Picarro, Inc.

Yonggang He

Sze Meng Tan

Chris W Rella

David Steele

Sean Patrick MacMullin

Aaron Van Pelt

Tim Clark

Alex Balkanski

Eric R Crosson

Michael R Woelk

Bruce A Richman

Alejandro Dario Farinas

Chris W Relia

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

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.

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.

Methods for gas leak detection and localization in populated areas using two or more tracer measurements

Described self-referencing cavity enhanced spectroscopy (SRCES) systems and methods are tailored to acquiring spectra in a middle regime, in which signals are lower than optimal for conventional absorption spectroscopy, and absorption is higher than optimal for cavity ring-down spectroscopy (CRDS). Longitudinal mode resonance spectral peaks are analyzed individually to extract intensity ratios (e.g. maximum to minimum) and/or curve-fitting parameters, obviating the need to measure or precisely control the input light intensity.

Self-referencing cavity enhanced spectroscopy (SRCES) systems and methods

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.

Methods for gas leak detection and localization in populated areas using isotope ratio measurements

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.

Cavity ring down spectroscopy using measured backward mode data

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.

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Union City, CA, United States of America

Yonggang He