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    Methods for gas leak detection and localization in populated areas using two or more tracer measurements
    1.
    发明申请
    Methods for gas leak detection and localization in populated areas using two or more tracer measurements 审中-公开
    使用两次或多次示踪剂测量的人口稠密地区气体泄漏检测和定位方法

    公开(公告)号：US20160216172A1

    公开(公告)日：2016-07-28

    申请号：US15088885

    申请日：2016-04-01

    Applicant: Picarro, Inc.

    Inventor： Chris W. Rella ,  Eric R. Crosson ,  Michael R. Woelk ,  Sze Meng Tan ,  Yonggang He ,  David Steele

    IPC: G01M3/04 ,  G01N33/00

    CPC classification number: G01N33/0009 ,  G01M3/22 ,  G01M3/38 ,  G01N21/01 ,  G01N21/3504 ,  G01N21/39 ,  G01N2021/0314 ,  G01N2201/025

    Abstract: Improved gas leak detection from moving platforms is provided. Automatic horizontal spatial scale analysis can be performed in order to distinguish a leak from background levels of the measured gas. Source identification can be provided by using two or more tracer measurements of isotopic ratios and/or chemical tracers to distinguish gas leaks from other sources of the measured gas. Multi-point measurements combined with spatial analysis of the multi-point measurement results can provide leak source distance estimates. Qualitative source identification is provided. These methods can be practiced individually or in any combination.

    Abstract translation: 提供了从移动平台改进的气体泄漏检测。 可以进行自动水平空间尺度分析，以区分泄漏与被测气体的背景水平。 可以通过使用两个或更多个同位素比率和/或化学示踪剂的示踪剂测量来提供源识别，以区分气体泄漏与测量气体的其他来源。 多点测量结合多点测量结果的空间分析可以提供泄漏源距离估计。 提供定性来源识别。 这些方法可以单独或以任何组合实施。

    Leak detection event aggregation and ranking systems and methods
    2.
    发明授权
    Leak detection event aggregation and ranking systems and methods 有权

    公开(公告)号：US11933774B1

    公开(公告)日：2024-03-19

    申请号：US18060111

    申请日：2022-11-30

    Applicant: Picarro Inc.

    Inventor： Sean MacMullin ,  Chris W. Rella ,  Aaron Van Pelt ,  Alex Balkanski ,  Yonggang He ,  Sze M. Tan ,  David Steele ,  Tim Clark

    IPC: G01N33/00 ,  G01M3/04

    CPC classification number: G01N33/0075 ,  G01M3/04 ,  G01N33/0047

    Abstract: In some embodiments, data from multiple vehicle-based natural gas leak detection survey runs are used by computer-implemented machine learning systems to generate a list of natural gas leaks ranked by hazard level. A risk model embodies training data having known hazard levels, and is used to classify newly-discovered leaks. Hazard levels may be expressed by continuous variables, and/or probabilities that a given leak fits within a predefined category of hazard (e.g. Grades 1-3). Each leak is represented by a cluster of leak indications (peaks) originating from a common leak source. Hazard-predictive features may include maximum, minimum, mean, and/or median CH4/amplitude of aggregated leak indications; estimated leak flow rate, determined from an average of leak indications in a cluster; likelihood of leak being natural gas based on other indicator data (e.g. ethane concentration); probability the leak was detected on a given pass; and estimated distance to leak source.

    Leak detection event aggregation and ranking systems and methods
    3.
    发明授权
    Leak detection event aggregation and ranking systems and methods 有权

    公开(公告)号：US11525819B1

    公开(公告)日：2022-12-13

    申请号：US17249795

    申请日：2021-03-12

    Applicant: Picarro Inc.

    Inventor： Sean MacMullin ,  Chris W. Rella ,  Aaron Van Pelt ,  Alex Balkanski ,  Yonggang He ,  Sze M. Tan ,  David Steele ,  Tim Clark

    IPC: G01N33/00 ,  G01M3/04

    Abstract: In some embodiments, data from multiple vehicle-based natural gas leak detection survey runs are used by computer-implemented machine learning systems to generate a list of natural gas leaks ranked by hazard level. A risk model embodies training data having known hazard levels, and is used to classify newly-discovered leaks. Hazard levels may be expressed by continuous variables, and/or probabilities that a given leak fits within a predefined category of hazard (e.g. Grades 1-3). Each leak is represented by a cluster of leak indications (peaks) originating from a common leak sources. Hazard-predictive features may include maximum, minimum, mean, and/or median CH4/amplitude of aggregated leak indications; estimated leak flow rate, determined from an average of leak indications in a cluster; likelihood of leak being natural gas based on other indicator data (e.g. ethane concentration); probability the leak was detected on a given pass; and estimated distance to leak source.

