Patent Description:
Methane (CH<NUM>) is an odorless and colorless naturally occurring organic molecule, which is present in the atmosphere at average ambient levels of approximately <NUM> ppm as of <NUM> and is projected to continually climb. While methane is found globally in the atmosphere, a significant amount is collected or "produced" through anthropogenic processes including exploration, extraction, and distribution of petroleum resources in the form of natural gas. Natural gas, an odorless and colorless gas, is a primary source of energy used to produce electricity and heat. The main component of natural gas is methane (<NUM> mol % CH<NUM> typ. While extraction of natural gas is a large source of methane released to atmosphere, major contributors of methane also include livestock farming (enteric fermentation), and solid waste and wastewater treatment (anaerobic digestion). <CIT> discloses an apparatus and method for detecting the location of one or more sources of one or more target molecule, the apparatus comprising: a molecule detector, wherein a processor calculates the presence of the one or more target molecules, runs a computer code that determines a dynamic reverse gas stack model for the one or more target molecules, and triangulates the possible position for a source or effluent of the one or more target molecules based on the dynamic reverse gas stack model. <CIT> discloses methods for analyzing air including monitoring the air. For example, monitoring the air includes electronically sensing the air and determining whether an event in the air is occurring. If an event is occurring, a sample of the air is collected for a further analysis.

According to a first aspect of the invention, a method is provided as recited in claim <NUM>.

Additional method embodiments may include: generating, by a weather station, the meteorological data. In additional method embodiments, the meteorological data comprises an instantaneous wind vector, an average wind vector, a wind vector component magnitude variance, and a wind vector component direction variance.

In additional method embodiments, the point source gas concentration measurement may be a methane gas measurement. In additional method embodiments, the generated back trajectory may be generated using a stochastic particle trajectory model. In additional method embodiments, storing the position of each generated back trajectory in a grid may further comprise: summing, by the GCS, the stored position within each cell of the grid. Additional method embodiments may include: normalizing, by the GCS, the stored position of each generated back trajectory in the grid. Additional method embodiments may include: determining, by the GCS, a perimeter of the gas source location based on the normalized stored position of each generated back trajectory.

Additional method embodiments may include: displaying, by the GCS, the generated overlay on a two-dimensional (2D) map. Additional method embodiments may include: displaying, by the GCS, the generated overlay on a three-dimensional (3D) map. In additional method embodiments, the grid may be a two-dimensional (2D) grid. In additional method embodiments, the grid may be a three-dimensional (3D) grid.

According to another aspect of the invention a system is provided as recited in claim <NUM>.

Additional system embodiments may include: a weather station, where the weather station may be configured to generate the meteorological data. In additional system embodiments, the meteorological data comprises an instantaneous wind vector, an average wind vector, a wind vector component magnitude variance, and a wind vector component direction variance.

In additional system embodiments, the point source gas concentration measurement may be a methane gas measurement. In additional system embodiments, the generated back trajectory may be generated using a stochastic particle trajectory model. In additional system embodiments, the processor may be further configured to: sum the stored position within each cell of the grid. In additional system embodiments, the processor may be further configured to: normalize the stored position of each generated back trajectory in the grid. In additional system embodiments, the processor may be further configured to: determine a perimeter of the gas source location based on the normalized stored position of each generated back trajectory.

Additional system embodiments may include: a display in communication with the GCS, where the display may be configured to show the generated overlay on at least one of: a two-dimensional (2D) map and a three-dimensional (3D) map. In additional system embodiments, the grid may be a two-dimensional (2D) grid. In additional system embodiments, the grid may be a three-dimensional (3D) grid.

There is also disclosed a system including: an unmanned aerial vehicle (UAV) configured to follow a flight path about one or more potential methane sources, where the UAV may be configured to collect a UAV data; a payload affixed to the UAV, where the payload comprises one or more gas concentration sensors, and where the payload may be configured to measure an ambient methane gas concentration corresponding to the UAV data along the flight path; a weather station configured to measure a meteorological data; a ground control station (GCS) having a processor with addressable memory, where the GCS may be in communication with the UAV, the payload, and the weather station, and where the processor of the GCS may be configured to: receive the measured ambient methane gas concentration corresponding to the UAV data; receive the Meteorological data; determine whether the received ambient methane gas concentration may be an elevated ambient methane gas concentration; determine a probability of a location of the one or more potential methane sources based on elevated ambient methane gas concentration, UAV data, and Meteorological data; and generate a relative probability map of the one or more potential methane sources based on the determined probability of the location of the one or more potential methane sources.

There is also disclosed a method including: determining, by a ground control station (GCS) having a processor with addressable memory, a flight path of an unmanned aerial vehicle (UAV) about one or more methane sources; measuring, by a payload of the UAV, a methane concentration along the flight path; determining, by an autopilot of the UAV, a UAV information Data Packet comprising at least one of: GPS location, time, barometric pressure, altitude, relative altitude, and/or UAV orientation; generating, by the UAV, a UAV Data Packet comprising the measured methane concentration and the determined UAV information; generating, by a weather station, a Meteorological Data Packet comprising data from at least one of: an anemometer, one or more pressure sensors, a pyranometers, a ground surface temperature sensor, an air temperature sensor a humidity sensor, and soil heat flux plates; receiving, by the GCS, the UAV Data Packet and the Meteorological Data Packet; combining, by the GCS, the UAV Data Packet with a nearest temporal or interpolated or extrapolated Meteorological Data Packet; generating, by the GCS, a spatial map of methane concentration based on the combined data; and generating, by the GCS, the spatial map of the location of one or more emissions sources based on the combined data.

The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principals of the invention. Like reference numerals designate corresponding parts throughout the different views. Embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which:.

The following description is made for the purpose of illustrating the general principles of the embodiments discloses herein and is not meant to limit the concepts disclosed herein. Further, particular features described herein can be used in combination with other described features in each of the various possible combinations and permutations. Unless otherwise specifically defined herein, all terms are to be given their broadest possible interpretation including meanings implied from the description as well as meanings understood by those skilled in the art and/or as defined in dictionaries, treatises, etc..

The present system and method disclosed herein allows for determining a methane concentration and location of one or more emissions sources based on measurements from one or more sensors of an unmanned aerial vehicle (UAV), UAV data, and weather and/or atmospheric data from one or more sensors of a weather station. The UAV flies in a flight path about one or more methane sources. Data from the one or more UAV sensors, UAV data, and data from one or more sensors of the weather station are combined, stored, and filtered to generate a spatial map of methane concentration and the one or more emissions sources.

