的伽马光谱与土壤样本的地理坐标相关联,并且基于检测到的伽马光谱确定每个土壤样本内的至少一个元素的重量百分比,以及元素含量图单元,被配置为生成指示田地的土壤内的至少一个元素的浓度的图。
Description
相关申请的交叉引用
本申请要求2018年12月7日提交的美国临时申请序列号62/776,822的35 USC§119(e)下的权益,该美国临时申请的全部公开内容通过引用并入本文。
技术领域
本公开内容涉及用于绘制土壤内至少一个化合物的分布的系统和方法。
背景技术
给定地理区域土壤的元素含量分析可以揭示土壤是否适合特定用途,诸如农业、娱乐等。土壤含量分析的其他用途包括确定碳信用和养分的可利用性的水平或养分引入的需要,以评估目前和预计的产量以及施肥的潜在收益性。
土壤分析可以从土壤样本收集开始,使得实际上仅极小部分田地在实验室中进行分析。例如,土壤元素含量分析的一种常见方法是混合采样,其中从田地中随机选择的位置收集土壤的几个子样本。然后混合子样本,并分析混合物的元素含量。在一些情况下,被揭示包含在混合物内的给定元素的量可以被视为被分析的田地整个区域内该元素的平均量。
虽然子样本的实际数量可能基于田地的大小和均匀性而略有不同,但子样本的数量通常不超过20个,并且有时总和少于被分析面积的0.01%。此外,大多数土壤测试和分析系统不容易可适用于测试多于几个的样本,并且充其量提供田地的土壤真实元素含量的高水平近似。由于土壤元素含量准确性的重要性怎么强调都不为过,因此需要一种方法来为给定的田地区域产生更详细和准确的元素含量信息。
发明内容
一种用于分析田地土壤含量的系统,该系统包括:数据采集单元,被配置为检测多个土壤样本中每一个的伽马光谱,其中田地的表面区域被划分成多个部分,并且多个土壤样本包括来自多个部分中每一个的至少一个土壤样本;导航单元,被配置为检测多个土壤样本中的每一个的地理坐标;数据分析单元,被配置为将多个土壤样本中的每一个的检测到的伽马光谱与土壤样本的地理坐标相关联,并且基于检测到的伽马光谱确定土壤样本中的每一个内的至少一个元素的重量百分比,以及元素含量图单元,被配置为生成指示田地的土壤内的至少一个元素的浓度的图。
一种用于分析农田土壤含量的方法,该方法包括将田地的表面区域划分成多个部分,扫描每个部分内的至少一个土壤样本以检测土壤样本的伽马光谱,将检测到的光谱与土壤样本的地理位置相关联,基于检测到的光谱计算土壤样本内至少一个元素的量,并生成指示田地每个部分内至少一个元素的量的图。
一种用于分析田地土壤的元素含量的系统,该系统包括:数据采集单元,被配置为收集至少一个土壤样本的伽马光谱;导航单元,被配置为提供土壤样本的地理坐标;数据分析单元,被配置为将收集的伽马光谱与土壤样本的地理坐标相关联,并计算土壤样本内元素的重量百分比;以及元素含量图单元,被配置为基于计算的重量百分比生成指示土壤样本内至少一个元素的浓度的图。
附图说明
在附图中,本公开中描述的概念以示例的方式而非限制的方式图示。为了图示的简单性和清楚性,图中图示的元件不一定按比例绘制。例如,为了清楚起见,一些元件的尺寸可以相对于其他元件被放大。此外,在认为合适的情况下,在各图中重复了参考标记来指示对应或类似的元件。详细描述特别地参考附图,其中:
图1是图示伽马分析装置的示例实现的简化图解;
图2A-2B是图示伽马分析装置的示例移动实现的简化图解;
图3是图示将由伽马分析装置扫描的田地的多个部分的简化图解;
图4A是图示峰面积和硅的重量百分比之间的示例关系的曲线图;
图4B是图示图4A中图示的曲线图的一部分的曲线图;
图5是图示钾的伽马分析产量和能量之间的示例关系的曲线图;
图6是图示用于确定土壤钾含量的示例方法的简化图解;
图7是图示第一扫描田地的多个部分的简化图解;
图8是图示第一扫描田地的碳分布图的简化图解;
图9是图示第一扫描田地的硅分布图的简化图解;
图10和图11是图示从在两个不同的日子执行的第二扫描田地的扫描操作生成的图的简化图解;
图12是图示基于图10和图11的扫描数据的组合的碳分布图的简化图解;
