Patent Description:
The field of the disclosure relates to technologies for improving visualization of gas leak detection data.

A typical gas leak detection survey results in data regarding locations where gas was detected (and, often, concentrations of one or more gases measured at those locations). This data may be used to predict estimated gas source locations suspected to be emitting the detected gas. In some gas leak detection surveys, the data regarding gas detection locations and/or estimated gas source locations is displayed on a geographic map using markers. However, interpreting these markers can be difficult. Accordingly, a problem with current gas leak detection data visualization is that finding the actual gas leak is very difficult due to the interpretation of the markers being difficult. <NPL>) proposes a method of detecting air leaks by combining a laser scanner for SLAM localization and an ultrasonic microphone array on a robot. The document <CIT> discloses measurement approaches and data analysis methods for combining 3D topographic data with spatially-registered gas concentration data to increase the efficiency of gas monitoring and leak detection tasks.

Thus, it is desirable to improve the visualization of gas leak detection data to simplify and make more efficient the process for finding actual gas leaks.

In one aspect, according to claim <NUM>, a visualization system for visualizing gas leak detection data including a processor in communication with at least one memory device is provided. The processor is configured to: (i) receive, from one or more sensors, gas measurement data for a plurality of measurement locations within a survey area, wherein the gas measurement data includes gas concentration data, (ii) receive, from the at least one memory device, a geographic representation of the survey area, (iii) determine, based upon the gas measurement data, estimated gas source locations of the plurality of measurement locations, wherein the estimated gas source locations include one or more measurement locations of the plurality of measurement locations where the gas concentration data is at or above a threshold, and (iv) utilize, by a smoothing algorithm, point density data of the estimated gas source locations to create a multi-dimensional density plot.

In another aspect, a computer-implemented method is provided according to claim <NUM>.

In yet another aspect, a non-transitory computer-readable media having computer-executable instructions embodied thereon is provided according to claim <NUM>.

As used herein, the terms "processor" and "computer," and related terms, e.g., "processing device," "computing device," and "controller" are not limited to just those integrated circuits referred to in the art as a computer, but broadly refers to a microcontroller, a microcomputer, an analog computer, a programmable logic controller (PLC), an application specific integrated circuit (ASIC), and other programmable circuits, and these terms are used interchangeably herein. In the embodiments described herein, "memory" may include, but is not limited to, a computer-readable medium, such as a random-access memory (RAM), a computer-readable non-volatile medium, such as a flash memory. Alternatively, a floppy disk, a compact disc - read only memory (CD-ROM), a magneto-optical disk (MOD), and/or a digital versatile disc (DVD) may also be used. Also, in the embodiments described herein, additional input channels may be, but are not limited to, computer peripherals associated with an operator interface such as a touchscreen, a mouse, and a keyboard. Alternatively, other computer peripherals may also be used that may include, for example, but not be limited to, a scanner. Furthermore, in the example embodiment, additional output channels may include, but not be limited to, an operator interface monitor or heads-up display. Some embodiments involve the use of one or more electronic or computing devices. Such devices typically include a processor, processing device, or controller, such as a general-purpose central processing unit (CPU), a graphics processing unit (GPU), a microcontroller, a reduced instruction set computer (RISC) processor, an ASIC, a programmable logic controller (PLC), a field programmable gate array (FPGA), a digital signal processing (DSP) device, and/or any other circuit or processing device capable of executing the functions described herein. The methods described herein may be encoded as executable instructions embodied in a computer readable medium, including, without limitation, a storage device and/or a memory device. Such instructions, when executed by a processing device, cause the processing device to perform at least a portion of the methods described herein. The above examples are not intended to limit in any way the definition and/or meaning of the term processor and processing device.

As discussed previously, typical gas leak detection systems use markers on a geographic map to show a potential gas leak location. These markers are hard to interpret and make the actual localization of a gas leak very difficult.