    Cavity ring down spectroscopy using measured backward mode data
    4.
    发明授权
    Cavity ring down spectroscopy using measured backward mode data 有权
    使用测量的反向模式数据进行腔环衰减光谱

    公开(公告)号：US09116042B1

    公开(公告)日：2015-08-25

    申请号：US14015791

    申请日：2013-08-30

    Applicant: Picarro, Inc.

    Inventor： Yonggang He ,  Sze Meng Tan ,  Alejandro Dario Farinas

    IPC: G01J3/42

    CPC classification number: G01J3/42 ,  G01J3/0224 ,  G01J3/28 ,  G01N21/39

    Abstract: In cavity ring-down spectroscopy (CRDS), scattering into the backward mode of a traveling wave ring-down cavity can degrade conventional CRDS performance. We have found that this performance degradation can be alleviated by measuring the backward mode signal emitted from the ring-down cavity, and using this signal to improve the processing for extracting ring-down times from the measured data. For example, fitting an exponential to the sum of the intensities of the forward and backward signals often provides substantially better results for the ring-down time than fitting an exponential to the forward signal alone. Other possibilities include extracting cavity eigenmode signals from the forward and backward signals and performing separate exponential fits to the eigenmode signals. An optical circulator can be used to facilitate measurement of the backward mode signal.

    Abstract translation: 在空腔衰减光谱（CRDS）中，散射到行波衰减腔的向后模式可以降低传统的CRDS性能。 我们已经发现，通过测量从衰减腔发射的反向模式信号，并且使用该信号来改善从测量数据中提取衰落时间的处理，可以减轻性能下降。 例如，对于正向和反向信号的强度之和来拟合指数通常对于衰减时间提供比仅适合于正向信号的指数的更好的结果。 其他可能性包括从正向和反向信号提取腔本征模信号，并对本征模信号执行独立的指数拟合。 可以使用光循环器来促进反向模式信号的测量。

    Methods for gas leak detection and localization in populated areas using isotope ratio measurements
    5.
    发明申请
    Methods for gas leak detection and localization in populated areas using isotope ratio measurements 有权
    使用同位素比率测量在人口稠密地区进行气体泄漏检测和定位的方法

    公开(公告)号：US20150007638A1

    公开(公告)日：2015-01-08

    申请号：US14493853

    申请日：2014-09-23

    Applicant: Picarro, Inc.

    Inventor： Chris W. Rella ,  Eric R. Crosson ,  Michael R. Woelk ,  Sze Meng Tan ,  Yonggang He ,  David Steele

    IPC: G01M3/04 ,  G01N33/00

    CPC classification number: G01N33/0009 ,  G01M3/20 ,  G01M3/38 ,  G01N21/01 ,  G01N21/3504 ,  G01N21/39 ,  G01N2021/0314 ,  G01N2201/025

    Abstract: Improved gas leak detection from moving platforms is provided. Automatic horizontal spatial scale analysis can be performed in order to distinguish a leak from background levels of the measured gas. Source identification can be provided by using isotopic ratios and/or chemical tracers to distinguish gas leaks from other sources of the measured gas. Multi-point measurements combined with spatial analysis of the multi-point measurement results can provide leak source distance estimates. Qualitative source identification is provided. These methods can be practiced individually or in any combination.

    Abstract translation: 提供了从移动平台改进的气体泄漏检测。 可以进行自动水平空间尺度分析，以区分泄漏与被测气体的背景水平。 可以通过使用同位素比率和/或化学示踪剂来提供源识别，以区分气体泄漏与测量气体的其他来源。 多点测量结合多点测量结果的空间分析可以提供泄漏源距离估计。 提供定性来源识别。 这些方法可以单独或以任何组合实施。

    Leak detection event aggregation and ranking systems and methods
    6.
    发明授权
    Leak detection event aggregation and ranking systems and methods 有权

    公开(公告)号：US10948471B1

    公开(公告)日：2021-03-16

    申请号：US15996069

    申请日：2018-06-01

    Applicant: Picarro Inc.

    Inventor： Sean MacMullin ,  Chris W. Relia ,  Aaron Van Pelt ,  Alex Balkanski ,  Yonggang He ,  Sze Tan ,  David Steele ,  Tim Clark

    IPC: G01N33/00 ,  G01M3/04

    Abstract: In some embodiments, data from multiple vehicle-based natural gas leak detection survey runs are used by computer-implemented machine learning systems to generate a list of natural gas leaks ranked by hazard level. A risk model embodies training data having known hazard levels, and is used to classify newly-discovered leaks. Hazard levels may be expressed by continuous variables, and/or probabilities that a given leak fits within a predefined category of hazard (e.g. Grades 1-3). Each leak is represented by a cluster of leak indications (peaks) originating from a common leak sources. Hazard-predictive features may include maximum, minimum, mean, and/or median CH4/amplitude of aggregated leak indications; estimated leak flow rate, determined from an average of leak indications in a cluster; likelihood of leak being natural gas based on other indicator data (e.g. ethane concentration); probability the leak was detected on a given pass; and estimated distance to leak source.

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