图13是图示第二扫描田地的硅分布图的简化图解;
图14和图15是分别图示使用本公开的方法生成的第一图和基于与第一图同时执行的湿度测量生成的第二图的简化图解;和
图16A和图16B是用于确定田地土壤元素含量的示例过程算法的框图。
具体实施方式
虽然本公开的概念易于进行各种修改和替代形式,但是其特定实施例已经通过各图中的示例示出,并且将在本文详细描述。然而,应当理解,不意图将本公开的概念限制于所公开的特定形式,而是相反,意图是覆盖与本公开和所附权利要求一致的所有修改、等同物和替代物。
说明书中对“一个实施例”、“实施例”、“说明性实施例”等的引用指示所描述的实施例可以包括特定的特征、结构或特性,但是每个实施例可以包括或可以不一定包括该特定的特征、结构或特性。此外,此类短语不一定指代相同实施例。此外,当结合实施例描述特定特征、结构或特性时,认为结合其他实施例实现这样的特征、结构或特性在本领域技术人员的知识内,无论是否明确描述。
在一些情况下,所公开的实施例可以用硬件、固件、软件或其任何组合来实现。所公开的实施例也可以实现为由暂时性或非暂时性计算机可读存储介质承载或存储在其上的指令,其可以由一个或多个处理器读取和执行。计算机可读存储介质可以体现为用于以计算设备可读的形式存储或传输信息的任何存储设备、机构或其他物理结构(例如,易失性或非易失性存储器、介质盘或其他介质设备)。
在附图中,一些结构或方法特征可以以特定的布置和/或次序示出。然而,应当领会,可能不需要此类特定布置和/或次序。相反,在一些实施例中,此类特征可以以不同于说明性图中所示的方式和/或次序布置。附加地,在特定图中包括结构或方法特征不意味着暗示在所有实施例中都需要这样的特征,并且在一些实施例中,可以不包括这样的特征或者这样的特征可以与其他特征相组合。
用于开发给定田地的土壤的详细和准确的元素含量的示例系统可以包括中子生成器设备和用于扫描该田地的至少一部分的多个伽马检测器(例如钠碘伽马检测器)以及用于存储和分析扫描结果并生成指示该田地的部分的元素含量的图的计算系统。该示例系统可以是移动系统,并且可以被配置为在田地的大部分上行进,以执行土壤扫描。根据本公开的一些实施例,土壤中的元素(C、Si、O、H、K、Cl等)含量可以使用由伽马检测器捕获的测量光谱来计算。
该示例系统可以进一步被配置为与全球定位系统(GPS)设备通信,以在扫描过程期间捕获土壤的地理位置。在一个示例中,在扫描期间标识的元素含量数据可以与由GPS设备提供的地理坐标组合(或关联)。附加地或替代地,基于从扫描确定的元素含量和相关联的地理坐标,示例系统可以被配置为生成适合用于农业和其他目的的元素分布图。
图1图示了用于分析田地120的土壤样本124的示例系统100。系统100可以包括用于土壤元素分析的一个或多个组件。在一个示例中,系统100包括中子生成器设备102、多个伽马检测器104、分裂电子设备106和处理单元(或处理器)110。虽然没有单独图示,但是示例系统100可以包括一个或多个附加或替代组件,诸如但不限于处理和存储器/数据存储单元和设备、音频和视频扫描设备等,其被配置为获取、处理、存储和/或分析元素分析数据。附加地或替代地,可以使用任何中子脉冲源102,并且本公开不限于中子生成器102。此外,基于加速器的中子源,诸如氘-氘(D-D)和氘-氚(D-T)聚变中子生成器以及允许中子发射118的电子控制的其他中子源是优选的。特别地,在实践本发明中,D-T中子生成器可能是优选的。此类生成器可以是脉冲调制的(即,对于各种长度关闭和打开),从而提供对中子发射118的电子控制。
处理单元110可以被配置为监视和操作中子生成器设备102、伽马检测器104和分裂电子设备106,以实施土壤120的扫描和扫描期间收集的光谱数据的分析。系统100可以与全球定位系统(GPS)设备112通信,以接收一个或多个地理坐标。在示例中,处理单元110可以被配置为请求指示土壤样本124的地理位置的地理坐标。在另一示例中,处理单元110可以将接收到的土壤样本124的地理位置与指示该土壤样本124的检测到的伽马光谱116的数据相关联。