The goal of the natural gas production and supply chain is to deliver gas from source production areas to endpoint users without undue loss. Product loss in this context amounts to flaring or venting, intentional or otherwise, of natural gas to the atmosphere. Undue product loss results in uncaptured revenue, an increased environmental footprint, and possible safety hazards for vented emissions. There are many opportunities throughout the natural gas production and supply chain for gas to be released from containment and lost, e.g. pneumatic component venting, maintenance blowdowns, component failures, accidental release, etc. Natural gas production and distribution infrastructure is spatially distributed. Efficient, wide area survey methods are needed to identify, localize, and quantify natural gas releases throughout the system.

The disclosed UAS measures methane concentration along the chosen UAS flight path at high frequency to detect anomalies associated with natural gas releases. These data are reconciled with atmospheric conditions to identify and quantify the mass flow rate of natural gas sources within an inspection area.

<FIG> depicts a data flow <NUM> in a single sensor and unmanned aerial vehicle (UAV) <NUM> configuration with a Ground Control Station (GCS) <NUM> as a point of interface between the UAV <NUM> and the Cloud Connected Processor, Local Server Processor, and/or Database <NUM>, according to one embodiment. The UAV <NUM> may be a small unmanned aerial vehicle (UAV), with the ability to fly in a three-dimensional flight path in the vicinity of (≤<NUM> from a ≥<NUM> SCFH emissions point) potential methane source, and report GPS spatial position. The general flow of data is from one or more gas concentration sensors, i.e., payload <NUM>, affixed to one or more UAVs <NUM> and wirelessly transmitted to a centralized GCS <NUM> and transferred to a Cloud Connected Server Processer, Local Server Processor, and/or Database <NUM>. The payload <NUM> may be an ultra-lightweight, low power, Part per Billion (ppb) sensitivity, mid-Infrared (λ= <NUM>-<NUM>), open path methane concentration sensor with sampling rate > <NUM>. A wireless radio or cellular connection may be used for remote data transfer between the UAV <NUM> and the base station <NUM> or a cloud server/processor <NUM>. A wireless interface or cellular connection may be used between the base station <NUM> and/or UAV <NUM> and a cloud server/processor <NUM> for performing advanced data analysis functions. Direct, bidirectional data transfer may occur between the UAV and the base station, between the UAV and the cloud processor, and/or between the base station and the cloud processor.

The UAV <NUM> flight path may be determined on a site-specific basis, using pilot experience and/or self-determined, remote commands. The purpose of the flight path is to measure atmospheric methane background concentration in the vicinity of a possible gas leak, as well as emissions signature (elevated ambient concentration) from all potential sources at the inspection site. High resolution (<<NUM>/s), high frequency measurements (> <NUM>) of wind speed and direction may be made using one or more wind sensors, and one or more additional weather/micro-meteorological sensors including, air temperature, humidity, atmospheric pressure, solar irradiance, ground surface temperature - from the ground, e.g., via a weather station <NUM>, and/or from the UAV <NUM>. The UAV flight path may be determined on a site-specific basis, using a human at the controls and/or self-determined, autonomous control. The purpose of the flight path is to measure gas concentration along a crosswind transect, and vertical profile in the vicinity of a possible gas emissions point. This flight plane is designed to capture the atmospheric methane background as well as emissions signature, i.e., elevated ambient concentration, from all potential sources at the inspection site. A stochastic, back-trajectory model to calculate the receptor sensitivity of the UAS concentration sensor payload and the source location probability. Source emissions data may be displayed on a map, satellite image, aerial image, two-dimensional color map, two-dimensional contour map, and/or three-dimensional topographical surface / mesh.

<FIG> depicts a data flow <NUM> in a single sensor and UAV configuration with the UAV <NUM> directly interfacing with the cloud connected processor, local server processor, and/or database <NUM>, according to one embodiment. In another embodiment, the payload(s) <NUM>, UAV(s) <NUM>, and/or weather station(s) <NUM> communicate directly with a cloud server, processor, local server, processor, and/or database <NUM>. In all cases, each subsystem, e.g., UAV <NUM>; payload <NUM>; GCS, as shown in <FIG>; cloud server processor, local server processor, and/or database <NUM>; and weather station <NUM>, may or may not have the ability to directly communicate with each other subsystem, which is represented in <FIG>. At the GCS and/or cloud server processor, local server processor, and/or database <NUM>, the data from the payload <NUM> is coupled with local weather station <NUM> data through local private networks and/or publicly available over the internet. The data can then be post-processed on the GCS, as shown in <FIG>, and/or on a local server and/or on a cloud-hosted server <NUM>.

<FIG> depicts a detailed data transfer <NUM> from a single sensor with a single UAV <NUM>, where this combination of devices comprise a UAS, according to one embodiment. Data from the payload <NUM> transfers to the UAV <NUM> and directly to the autopilot <NUM> via a serial connection. In some embodiments, the data transfer may be via any connection hardwire or wireless. Then, the data is fused with GPS location and time, barometric pressure altitude, relative altitude from LIDAR, Sonar, Radar, and/or UAV orientation, which forms a UAV Data Packet. The UAV Data Packet is transferred to the GCS and/or cloud processor <NUM> via a 500mW <NUM> Frequency Hopping Spread Spectrum (FHSS) transceiver. In some embodiments, the UAV Data Packet may be transferred via any wireless radio. In parallel, a Weather Station <NUM> having at least an anemometer and which may contain pressure sensors, pyranometers, i.e., solar irradiance, ground temperature sensors, air temperature sensors, and/or any sensors necessary for quantifying current atmospheric conditions forms a Meteorological Data Packet. The GCS and/or cloud processor <NUM> receives both the Meteorological Data Packet and UAV Data Packet at a frequency greater than <NUM>. Each UAV Data Packet is fused with the nearest temporal or interpolated or extrapolated Meteorological Data Packet and saved on the GCS and/or a cloud processor <NUM> in an ASCII, binary, or any file necessary.

The data may be uploaded to a cloud server in real-time, near real-time, or at a later time. The data may include: time (GPS or other), latitude, longitude, altitude (barometric or GPS), relative altitude (from LIDAR, Sonar, and/or RADAR), gas concentration, wind vector (x, y, z or x, y), ambient temperature, and/or ambient pressure.