In the embodiments described herein, technologies using smoothing algorithms for improved visualization of gas leak detection data are described. The presently disclosed technologies apply smoothing algorithms to an estimated gas source location (or similar) data to indicate the likely location for each actual gas source in the surveyed area, in a manner analogous to a probability distribution plot. Plots based on this approach allow for a more informed approach to finding actual gas sources.

Portable gas analyzers, which include one or more detection sensors as described herein, have allowed for increased ease in finding emissions of specified gases over a large area by walking, driving, or flying with the instrument though the area. During the course of the survey, isolated or closely spaced Gaussian-like peaks emerge in the time trace of the gas concentrations as the analyzer comes either near a gas source or is appropriately downwind of the gas source. Using global positioning system (GPS) location data of the gas detections as well as the velocity data (including both speed and direction) of the analyzer movement and local instantaneous wind data, the estimated location(s) of one or more gas sources can be calculated from the data collected. For example, such a gas leak detection survey can be performed by any of ABB's MicroGuard™ (walking survey), MobileGuard™ (driving survey), and HoverGuard™ (flying survey, using an unmanned aerial vehicle (UAV)) gas leak detection solutions, all commercially available in the United States from ABB Inc. In other embodiments, any gas leak detection survey platform that produces estimated gas source locations (or the data needed to calculate estimated gas source locations) can be used.

For example, a UAV may follow a flight path through a survey area, while detection sensors (e.g., of a portable gas analyzer) carried by the UAV collect measurements of the concentration of one or more gases of interest at points along the flight path. As described above, the detection sensors also collect data regarding location (e.g., GPS data) and velocity (e.g., UAV velocity data, wind velocity data) corresponding to each gas concentration measurement.

<FIG> depicts a simplified block diagram of a computer system <NUM> in an exemplary embodiment. In the exemplary embodiment, computer system <NUM> may be used for generating a gas leak detection visualization based upon an analysis of received sensor data, as described further herein. In the exemplary embodiment, system <NUM> may include a visualization computing device <NUM> and a database server <NUM>. Visualization computing device <NUM> may be in communication with one or more databases <NUM> (or other memory devices), detection sensors <NUM>, and/or user devices <NUM>.

Database server <NUM> may be communicatively coupled to database <NUM> that stores data. In one embodiment, database <NUM> may include map data, historical gas leak data, historical gas visualization data, etc. In the exemplary embodiment, database <NUM> may be stored remotely from visualization computing device <NUM>. In some embodiments, database <NUM> may be decentralized. In the exemplary embodiment, a user may access database <NUM> and/or visualization computing device via user device <NUM>.

Detection sensors <NUM> may be communicatively coupled with visualization computing device <NUM>. In some embodiments, detection sensors <NUM> may be associated with, or otherwise in communication with, a detection vehicle located proximate the site of the gas leak. For example, detection sensors <NUM> may be coupled to an unmanned aerial vehicle (UAV) that takes measurements at the site of the gas leak. Detection sensors <NUM> may include any sensors associated with detecting and locating a gas leak. For example, detection sensors <NUM> may include any of gas concentration sensors, global positioning system (GPS) sensors, wind sensors, height sensors, accelerometers, gyroscopes, and any other suitable sensors.

Detection sensors <NUM> may be communicatively coupled to the Internet through many interfaces including, but not limited to, at least one of a network, such as the Internet, a local area network (LAN), a wide area network (WAN), or an integrated services digital network (ISDN), a dial-up-connection, a digital subscriber line (DSL), a cellular phone connection, and a cable modem.

Visualization computing device <NUM> may be in communication with a plurality of user devices <NUM>. In the exemplary embodiment, user devices <NUM> may be a smartphone, tablet, or laptop that one or more users have at a survey location. That is, user devices <NUM> may be computers that include a web browser or a software application, which enables user computing devices <NUM> to access remote computer devices, such as visualization computing device <NUM>, using the Internet or other network. More specifically, user devices <NUM> may be communicatively coupled to visualization computing device <NUM> through many interfaces including, but not limited to, at least one of the Internet, a network, such as the Internet, a local area network (LAN), a wide area network (WAN), or an integrated services digital network (ISDN), a dial-up-connection, a digital subscriber line (DSL), a cellular phone connection, and a cable modem. User devices <NUM> may be any device capable of accessing the Internet including, but not limited to, a desktop computer, a laptop computer, a personal digital assistant (PDA), a cellular phone, a smartphone, a tablet, a phablet, wearable electronics, smart watch, or other web-based connectable equipment or mobile devices.