系统100的一个或多个部件可以设置和/或固定到固定装置、手推车或另一刚性或半刚性结构114上。结构114可以是自推进的,或直接或远程驱动的,以在田地120的至少一部分之上行进来扫描土壤122。图2A图示了土壤分析系统100的示例移动实现方式200-A,其中系统100的至少一部分设置在拖车202中。拖车202可以由机动车辆206拉动204穿过田地120,机动车辆206是燃气动力的或是电池动力的。附加地或替代地,图2B图示了系统100的示例移动实现方式200-B,其中系统100的一个或多个组件被设置在远程控制无人驾驶航空器上和/或固定到远程控制无人驾驶航空器,诸如无人驾驶飞机220。
图3-15图示了由系统100执行的示例过程,以扫描田地120的土壤,分析和存储对应于田地120的土壤122的扫描数据,并基于扫描期间获取的光谱数据生成田地120的元素含量图。处理单元110可以执行一个或多个过程,诸如但不限于分析、计算和图生成任务。附加地或替代地,在扫描期间由系统110的一个或多个组件收集的扫描数据可以从系统100下载或以其他方式从系统100提取,并导出用于在远程(例如,基于云的)计算系统上的进一步处理。也设想其他扫描数据收集、处理和分析方法。
图3图示了土壤分析系统100要扫描的区域302的示例图解300。在一个示例中,系统100的处理单元110可以被配置为在发起扫描操作之前将田地120划分成多个部分(框或地点)306。在一些情况下,部分(下文中,地点)306的数量可以基于田地大小和一个或多个景观特征的存在。例如,当确定地点306的大小时,可以考虑扫描的适当速度(例如,~5 km/h)和地形轮廓。每个地点306可以包括相对均匀的地形轮廓。在田地划分期间,系统100的处理单元110可以被配置为响应于检测到地形类型的改变,诸如穿过田地120的沥青道路308等,和/或检测到地形的轮廓和构成的改变,例如响应于检测到地形中的低点,来指定单独的地点306。给定影响扫描条件的前述地形相关和其他因素,多个地点306中的每个地点306在大小方面可以从小于~100 m2变化到~1000 m2以及更大。在图3中图示的示例中,田地120的总面积大约为800 m2,并且地点数量为12个。
系统100的处理单元110可以被配置为在预定义的时间段内扫描土壤122,以便在标识地点306的土壤122内给定元素的存在或不存在中和/或在测量地点306的土壤122内元素的量中实现预定义的期望准确度值。在一些情况下,系统100的处理单元110扫描每个地点306的时间段可以基于元素的伽马峰值产量,该伽马峰值产量进而可以受到地点306的土壤内该元素的量、被标识的元素的化学、分子和/或解剖结构以及一个或多个其他特性影响。
附加地或替代地,基于测量的预定义的期望准确度值扫描每个地点306的时间段。例如,为了达到±0.5 w%的碳含量测量准确度,一个地点的采集时间可以是15分钟。作为另一个示例,具有±0.5-1 w%的可接受准确度的硅的系统100测量时间可以是~5分钟,这归因于硅的硅伽马峰值产量是碳峰值产量的几倍(归因于其在土壤122中的更高含量)。
系统100可以被配置为标识多个地点306中的每一个的地理位置,例如地理坐标,并且在图的数字呈现上标记和编号每个地点306。在一个示例中,系统100的处理单元110可以被配置为从GPS单元112(在系统100的内部或外部)请求和接收每个地点306的地理坐标,系统100使用例如有线网络连接、另一种类型的网络通信介质或远程或短程无线网络、诸如但不限于无线局域网(LAN)、蓝牙、广域网(WAN)等与GPS单元112通信。因此,系统100的处理单元110可以被配置为获取和跟踪系统100相对于正在进行的路径的当前地理位置,该当前地理位置在运行时间显示在采样田地的图上。