The payload <NUM> may include a concentration measurement instrument, and/or a gas concentration analyzer. In some embodiments, the payload <NUM> may also include a pressure sensor, a temperature sensor, and/or an anemometer.

The weather station <NUM> may include at least an anemometer. In some embodiments, the weather station <NUM> may also include a pressure sensor, a pyranometer for solar irradiance, a ground temperature sensor, and an air temperature sensor.

<FIG> depicts a plan view illustration <NUM> of a path for the disclosed UAS for natural gas release detection and localization, according to one embodiment. One or more natural gas point sources <NUM>, <NUM>, <NUM> are located within a site boundary, and result in the downwind propagation of natural gas plumes <NUM> in an average downwind direction <NUM>, which is indicated by an arrow. The UAV <NUM> traverses a three-dimensional transect <NUM> and generates a spatial map of methane concentration, i.e., detection, and emissions sources, i.e., localization. This data is analyzed to determine source locations and quantify emission rates using non-parametric regression techniques.

The UAV payload may be an ultra-lightweight, low power, Part per Billion (ppb) sensitivity, mid-Infrared, open path methane concentration sensor with a sampling rate greater than <NUM>. The UAV flight path is designed to measure the ambient methane concentration in the vicinity of possible source locations within the inspection area. The inspection area may include various natural gas infrastructure components, e.g., wells, valves, tanks, pipelines, compressors, condensers, flares, vents, and the like. The inspection area may also include other areas of possible methane emissions, such as compost facilities, manure collection facilities, livestock containment, landfills, sewer pipelines and vents, abandoned wells, and the like. The UAV flight path is designed based on pilot experience and/or automated input from a search algorithm commanded via autopilot software, as shown in <FIG>. The goal of the UAV flight path is to position the UAV in as many possible locations on the well pad as possible, both upwind and downwind of all potential and/or observed emission sources. Flight paths may maintain any specified intrinsically safe distance from infrastructure components.

The UAV records and transmits synchronized, CH<NUM> concentration data in volumetric concentration units, i.e.,Parts Per Billion Volume (ppbv), and/or pressure and/or temperature and GPS coordinates (latitude, longitude and altitude) via wireless radio to a base station and/or cloud server, as shown in <FIG> and <FIG>. The data is recorded in ASCII, binary, and/or database format on the base station, and synthesized with wind speed and direction data, as well as other Meteorological and/or weather data including air temperature and atmospheric pressure. The combined data is transmitted via wireless radio to a cloud processor for additional advanced analytics and reporting.

<FIG> depicts a background gas concentration workflow <NUM>, according to one embodiment. The first step in the localization model is to calculate local background concentration. Typically, it is assumed that the background concentration measured on the upwind side of the source inspection area is a good representation of the local background concentration and provides an estimate of the upwind in-flow condition. Data may be selected for the appropriate time period (step <NUM>). The GPS coordinate, i.e., longitude and latitude, frame is converted to along a path distance (step <NUM>). A statistical filter is applied to the concentration data (step <NUM>).

<FIG> depicts a graph <NUM> of raw concentration data <NUM>, filtered/interpolated data <NUM>, and a background concentration estimation <NUM>, according to one embodiment. <FIG> depicts a graph <NUM> of concentration enhancement data <NUM> resolved utilizing the sliding window median filter and spike detection algorithm <NUM> applied with a width filter <NUM>, according to one embodiment.

The raw concentration data as a function of distance, e.g., spatial coordinate, is filtered using a sliding window median filter. The filter window scale is determined based on the typical, or expected, gas plume width. For example, if the maximum plume width is expected to be on the order of <NUM>, the filter scale would be set to three to five times the max plume width. The median filter also removes infrequent transients, or dropouts, in the concentration measurement caused by communication interference, or platform vibrations. The background concentration <NUM> is subtracted from the total concentration <NUM> to obtain the concentration enhancement <NUM>. The concentration enhancement signal contains the signature of an upwind emission source, and quantifies the emissions released by the local source.

A statistical filter is then applied to the concentration enhancement signal <NUM> to identify "spikes" <NUM> in the data that indicate methane plumes from nearby sources. The statistical filter determines the Cumulative Distribution Function (CDF) for the concentration enhancement, and targets extremum data points based on a prescribed percentile threshold. The selected points are then analyzed for contiguity and consolidated to form spatially continuous events. Each spike event may be further analyzed according to other metrics such as spatial extent, amplitude, magnitude, variance, and waveform shape. Individual spike events may be included or excluded through a selection process based on these derived quantities.

<FIG> depicts a graph <NUM> of typical wind values collected, according to one embodiment. Wind vector, i.e., a three-component magnitude and direction, is measured continuously, and concurrently over the duration of the UAV flight. Wind measurements may be performed using one or more stationary wind sensors connected to a ground station. Or the wind measurement may be made on-board the UAS during the flight. Additional weather sensors may be included with the ground station to quantify air temperature and pressure.

Spike events that were identified based on a statistical analysis of CH<NUM> concentration data are correlated with wind vector measurements and processed to obtain statistics of wind speed and direction during the detection of each plume.

<FIG> depicts a workflow <NUM> for spike identification and statistical analysis of atmospheric conditions, according to one embodiment. The wind statistics are then applied to determine the approximate location of a detected methane source using an inverse stochastic dispersion model. Meteorological and/or Weather data including air temperature, humidity, atmospheric pressure, solar irradiance, ground surface temperature may be applied to develop a model for local turbulence characteristics and quantify the spatial decorrelation of the wind. This approach is used to quantify the relationship between in situ wind measurements that are made some distance away from the probable source locations, and the actual winds and turbulence occurring at or near the source.

Candidate spike events are identified (step <NUM>). The time period of the spike event is correlated with weather data (step <NUM>). Qualitative statistics of weather data for each event are computed (step <NUM>). The computed qualitative statics (step <NUM>) determine an instantaneous wind vector <NUM>, an average wind vector <NUM>, a wind vector component magnitude variance <NUM>, and/or a wind vector component direction variance <NUM>.