在扫描操作期间,系统可以为每个单独地点306确定一段时间,在该段时间期间,土壤122的伽马光谱已经被扫描用于地点306。系统100的处理单元110可以被配置为响应于在其期间已经扫描了给定地点306的土壤122的时间段来变更该地点306在所显示的图上的颜色。在一些情况下,系统可以基于在扫描运行时间期间在每个地点306内收集的总采集时间来改变或变更显示图上的地点306的颜色。在一个示例中,系统100的处理单元110可以使用颜色编码来指示已经获取了用于该地点306的准确土壤元素确定的预定义的足够量的数据,诸如当所有地点306都变成预定义的颜色时,处理单元110可以发出指示扫描操作已经完成的对应命令和/或通知。
在扫描期间获取的来自每个检测器104的INS和TNC光谱在运行时间显示在膝上型计算机屏幕上。处理器110可以被配置为在预定义的时间段,例如每30秒,存储给定地点306的土壤122的伽马光谱(来自伽马检测器104中的每一个的INS和TNC光谱)和该地点306的对应地理坐标。系统100可以被配置为定期检查GPS设备112和记录装备中的处理单元110和/或存储器之间是否维持连接。此外,响应于检测到GPS设备和记录装备之间的连接已经丢失,系统100可以被配置为发出对应的警报,并且可以暂停扫描数据的记录,以防止记录不准确的数据。在一些情况下,取决于扫描时间,保存的光谱总数可以达到几千或更多。在扫描之后,保存的光谱可以被传送到系统100的一个或多个数据处理组件(未示出)。
在开始时,计算每30 s测量的净INS光谱。
可以使用等式(2)来计算寿命(LT),使得
其中RT指示以秒(s)为单位的现实测量时间,OCR指示输出计数率,并且ICR指示输入计数率。在一些情况下,RT、OCR和ICR参数可以由用于执行扫描操作的光谱采集硬件的规格来定义。此外,RT、OCR和ICR参数值可以包括在每个对应的光谱文件中。
在示例中,每个检测器104可以包括唯一的能量校准,其指示能量和通道号之间的相关性。在扫描的给定日子或时间存在的环境条件的改变可能引起相关性改变。为了将所有光谱进行一个能量校准,可以移位光谱,使得主峰(例如,硅和氧峰)的质心在所有光谱中在相同通道中。
进一步参考图3,具有中点312坐标的数据可以按地点306分类,使得例如使用数字4、5、6、7、8和9标识的中点312被归于地点#2等等。给定地点306- n的加权中心310可以基于归因于该地点306-n的中点312来确定。可以使用等式(5)来确定以每通道cps为单位的地点S的平均光谱,使得
因此,每个地点306的平均光谱可以用于确定多个地点306中每一个的元素含量。元素含量可以从对应的元素(原子核)伽马峰面积计算。峰面积可以通过使用IGOR软件的设计的软件从光谱计算。在一些情况下,可以基于先前定义的校准数据或其他参数或值来计算元素含量分布。
如至少参考图8-15进一步讨论的,碳、硅、氢和钾的元素分布图可以基于扫描操作期间收集的数据绘制。碳和硅的含量分布可以从光谱限定。此外,可以使用TNC光谱数据来确定氢含量。钾含量和绘制可以基于自然伽马背景光谱测量来确定。
等式(6)可以用于确定碳含量(以重量百分比Cw%为单位),使得
图4图示了用于基于校准相关性确定硅的元素含量的示例图解400。例如,硅校准相关性的合理近似可以基于几个点来确定,例如四(4)个数据点和零-零点。在一些情况下,可以使用附加的扫描数据来继续改进硅校准。
因此,硅含量可以基于等式(7)来确定,使得
土壤氢分布可以基于TNC光谱中的氢峰面积确定,其中质心峰为2.223 MeV。在一个示例中,为了定义氢峰面积,第r个记录和第i个检测器104的TNC光谱可以使用等式(8)在逐通道的基础上计算,使得
移位、汇总多个伽马检测器104之上的光谱、确定平均寿命和中点312的地理位置、按地点306分类光谱、确定地点306的加权中心310和地点306的平均TNC光谱可以以类似于光谱确定的方式的方式来确定。具体地,
土壤钾分布图可以以类似于关于通过中子-伽马技术分析(例如,通过中子脉冲源102和/或伽马检测器104以及系统100的相关联组件)的其他元素(诸如氢和硅)概述的过程的方式生成。附加地或替代地,钾含量可以严格基于从土壤收集的自然伽马光谱来确定,而不依赖于土壤的中子辐照。例如,同位素可以自然存在于含钾化合物的钾同位素混合物内。该同位素在钾化合物中的已知丰度为,并且它是放射性(年)。的放射性衰变伴随着能量为1.46 MeV的伽马射线发射,伽马辐射其是自然放射性的主要成分之一。因此,土壤中钾的存在可以基于测量的伽马线强度来确定。
图5图示了自然放射性的伽马光谱的示例曲线图500,并且可以指示由直接安装在土壤122表面上的系统100在0.5小时期间测量的光谱。在一个示例中,虚线506可以指示当含钾物质(~11 kg)(总重量22.7 kg)被放置在测量系统下时测量的伽马光谱,其中质心处于或大约为1.46 MeV的显著峰(诸如在能量值508-1和508-2之间)指示钾的存在。
图6图示了用于估计钾校准系数的方法的示例图解600。对于第一近似,假设土壤钾均匀分布在半径为R的半球形体积中。伽马检测器104处于该半球602的中心608。如果在单位体积dV 606中存在Kw% 606,并且材料密度为d,则、,能量为1.46 MeV的伽马射线将表现为
其中t是伽马射线配准效率,是物质中1.46 MeV伽马线的质量吸附系数,距离l是dV和伽马检测器104之间的距离,并且R是半球半径。含钾物质中Kw%为。该物质的体密度为,并且关于该物质的半球半径为。含钾物质(KCl)对1.6 MeV的质量衰减系数为,并且值。对于含钾物质,光谱中的峰面积计算为237 cps,如图5中虚线所图示。从这里,t值可以估计为69.4。
土壤密度可以取成等于并且(主要土壤元素为Si和O)进行估计。然后,对于无限半径的土壤,并且土壤的钾峰面积为14 cps,如图5中实线所图示。峰面积与G和伽马射线配准效率t成比例,根据这些值,土壤中钾的校准系数可以估计为0.15 Kw%/cps,并且土壤中Kw%可以估计为2%。该值与土壤中的平均钾含量一致。虽然为了更好的准确度,应该用几个参考样本重复该校准,但是等式(15)的估计可以用于给定的一系列测量,使得
地理坐标和元素含量(以重量%为单位)的测量数据集用于创建元素分布图。可以使用局部多项式插值或另一种计算方法来生成该图。该图被放在地理基础图上。生成的元素分布图可以包括指示一个或多个基本方向(诸如北、南、东和西)的箭头或另一种类型的图标,以指示图的方向定向。在其他示例中,生成的元素分布图可以被自动定向,使得向上的垂直方向表示向北的方向,等等。在一些情况下,生成的图可以包括指示给定元素或多个元素的元素含量的一个或多个范围的对应图例和/或比例尺。生成的元素分布图可以包括等高线图,该等高线图包括指示具有相同含量的区域的一个或多个等高线值标签。至少在图8-15中图示了基于伽马光谱数据分析生成的元素分布图的一些示例。
第一个扫描田地包括第一个总面积,例如~6公顷(ha),并且第二个扫描田地包括第二个总面积,例如~23 ha,其中土壤类型分别为Marvyn 壤质砂土(loamy sand)和Marlboro 壤质砂土(1-6%坡度)。
图7图示了被划分成多个地点306的第一扫描田地702的示例图解700,其中第一多个地点306处于第一扫描田地702上,并且多个地点704位于与第一扫描田地702相邻的道路上。作为一个示例,在表1中示出每个地点306中的多个中点和每个地点306中的总测量时间。
图8图示了至少参考图7描述的第一扫描田地702的碳分布图808的数字呈现的示例图解800。在一个示例中,碳含量分布802从南到北(例如,如通过参考元素802-1至802-4所图示)从0.5 w%增加到2.0 w%,而道路上的碳含量与田地122相比极高,达到18 w%。图9图示了至少参考图7描述的第一扫描田地702的硅分布图908的数字呈现的示例图解900。在一个示例中,第一扫描田地702上的硅含量分布902变化,使得硅含量通常保持在44±2w %的范围内。在另一个示例中,在与田地702相邻的道路上,硅含量902非常低(大约10 w%),这意味着道路可能由诸如碳酸盐砾石的矿物组成,并且具有非常少的硅。