<FIG> depicts a back trajectory workflow <NUM> taking account atmospheric statistics, Monte Carlo Markov Chain (MCMC) particle back trajectory simulation and localization in 3D space into account, according to one embodiment. An inverse stochastic dispersion model is applied to determine the probable location of a methane source or sources based on wind statistics measured during each plume event. An inverse dispersion model applies statistics of wind speed, direction, and turbulence to simulate upwind trajectories of massless particles, or air parcels, arriving at a specified downwind sensor location. After stochastically simulating many particle trajectories, the upwind distribution of particle positions provides an estimate of the sensor footprint. The footprint represents the spatial probability that a source of a given magnitude in any location within the model domain would have been detected by the sensor. When applied to individual plume events the inverse model predicts the most probable locations for the source(s) associated with the observed concentration enhancement. When data for many events are combined localization of sources to spatial regions on the order of <NUM> - <NUM><NUM> is achieved through convergence of the ensemble particle trajectories.

Wind direction, variance, and turbulent kinetic energy for each event are calculated (step <NUM>). This calculation (step <NUM>) is iterated over M detected methane plumes. Then, the particle back trajectories are simulated using Markov Chain Monte Carlo (step <NUM>). This simulation (step <NUM>) is iterated over N trajectories. Then, the position of each particle in 3D space is tracked and stored (step <NUM>). The workflow <NUM> includes Eqs. <NUM>-<NUM>, as discussed below. 3D space may include x, y positions used on a 3D map in some embodiments. In other embodiments, a 3D probability map, e.g., x, y, z, may be created for source location probability.

<FIG> depicts a graph <NUM> showing a UAV trajectory according to measured gas concentration enhancements and projected to the (x,y) plane, according to one embodiment. Particle trajectories <NUM> are shown as back-trajectories simulated using MCMC and the Langevin Equation. Each particle trajectory <NUM> is created by a stochastic particle trajectory model or stochastic particle back trajectory model, such as shown in Eq. <NUM>. Upwind trajectories are modeled according to a Langevin Equation stochastic differential equation using a MCMC method. In this model, the upwind position of the particle at each timestamp depends on the current position of the particle, the average wind speed and direction, and a random component which is parameterized in terms of the turbulent kinetic energy. Equation <NUM> shows a form of the Langevin Equation used to compute the particle back trajectory. In Eq. <NUM> xi is the vector representing the position of the back trajectory at time t, v is the advective velocity vector, η is a stochastic random variable, κ is the turbulent kinetic energy, and A is scaling parameter which depends on the position of the particle at any given time and other aspects of the turbulent velocity field. v and η are vector quantities of <NUM>-dimensional space.

The path of each parcel back trajectory is determined by solving Equation <NUM> iteratively using an Euler method and substituting measured values of v, η, κ and A during each plume event. Several hundred particle back-trajectories are derived from independent realizations of Eq. <NUM> for each plume event and tracked backward in space over a specified time interval. Because each individual particle trajectory is independent of the others, the solution to Eq. <NUM> is readily distributed in a shared CPU architecture across many processors. When very large simulations are completed near <NUM>:<NUM> speed up can be achieved by distributing the calculation of individual particle trajectories in across many processors. An additional computational advantage of Eq. <NUM> is that it does not rely on a spatially regular grid, and solutions to particle trajectories are solved on an unstructured grid. This substantially reduces memory usage of the algorithm. Stochastic particle trajectory models in place of Eq. <NUM> are possible and contemplated.

<FIG> depicts a workflow <NUM> for a localization clustering, according to one embodiment. After particle trajectories are determined the spatial footprint of the sensor, weighted over all the methane plumes that were detected, is calculated on a regular grid. A spatial grid is defined across a range/extent of simulated particles (step <NUM>). Each kernel function is defined for each particle position in grid variables (step <NUM>). The normalized sum of all kernel functions is computed to yield a normalized footprint (step <NUM>). Thresholds are applied to the footprint function for probable source location (step <NUM>). The workflow <NUM> includes Eqs. <NUM>-<NUM>, as discussed below.

The grid is defined in terms of a fixed coordinate system, which may be Cartesian, spherical, or following a geodesic approximation. The position of each particle at time t is represented on the grid as a kernel. An example of a typical Gaussian kernel p(x,y,z) is shown in Eq. <NUM>, where µ and σ are parameters in the model and x<NUM>, y<NUM>, z<NUM> a define the location of the maximum value of the Gaussian. Eq. <NUM>-<NUM> show a Monte Carlo simulation using Gaussian kernel. Other simulations are possible and contemplated. <MAT> <MAT>.

The kernel function is calculated for each independent trajectory and at each timestep (Eq. <NUM>), then summed to generate the cumulative footprint function (Eq. <NUM>). <NUM>-<NUM> relate to generating the probability map. The cumulative footprint function describes the probability that the source is in a given location within the simulation domain based on all the methane plume events identified by the disclosed UAS system.

After the footprint is calculated, the source location area is determined by applying a threshold τ to the source location probability (Eq. <NUM>). The threshold may be set based on a determined value. In some embodiments, the threshold may be tuned manually. For example, the entire grid shown in <FIG> may have a non-zero probability. Applying the threshold may constrain the probability down to the overlay <NUM> shown in <FIG>. The source location probability function may be further modified using a power parameter β to enhance the probability gradient in the predicted source area. The power parameter β may be used to scale the gradient to increase the rate of change of the gradient. The power parameter β may be used to create a larger difference between the minimum probability and the maximum probability. Smaller variations may be enhanced and larger changes may be lessened. The power parameter β may be tunable in some embodiments, such as based on wind conditions, environmental factors, or the like. The perimeter of the source location area can also be calculated to provide a spatially uniform source location prediction (Eq. <NUM>). <MAT> <MAT>.

The result is a three-dimensional probability map, shown in <FIG> depicts a relative probability map <NUM> of emissions source location (x,y,P), according to one embodiment. Each particle trajectory is created by a stochastic particle trajectory model and mapped to a cell on the grid. The density of each cell in the grid, e.g., counting the number of particles in each grid, is used to create the relative probability map <NUM>.

<FIG> depicts an aerial map <NUM> with a relative probability overlay <NUM>, according to one embodiment. <FIG> depicts a three-dimensional illustration <NUM> of the disclosed UAS system and process for methane source detection and localization, according to one embodiment. The UAV <NUM> flight trajectory <NUM> is projected onto overlay <NUM> and illustration <NUM>. The UAV <NUM> measures point source gas concentration measurements as it flies the flight path <NUM>. Each measured gas concentration along the flight path <NUM> has a stochastic particle trajectory model applied to determine a potential source for elevated gas concentrations. The potential sources are combined in a grid to create the overlay <NUM> showing the probability of the gas source location. The overlay <NUM> may have an area of highest probability surrounded by areas of lower probability. The flight path <NUM> may be any flight path that is downwind of the potential gas source.