图10和图11分别图示了在两个不同的日子、诸如在2019年4月11日和2019年4月17日捕获的第二扫描田地1004上的碳分布1002、1102的示例图1000和1100,两个日期之间的天气稳定(晴朗),其中第二扫描田地1004的面积近似为13.6 ha。该比较表明两个图非常相似,其中轮廓1002、1102中的一些微小差异被限制在图1000和1100的相应北部部分。因此,对第二扫描田地1004的多次扫描确认,在近似相同的天气条件下,扫描结果和从收集的扫描数据生成的图保持相对一致。因此,使用伽马分析装置系统100来收集土壤122的扫描数据并基于所收集的扫描数据生成土壤122的元素含量分布图的方法和途径足够准确,并且该方法的结果在类似的基本条件下是可再现的。图12图示了指示碳(C)的元素分布1202的图1200,该图由用于生成图10和图11的图的数据集1000、1100的组合产生,并且可以是更可靠的元素含量图。图13图示了硅分布图1300,该硅分布图1300基于在两个不同的日子(本文没有单独图示)执行的第二扫描田地1004的扫描操作的组合数据来指示硅元素分布1302。
图14图示了第二扫描田地1004的土壤122中氢的元素分布1402的示例图1400。在一个示例中,氢分布图1400指代氢峰面积的分布,其中元素号1404指示每个地点306的相应峰值。图15图示了从使用湿度测量(使用TDR-300土壤湿度计)执行的氢扫描生成的示例图1500。电极长度为7英寸(in),并且在测量期间选择“沙地”作为土壤类型模式。通过该仪器测量的土壤湿度图1500。由TDR-300进行的湿度测量1502的相对误差(包括对应的峰值1504)范围在近似12%和20%之间。尽管由TDR 300进行的湿度确定存在该相对误差值,但是图14和图15的图1400和1500的比较分别证明了两个仪器之间的相似性,并且可以得出结论,使用中子伽马分析进行湿度分布绘制1402可以产生准确的结果。
图16A和图16B图示了用于确定田地120的土壤122的元素含量的示例过程1600。过程1600可以在框1602开始,其中处理单元110接收对给定田地120执行元素土壤分析的请求。在一些示例中,请求可以是用户或系统生成的。此外,还设想用于发起元素土壤分析过程1600的其他方法。
响应于该请求,在框1604,处理单元110可以检测要扫描的田地120的一个或多个外部边界。在一个示例中,处理单元110可以基于包括要扫描的田地120的至少一部分的地理图的数字呈现、基于田地120的实际扫描(例如,视频、声纳等)或其某种组合来检测田地120的外部边界。在外部边界标识过程期间分析的地理图可以包括田地120的近似或精确地理坐标、田地120的纬度和经度、田地120的面积、田地120相对于四个基本方向的定向以及足以建立田地120的地理空间、相对和特定位置的其他数据参数。
在框1606,处理单元110可以将待扫描的田地120划分成多个部分或地点306。例如,处理单元110可以基于地形轮廓、地形的同质性或异质性和/或地形特征的存在或不存在(无论是自然的还是人为的),诸如山丘、山脊、鞍状物、洼地、道路、结构、水特征、植被等,将田地120划分成多部分。在一些情况下,每个地点306可以包括相对均匀的地形轮廓。在田地划分期间,系统100的处理单元110可以被配置为响应于检测到地形的改变,诸如穿过田地120的沥青柏油路308等,和/或检测到地形的轮廓和构成的改变,例如响应于检测到地形中的低点,来指定单独的地点306。给定影响扫描条件的前述地形相关和其他因素,多个地点306中的每个地点306在大小方面可以从小于~100 m2变化到~1000 m2以及更大,使得具有近似800 m2的总面积的给定田地120可以包括十二(12)个地点,等等。
处理单元110可以被配置为在框1608发起对田地120的第一部分的第一土壤样本的扫描。在一个示例中,处理单元110可以使用中子脉冲源102来扫描第一部分/地点306的第一土壤样本。附加地或替代地,在框1608,处理单元110可以被配置为例如使用伽马检测器104检测第一土壤样本的伽马光谱。在框1610,处理单元110可以被配置为请求第一土壤样本的地理位置。在一些情况下,处理单元110可以与无论在系统100内部还是外部的GPS设备112通信,并且可以被配置为请求和接收指示田地120的第一部分306的第一土壤样本的位置的地理坐标或其他地理空间定位参数。
在框1612,处理单元110可以将检测到的第一土壤样本的伽马116光谱数据与接收到的第一土壤样本的地理坐标相关联。在一个示例中,在框1612,处理单元110可以将扫描数据和相关联的地理坐标存储在与其直接连接的数据存储设备中。在其他示例中,处理单元110可以与外部、远程或离场(off-site)存储服务器和/或云联网和数据存储设备或系统通信。
在框1614,处理单元110可以确定是否已经检测到多个地点306中的同一地点内的下一个土壤样本。在一个示例中,处理单元110可以检测系统100(例如结构114、中子脉冲源102和/或伽马检测器104)相对于被扫描的田地120的区域和/或地点306的区域的当前地理位置。附加地或替代地,处理单元110可以操作系统100来改变其自身的地理位置,使得可以确定下一个土壤样本124和/或下一个地点306的存在或不存在。还设想用于确定系统100是否需要执行另外的数据收集的其他场景和方法。例如,系统100可以被配置为显示用户通知,其请求确认需要扫描另外的土壤样本124和/或地点306以进行元素土壤含量分析。响应于下一个土壤样本可在当前地点306内被扫描,处理单元110可以返回到框1608以扫描地点306内的下一个可用土壤样本。
响应于确定当前地点306的所有土壤样本已经被扫描,处理单元110可以在框1616确定是否已经检测到多个地点306中的下一个。在一个示例中,处理单元110可以检测系统100(例如结构114、中子脉冲源102和/或伽马检测器104)相对于被扫描的田地120的区域和/或地点306的区域的当前地理位置。响应于在框1616确定多个地点306中的下一个地点306在被扫描的田地120内可用,处理单元110可以返回到框1608以扫描下一个地点306内的第一土壤样本124,等等。附加地或替代地,响应于已经扫描了田地120的所有地点306,处理单元110可以继续分析收集的扫描数据。
在框1618,处理单元110可以被配置为分析多个元素中至少一个的一个或多个峰值的所收集的伽马光谱,所述多个元素诸如但不限于C、Si、O、H、K、Cl等。如至少参考图3-6和图10-15所述,用于确定给定元素的峰值的方法可以变化。在一些情况下,处理单元110可以被配置为标识中点、加权中心以及与土壤120的元素含量分析相关联的其他参数值。附加地或替代地,处理单元110可以被配置为按部分/地点306对每个元素的标识的峰值进行分类。也设想用于分析收集的光谱的其他操作和方法。
在框1620,处理单元110可以被配置为基于在使用系统100进行的扫描操作期间收集的伽马光谱数据来生成元素分布图。如先前讨论的,元素分布图可以使用局部多项式插值或另一种计算方法生成,并且可以叠加在地理基础图上。生成的元素分布图可以包括指示一个或多个基本方向(诸如北、南、东和西)的箭头或另一种类型的图标,以指示图的方向定向。在其他示例中,生成的元素分布图可以被自动定向,使得向上的垂直方向表示向北的方向,等等。在一些情况下,生成的图可以包括指示给定元素或多个元素的元素含量的一个或多个范围的对应图例和/或比例尺。生成的元素分布图可以包括等高线图,该等高线图包括指示具有相同含量的区域的一个或多个等高线值标签。
过程1600然后可以结束。在一些情况下,处理单元110可以被配置为基于收集的伽马光谱数据重复用于生成元素分布图的一个或多个过程。
虽然在各图和前面的描述中已经详细描述了某些说明性实施例,但是这样的说明和描述在特性方面应当被认为是示例性的,而不是限制性的,应当理解,仅已示出和描述了说明性实施例,并且期望保护落入本公开精神内的所有改变和修改。存在本公开的多个优点,其起因于本文描述的装置、系统和方法的各种特征。将注意到,本公开的装置、系统和方法的替代实施例可能不包括所描述的所有特征,但是仍然受益于此类特征的优点中的至少一些。本领域的普通技术人员可以容易地设计他们自己的装置、系统和方法的实现,其结合了本公开的特征中的一个或多个。