The source footprint, localization probability, source location boundary areas are geo-referenced and displayed visually on a map for data reporting purposes. The base map may include a variety of styles including basic street maps, satellite images, and high resolution aerial images, as shown in <FIG>. <FIG> depicts a three-dimensional view of the overlay <NUM>. In addition to displaying the source location areas, the UAS vehicle path is also shown to indicate the flight path with in the inspection area and identify areas where no sources were detected. The map may also include other features including information about the mass flow rate of the source, wind direction indicators, a distance scale, and a compass rose.

<FIG> depicts a high-level block diagram of a UAS system <NUM> and process for methane source detection and localization, according to one embodiment. The system <NUM> may include a UAV <NUM>. In some embodiments, the UAV <NUM> may be a quadcopter-style aerial vehicle capable of hovering and flying a flight path. In other embodiments, the UAV <NUM> may be a winged aerial vehicle. The UAV <NUM> may have any number of rotors, motors <NUM>, wings, or the like to sustain flight and fly the determined UAV flight path. The UAV <NUM> may have the ability to fly in a three-dimensional flight path in the vicinity of a potential methane or other gas source.

Embodiments of the unmanned aerial vehicle <NUM> may include any number of sensors shown in <FIG> based on the desired data. Embodiments of the weather station <NUM> may include any number of sensors shown in <FIG> based on the desired data. In some embodiments, the weather station <NUM> may only include an anemometer <NUM>. In other embodiments, the weather station <NUM> may be integrated into the unmanned aerial vehicle <NUM>. For example, the anemometer <NUM> may be integrated on the unmanned aerial vehicle <NUM>. In another embodiment, the weather station <NUM> may be located on another aerial vehicle or unmanned aerial vehicle. For example, the system may include two or more unmanned aerial vehicles where at least one unmanned aerial vehicle is recording methane gas concentrations and at least one unmanned aerial vehicle is recording meteorological data. The weather station <NUM> may be stationary or mobile. The weather station <NUM> may be in relatively close proximity to the unmanned aerial vehicle <NUM>. In some embodiments, the weather station <NUM> may record meteorological data. In some embodiments, the weather station <NUM> may be from a third-party source, such as a third-party sensor. In some embodiments, the weather station <NUM> may predict future meteorological measurements. The nearest temporal Meteorological (MET) data packet <NUM> may be combined with the UAV data packet <NUM>. The frequency of each of the MET data packet <NUM> and the UAV data packet <NUM> may be different but close in some embodiments. The frequency of each of the MET data packet <NUM> and the UAV data packet <NUM> may be substantially the same in some embodiments.

The UAV <NUM> may have a global positioning system <NUM>, an onboard avionics <NUM>, and/or a location sensor <NUM> to track a spatial position of the UAV <NUM> as it travels along the flight path. The UAV <NUM> tracks its spatial position as it measures gas concentrations along the flight path such that each gas measurement of the UAV <NUM> corresponds to a spatial position where that gas measurement was taken. The global positioning system <NUM>, onboard avionics <NUM>, and/or location sensor <NUM> may be in communication with a UAV processor <NUM> having addressable memory <NUM>. In some embodiments, the location of the UAV <NUM> may be determined by the onboard avionics <NUM>. The onboard avionics <NUM> may include a triangulation system, a beacon, a spatial coordinate system, or the like. The onboard avionics <NUM> may be used with the GPS <NUM> and/or location sensor <NUM> in some embodiments. In other embodiments, the UAV <NUM> may use only one of the GPS <NUM>, the onboard avionics <NUM>, and/or the location sensor <NUM>.

The UAV <NUM> may include a payload <NUM> in communication with the UAV processor <NUM>. The payload <NUM> may include one or more gas concentration sensors. The payload <NUM> may be detachably attached to the UAV <NUM>. In other embodiments, the payload <NUM> may be fixedly attached to the UAV <NUM>. The payload <NUM> may be in communication with the UAV processor <NUM>. In one embodiment, the payload <NUM> may be an ultra-lightweight, low power, Part per Billion (ppb) sensitivity, mid-Infrared (λ = <NUM> - 8µm), open path methane concentration sensor with sampling rate > <NUM>. The payload <NUM> may record point source gas concentration measurements.

The UAV processor <NUM> may also be in communication with an orientation sensor <NUM>, an inertial measurement unit (IMU) <NUM>, an altitude sensor <NUM>, a radar <NUM>, a LIDAR <NUM>, and/or a Sonar <NUM> for generating additional information on the spatial position of the UAV <NUM> during each gas measurement by the payload <NUM>. The orientation sensor <NUM> may be used to determine an orientation of the UAV <NUM> relative to ground. The IMU <NUM> may be used to determine attitude, velocity and/or position of the UAV <NUM>. The altitude sensor <NUM> may be used to determine an altitude of the UAV <NUM>. The LIDAR <NUM>, Sonar <NUM>, and/or radar <NUM> may be used to determine a relative altitude of the UAV <NUM>.

In some embodiments, the UAV processor <NUM> may also be in communication with an anemometer <NUM>, one or more weather sensors <NUM>, and/or a barometric pressure sensor <NUM>. The anemometer <NUM> may be used to measure the speed of the wind. The anemometer <NUM> may be attached to the UAV <NUM> at a point so as to ensure an accurate wind measurement without interfering with the propulsion from the motors <NUM> or sensors of the payload <NUM>. The weather sensor <NUM> may measure weather and/or atmospheric conditions. The barometric pressure sensor <NUM> may measure a barometric pressure. The anemometer <NUM>, weather sensor <NUM>, and/or barometric pressure sensor <NUM> may be used to record data at each gas measurement from the payload <NUM>.

In some embodiments, the UAV processor <NUM> may also be in communication with a time measurement device <NUM>. The time measurement device <NUM> may be used to record the time for each gas measurement measured by the payload <NUM> of the UAV <NUM>. Each gas measurement, position measurement, orientation measurement, weather measurement, and/or relative altitude measurement may be 'time-stamped' so as to be combined by the processor <NUM> and/or the UAV processor <NUM>.

The UAV processor <NUM> may also be in communication with a transceiver <NUM> and/or a wireless radio <NUM>. The transceiver <NUM> and/or wireless radio <NUM> may be used to communicate between the UAV <NUM> and the processor <NUM>, the ground control station (GCS) <NUM>, and/or a cloud server <NUM>.