Claims (21)
1.一种用于分析田地土壤含量的系统,所述系统包括:
数据采集单元,被配置为检测多个土壤样本中每一个的伽马光谱,其中田地的表面区域被划分成多个部分,并且多个土壤样本包括来自所述多个部分中每一个的至少一个土壤样本;
导航单元,被配置为检测多个土壤样本中每一个的地理坐标;
数据分析单元,被配置为将多个土壤样本中的每一个的检测到的伽马光谱与土壤样本的地理坐标相关联,并且基于检测到的伽马光谱确定土壤样本中每一个内的至少一个元素的重量百分比;和
元素含量图单元,被配置为生成指示田地的土壤内的所述至少一个元素的浓度的图。
2.根据权利要求1所述的系统,其中,所述土壤样本累积包括所述田地表面区域的至少5%。
3.根据权利要求1所述的系统,其中,所述土壤样本累积包括所述田地表面区域的至少10%。
4.一种用于分析农田土壤含量的方法,所述方法包括:
将田地的表面区域划分成多个部分;
扫描每个部分内的至少一个土壤样本,以检测土壤样本的伽马光谱;
将检测到的光谱与土壤样本的地理位置相关联;
基于检测到的光谱,计算土壤样本内至少一个元素的量;和
生成指示田地每个部分内所述至少一个元素的量的图。
5.根据权利要求4所述的方法,其中,所述至少一个元素的量包括碳(C)、硅(Si)、钾(K)、氧(O)、氢(H)和氯(Cl)中的至少一个的浓度值。
6.根据权利要求4所述的方法,其中,每个部分具有均匀的景观。
7.根据权利要求4所述的方法,其中,扫描包括使用具有中子生成器的脉冲快速热中子系统进行扫描。
8.根据权利要求4所述的方法,其中,所述土壤样本累积包括所述田地表面区域的至少10%。
9.根据权利要求4所述的方法,进一步包括基于预定义值校正光谱能量。
10.一种用于分析田地土壤的元素含量的系统,所述系统包括:
数据采集单元,被配置为收集至少一个土壤样本的伽马光谱;
导航单元,被配置为提供土壤样本的地理坐标;
数据分析单元,被配置为将收集的伽马光谱与土壤样本的地理坐标相关联,并计算土壤样本内元素的重量百分比;和
元素含量图单元,被配置为基于计算的重量百分比生成指示土壤样本内至少一个元素的浓度的图。
11.根据权利要求10所述的系统,其中,所述数据采集单元包括脉冲快速热中子系统。
12.根据权利要求11所述的系统,其中,所述脉冲快速热中子系统包括中子生成器。
13.根据权利要求12所述的系统,其中,所述脉冲快速热中子系统进一步包括伽马检测器。
14.根据权利要求10所述的系统,其中,所述数据采集单元进一步被配置为收集多个土壤样本的伽马光谱,并且其中,所述多个土壤样本累积包括所述田地表面区域的至少10%。
15.根据权利要求10所述的系统,其中,所述分析单元进一步被配置为基于使用光谱移位和重量百分比计算器确定的预定义值来校正光谱能量。
16.根据权利要求15所述的系统,其中,校正光谱能量包括移位光谱,使得对于多个光谱中的每一个,元素的主峰的质心与光谱的多个能量通道中的同一个相关联。
17.根据权利要求10所述的系统,其中,所述分析单元进一步被配置为基于光谱的寿命来计算重量百分比,其中光谱的寿命是多个检测器中的每一个的寿命的平均值。
18.根据权利要求17所述的系统,其中,所述检测器的寿命基于实际测量时间、输入计数率和输出计数率。
19.根据权利要求17所述的系统,其中,所述分析单元进一步被配置为将计算的重量百分比与两个相邻记录之间的地理中点相关联。
20.根据权利要求10所述的系统,其中,所述浓度指示碳(C)、硅(Si)、钾(K)、氧(O)和氢(H)中的至少一个的含量。
21.根据权利要求20所述的系统,其中,所述土壤样本内的碳(C)浓度是基于在所述田地的一部分内检测到的净光谱的平均值来确定的,并且其中,钾浓度是基于所述土壤样本的自然伽马光谱来确定的,而不使用所述土壤的中子辐照。
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