The processor <NUM>, the cloud server <NUM>, the ground control station (GCS) <NUM>, and/or the UAV processor <NUM> may determine a flight path for the UAV <NUM> having the payload <NUM>. In some embodiments, the flight path may be determined on a site-specific basis. In other embodiments, the flight path may be determined and/or flown via a user of the GCS <NUM>. In other embodiments, the flight path may be self-determined, autonomous control. The flight path is used to measure gas concentration along a crosswind transect, and vertical profile, in the vicinity of a possible gas emissions point. This flight plane of the flight path is designed to capture the atmospheric methane background as well as emissions signature, i.e., elevated ambient concentration, from all potential sources at an inspection site.

The UAV <NUM> may have the UAV processor <NUM> in communication with addressable memory <NUM>, a GPS <NUM>, one or more motors <NUM>, and a power supply <NUM>. The UAV <NUM> may communicate gathered payload <NUM> data to the UAV processor <NUM>. The power supply <NUM> may be a battery in some embodiments. In some embodiments, the processor <NUM> may be a part of the UAV <NUM>, the cloud server <NUM>, the GCS <NUM> used to control the UAV <NUM>, or the like.

The UAV processor <NUM> may receive gas data from the one or more gas sensors of the payload <NUM>. The UAV processor <NUM> may also receive spatial position data from the GPS <NUM>, altitude sensor <NUM>, location sensor <NUM>, radar <NUM>, LIDAR <NUM>, Sonar <NUM>, orientation sensor <NUM>, IMU <NUM>, and/or onboard avionics <NUM>. In some embodiments, the UAV processor <NUM> may also receive weather data from the weather sensor <NUM>, the barometric pressure sensor <NUM>, and/or the anemometer. The UAV processor <NUM> may also receive the time from the time measurement device <NUM>. The UAV processor <NUM> may fuse the gas data from the payload <NUM> with the UAV <NUM> spatial position data, weather data, and/or time to form a UAV Data Packet <NUM>.

The UAV data packet <NUM> may be sent to the processor <NUM>, ground control station <NUM>, and/or cloud server <NUM> via the transceiver <NUM> and/or wireless radio <NUM>. In some embodiments, the wireless radio <NUM> or cellular connection may be used for remote data transfer between the UAV <NUM>, the GCS <NUM>, the processor <NUM>, and/or the cloud server <NUM>. The wireless interface or cellular connection between the UAV <NUM>, the GCS <NUM>, the processor <NUM>, and/or the cloud server <NUM> may be used to performing advanced data analysis functions. Direct, bidirectional data transfer may occur between the UAV <NUM> and the GCS <NUM>, between the UAV <NUM> and the cloud server <NUM>, and/or between the GCS <NUM> and the cloud server <NUM>.

The processor <NUM> may be a part of the UAV <NUM>, the GCS <NUM>, the cloud server <NUM>, and/or the weather station <NUM> in some embodiments. While multiple sensors and devices are depicted for the UAV <NUM>, any number of sensors and/or devices may be used based on the system <NUM>, desired accuracy, time limitations, weight limitations, and the like.

One or more weather stations <NUM>, <NUM>, <NUM> may provide local weather information to the UAV <NUM>, payload <NUM>, GCS <NUM>, and/or cloud server <NUM>. The weather stations <NUM>, <NUM>, <NUM> may also receive information from the UAV <NUM>, payload <NUM>, GCS <NUM>, and/or cloud server <NUM>.

The first weather station <NUM> may include one or more anemometers <NUM>, one or more pressure sensors <NUM>, one or more pyranometers <NUM>, one or more ground temperature sensors <NUM>, one or more air temperature sensors <NUM>, one or more atmospheric condition sensors <NUM>, and one or more location sensors <NUM>. The anemometer may be used to measure wind speed. The pressure sensor <NUM> may measure a pressure. The pyranometer may be used to measure solar irradiance. The ground temperature sensor <NUM> may be used to measure a temperature of the ground. The air temperature sensor <NUM> may be used to measure a temperature of the air. An atmospheric condition sensor <NUM> may be used to measure data relating to the atmosphere. The location sensor <NUM> may be used to measure the location of the weather station <NUM>. Each weather station <NUM>, <NUM>, <NUM> may include any number of sensors and/or devices based on the system <NUM>, desired accuracy, number of weather stations over a geographical area, and the like.

In some embodiments, sensors and/or devices of the weather station <NUM> may be located and/or duplicated on the UAV <NUM>. High resolution (<<NUM>/s), high-frequency measurements (> <NUM>) of wind speed and direction may be recorded using one or more wind sensors, and one or more additional weather/micro-meteorological sensors including, air temperature, humidity, atmospheric pressure, solar irradiance, ground surface temperature - from the ground via a weather station <NUM>, <NUM>, <NUM> and/or from the UAV <NUM> as disclosed herein. For example, both the weather station <NUM> and the UAV <NUM> may include respective anemometers <NUM>, <NUM>, which may be used to generate wind speed data. The weather station data may be associated with a time the data was collected and/or generated. The weather station data may be used to generate a Meteorological (MET) data packet <NUM>. The Meteorological data packet <NUM> may be sent to the processor <NUM>, ground control station <NUM>, cloud server <NUM>, and/or UAV <NUM>. The Meteorological data packet <NUM> may include measurements and/or predictions of the atmosphere, weather, temperature, wind patterns, or the like.

Each UAV Data Packet <NUM> may be combined with the nearest temporal Meteorological Data Packet <NUM> by the processor <NUM> and saved on the GCS <NUM> and/or cloud server <NUM>. The data may be uploaded to the cloud server <NUM> in real-time, near real-time, or at a later time. The combined UAV data packet <NUM> and Meteorological data packet <NUM> may be used to determine an elevated ambient emission of the methane source by the processor <NUM>, GCS <NUM>, and/or cloud server <NUM>. The elevated ambient emission may be determined based on a control volume model that combines concentration measurements from the UAV flight plane with measured wind speed, direction and spatial gradient to determine the mass flow rate emissions from sources in the inspection area.

This determined elevated ambient emission may used to generate a back trajectory, store the back trajectories in a grid, determine a gas source location probability, and/or generate an overlay of the probability of the gas source location by the processor <NUM>, GCS <NUM>, and/or cloud server <NUM>. The display <NUM> may show the overlay on a map, satellite image, aerial image, two-dimensional color map, two-dimensional contour map, and/or three-dimensional topographical surface / mesh.

<FIG> depicts a high-level flowchart of a method <NUM> embodiment of detecting and localizing methane sources via point source gas measurements, according to one embodiment. The method <NUM> may include generating a UAV data packet including a point source gas concentration measurement and UAV information (step <NUM>). The UAV data packet may be generated by one or more UAVs. The method <NUM> may also include generating, by a weather station, a Meteorological data packet including data measured by the weather station. One or more weather stations may be used. The weather station may be located on the UAV or another UAV in some embodiments. The weather station may include third-party data in some embodiments. The method <NUM> then includes receiving, by the processor, the UAV data packet and the Meteorological data packet (step <NUM>). The UAV data packet may be joined with the nearest temporal Meteorological data packet.

The method <NUM> may then include determining if each point source gas concentration measurement is an elevated ambient gas concentration (step <NUM>). Levels of methane, or other gasses, may be present in the atmosphere at certain levels. The processor determines if an elevated level of methane, or another gas, is detected which could indicate a gas leak from a gas source. The method <NUM> may then include generating a back trajectory for each elevated ambient gas concentration (step <NUM>). The point source gas concentration reading location, and meteorological data allow the system and method <NUM> to use a stochastic particle back trajectory model to determine a back trajectory of each elevated ambient gas concentration. The back trajectory model allows the system and method <NUM> to determine a probable location of a gas source by combining the cumulative calculated back trajectories.

The method <NUM> may then include storing the position of each generated back trajectory in a grid (step <NUM>). In some embodiments, the grid may be two-dimensional (2D) having x, y coordinates for each cell in the grid. In other embodiments, the grid may be three-dimensional (3D) having x, y, z coordinates for each cell in the grid. Each cell in the grid may be summed to find a density of each cell in the grid. The method <NUM> may then include normalizing the stored position of each generated back trajectory in the grid (step <NUM>). Normalizing may be used to clean up the results of the system and method <NUM>. For example, an entire area may have a non-zero probability of containing a gas source causing the detected elevated ambient gas concentration. By normalizing the results, the probability may be confined to an area with a higher likelihood of containing the gas source.

The method <NUM> may then include determining a probability of a gas source location corresponding to the stored positions in the grid (step <NUM>). The method <NUM> may then include generating an overlay showing the probability of the gas source location (step <NUM>). The method <NUM> may then include displaying the generated overlay on a map (step <NUM>). The map may be 2D map or a 3D map. The displayed overlay and map may be used to identify the most likely sources of gas leaks, which may then be used to take corrective action to repair equipment, minimize or eliminate gas leaks, or the like. The processor having addressable memory may be a part of a ground control system (GCS), a cloud server, a remote server, and/or one or more UAVs.

<FIG> is a high-level block diagram <NUM> showing a computing system comprising a computer system useful for implementing an embodiment of the system and process, disclosed herein. Embodiments of the system may be implemented in different computing environments. The computer system includes one or more processors <NUM>, and can further include an electronic display device <NUM> (e.g., for displaying graphics, text, and other data), a main memory <NUM> (e.g., random access memory (RAM)), storage device <NUM>, a removable storage device <NUM> (e.g., removable storage drive, a removable memory module, a magnetic tape drive, an optical disk drive, a computer readable medium having stored therein computer software and/or data), user interface device <NUM> (e.g., keyboard, touch screen, keypad, pointing device), and a communication interface <NUM> (e.g., modem, a network interface (such as an Ethernet card), a communications port, or a PCMCIA slot and card). The communication interface <NUM> allows software and data to be transferred between the computer system and external devices. The system further includes a communications infrastructure <NUM> (e.g., a communications bus, cross-over bar, or network) to which the aforementioned devices/modules are connected as shown.

Information transferred via communications interface <NUM> may be in the form of signals such as electronic, electromagnetic, optical, or other signals capable of being received by communications interface <NUM>, via a communication link <NUM> that carries signals and may be implemented using wire or cable, fiber optics, a phone line, a cellular/mobile phone link, an radio frequency (RF) link, and/or other communication channels. Computer program instructions representing the block diagram and/or flowcharts herein may be loaded onto a computer, programmable data processing apparatus, or processing devices to cause a series of operations performed thereon to produce a computer implemented process.

Embodiments have been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments. Each block of such illustrations/diagrams, or combinations thereof, can be implemented by computer program instructions. The computer program instructions when provided to a processor produce a machine, such that the instructions, which execute via the processor, create means for implementing the functions/operations specified in the flowchart and/or block diagram. Each block in the flowchart/block diagrams may represent a hardware and/or software module or logic, implementing embodiments. In alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures, concurrently, etc..

Computer programs (i.e., computer control logic) are stored in main memory and/or secondary memory. Computer programs may also be received via a communications interface <NUM>. Such computer programs, when executed, enable the computer system to perform the features of the embodiments as discussed herein. In particular, the computer programs, when executed, enable the processor and/or multi-core processor to perform the features of the computer system. Such computer programs represent controllers of the computer system.

<FIG> shows a block diagram of an example system <NUM> in which an embodiment may be implemented. The system <NUM> includes one or more client devices <NUM> such as consumer electronics devices, connected to one or more server computing systems <NUM>. A server <NUM> includes a bus <NUM> or other communication mechanism for communicating information, and a processor (CPU) <NUM> coupled with the bus <NUM> for processing information. The server <NUM> also includes a main memory <NUM>, such as a random access memory (RAM) or other dynamic storage device, coupled to the bus <NUM> for storing information and instructions to be executed by the processor <NUM>. The main memory <NUM> also may be used for storing temporary variables or other intermediate information during execution or instructions to be executed by the processor <NUM>. The server computer system <NUM> further includes a read only memory (ROM) <NUM> or other static storage device coupled to the bus <NUM> for storing static information and instructions for the processor <NUM>. A storage device <NUM>, such as a magnetic disk or optical disk, is provided and coupled to the bus <NUM> for storing information and instructions. The bus <NUM> may contain, for example, thirty-two address lines for addressing video memory or main memory <NUM>. The bus <NUM> can also include, for example, a <NUM>-bit data bus for transferring data between and among the components, such as the CPU <NUM>, the main memory <NUM>, video memory and the storage <NUM>. Alternatively, multiplex data/address lines may be used instead of separate data and address lines.

The server <NUM> may be coupled via the bus <NUM> to a display <NUM> for displaying information to a computer user. An input device <NUM>, including alphanumeric and other keys, is coupled to the bus <NUM> for communicating information and command selections to the processor <NUM>. Another type or user input device comprises cursor control <NUM>, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to the processor <NUM> and for controlling cursor movement on the display <NUM>.

According to one embodiment, the functions are performed by the processor <NUM> executing one or more sequences of one or more instructions contained in the main memory <NUM>. Such instructions may be read into the main memory <NUM> from another computer-readable medium, such as the storage device <NUM>. Execution of the sequences of instructions contained in the main memory <NUM> causes the processor <NUM> to perform the process steps described herein. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in the main memory <NUM>. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement the embodiments. Thus, embodiments are not limited to any specific combination of hardware circuitry and software.

The terms "computer program medium," "computer usable medium," "computer readable medium", and "computer program product," are used to generally refer to media such as main memory, secondary memory, removable storage drive, a hard disk installed in hard disk drive, and signals. These computer program products are means for providing software to the computer system. The computer readable medium allows the computer system to read data, instructions, messages or message packets, and other computer readable information from the computer readable medium. The computer readable medium, for example, may include non-volatile memory, such as a floppy disk, ROM, flash memory, disk drive memory, a CD-ROM, and other permanent storage. It is useful, for example, for transporting information, such as data and computer instructions, between computer systems. Furthermore, the computer readable medium may comprise computer readable information in a transitory state medium such as a network link and/or a network interface, including a wired network or a wireless network that allow a computer to read such computer readable information. Computer programs (also called computer control logic) are stored in main memory and/or secondary memory. Computer programs may also be received via a communications interface. Such computer programs, when executed, enable the computer system to perform the features of the embodiments as discussed herein. In particular, the computer programs, when executed, enable the processor multi-core processor to perform the features of the computer system. Accordingly, such computer programs represent controllers of the computer system.

Generally, the term "computer-readable medium" as used herein refers to any medium that participated in providing instructions to the processor <NUM> for execution. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Non-volatile media includes, for example, optical or magnetic disks, such as the storage device <NUM>. Volatile media includes dynamic memory, such as the main memory <NUM>. Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise the bus <NUM>. Transmission media can also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.

Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read.

Various forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to the processor <NUM> for execution. For example, the instructions may initially be carried on a magnetic disk of a remote computer. A modem local to the server <NUM> can receive the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infrared detector coupled to the bus <NUM> can receive the data carried in the infrared signal and place the data on the bus <NUM>. The bus <NUM> carries the data to the main memory <NUM>, from which the processor <NUM> retrieves and executes the instructions. The instructions received from the main memory <NUM> may optionally be stored on the storage device <NUM> either before or after execution by the processor <NUM>.

The server <NUM> also includes a communication interface <NUM> coupled to the bus <NUM>. The communication interface <NUM> provides a two-way data communication coupling to a network link <NUM> that is connected to the world wide packet data communication network now commonly referred to as the Internet <NUM>. The Internet <NUM> uses electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link <NUM> and through the communication interface <NUM>, which carry the digital data to and from the server <NUM>, are exemplary forms or carrier waves transporting the information.

In another embodiment of the server <NUM>, interface <NUM> is connected to a network <NUM> via a communication link <NUM>. For example, the communication interface <NUM> may be an integrated services digital network (ISDN) card or a modem to provide a data communication connection to a corresponding type of telephone line, which can comprise part of the network link <NUM>. As another example, the communication interface <NUM> may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. In any such implementation, the communication interface <NUM> sends and receives electrical electromagnetic or optical signals that carry digital data streams representing various types of information.

The network link <NUM> typically provides data communication through one or more networks to other data devices. For example, the network link <NUM> may provide a connection through the local network <NUM> to a host computer <NUM> or to data equipment operated by an Internet Service Provider (ISP). The ISP in turn provides data communication services through the Internet <NUM>. The local network <NUM> and the Internet <NUM> both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link <NUM> and through the communication interface <NUM>, which carry the digital data to and from the server <NUM>, are exemplary forms or carrier waves transporting the information.

The server <NUM> can send/receive messages and data, including e-mail, program code, through the network, the network link <NUM> and the communication interface <NUM>. Further, the communication interface <NUM> can comprise a USB/Tuner and the network link <NUM> may be an antenna or cable for connecting the server <NUM> to a cable provider, satellite provider or other terrestrial transmission system for receiving messages, data and program code from another source.

The example versions of the embodiments described herein may be implemented as logical operations in a distributed processing system such as the system <NUM> including the servers <NUM>. The logical operations of the embodiments may be implemented as a sequence of steps executing in the server <NUM>, and as interconnected machine modules within the system <NUM>. The implementation is a matter of choice and can depend on performance of the system <NUM> implementing the embodiments. As such, the logical operations constituting said example versions of the embodiments are referred to for e.g., as operations, steps or modules.

Similar to a server <NUM> described above, a client device <NUM> can include a processor, memory, storage device, display, input device and communication interface (e.g., e-mail interface) for connecting the client device to the Internet <NUM>, the ISP, or LAN <NUM>, for communication with the servers <NUM>.

The system <NUM> can further include computers (e.g., personal computers, computing nodes) <NUM> operating in the same manner as client devices <NUM>, wherein a user can utilize one or more computers <NUM> to manage data in the server <NUM>.

Claim 1:
A method, comprising:
receiving, by a ground control station (GCS) (<NUM>) having a processor (<NUM>) with addressable memory (<NUM>), a plurality of point source gas concentration measurements measured by one or more in situ gas concentration sensors disposed on an unmanned aerial vehicle, UAV, (<NUM>) along a flight path;
receiving, by the GCS, a spatial position of the UAV (<NUM>) corresponding to each point gas source concentration measurement;
receiving, by the GCS, data associated with one or more of: wind, temperature and pressure corresponding to each point source gas concentration measurement;
determining, by the GCS, if each point source gas concentration measurement of the plurality of point source gas concentration measurements is an elevated above ambient gas concentration;
generating, by the GCS, a plurality of back trajectories (<NUM>) for the plurality of gas concentration measurements, based on the gas concentration measurements and the data associated with the one or more of: wind, temperature and pressure, wherein each back trajectory (<NUM>) is generated for a respective elevated above ambient gas concentration, and wherein at least one back trajectory is generated independently of one or more other back trajectories;
determining, by the GCS, a probability of a gas source location corresponding to positions of one or more of the generated back trajectories.