<FIG> depicts a user device <NUM>, such as user device <NUM> as shown in <FIG>. User device <NUM> may be operated by a user <NUM>. User device <NUM> may include a processor <NUM> for executing instructions. In some embodiments, executable instructions may be stored in a memory area <NUM>. Processor <NUM> may include one or more processing units (e.g., in a multi-core configuration). Memory area <NUM> may be any device allowing information such as executable instructions and/or detection data to be stored and retrieved. Memory area <NUM> may include one or more computer readable media.

User device <NUM> may also include at least one media output component <NUM> for presenting information to user <NUM>. Media output component <NUM> may be any component capable of conveying information to user <NUM>. In some embodiments, media output component <NUM> may include an output adapter (not shown) such as a video adapter and/or an audio adapter. An output adapter may be operatively coupled to processor <NUM> and adapted to operatively couple to an output device such as a display device (e.g., a cathode ray tube (CRT), liquid crystal display (LCD), light emitting diode (LED) display, or "electronic ink" display) or an audio output device (e.g., a speaker or headphones).

In some embodiments, media output component <NUM> may be configured to present a graphical user interface (e.g., a web browser and/or a client application) to user <NUM>. A graphical user interface may include, for example, one or more visualizations of a gas leak. In some embodiments, user device <NUM> may include an input device <NUM> for receiving input from user <NUM>. User <NUM> may use input device <NUM> to, without limitation, select and/or enter data, input commands to change views of the visualization, confirm or deny whether a gas leak was present at the predicted location, etc..

Input device <NUM> may include, for example, a keyboard, a pointing device, a mouse, a stylus, a touch sensitive panel (e.g., a touch pad or a touch screen), a gyroscope, an accelerometer, a position detector, a biometric input device, and/or an audio input device. A single component such as a touch screen may function as both an output device of media output component <NUM> and input device <NUM>.

User device <NUM> may also include a communication interface <NUM>, communicatively coupled via a network to visualization computing device <NUM> (shown in <FIG>). Communication interface <NUM> may include, for example, a wired or wireless network adapter and/or a wireless data transceiver for use with a mobile telecommunications network.

Stored in memory area <NUM> are, for example, computer-readable instructions for providing a user interface to user <NUM> via media output component <NUM> and, optionally, receiving and processing input from input device <NUM>. A user interface may include, among other possibilities, a web browser and/or a client application. Web browsers enable users, such as user <NUM>, to display and interact with media and other information typically embedded on a web page or a website.

<FIG> depicts a server system <NUM> such as modeling computing device <NUM> and detection sensors <NUM>, as shown in <FIG>, and in accordance with one exemplary embodiment of the present disclosure. Accordingly, server system <NUM> may include a processor <NUM> for executing instructions. Instructions may be stored in a memory area <NUM>. Processor <NUM> may include one or more processing units (e.g., in a multi-core configuration).

Processor <NUM> may be operatively coupled to a communication interface <NUM> such that server system <NUM> is capable of communicating with a remote computing device. For example, communication interface <NUM> may receive requests from user devices <NUM> via the Internet and/or over a computer network.

Processor <NUM> may also be operatively coupled to a storage device <NUM> (e.g., database <NUM>, shown in <FIG>). Storage device <NUM> may be any computer-operated hardware suitable for storing and/or retrieving data. In some embodiments, storage device <NUM> may be integrated in server system <NUM>. For example, server system <NUM> may include one or more hard disk drives as storage device <NUM>. In other embodiments, storage device <NUM> may be external to server system <NUM> and may be accessed by a plurality of server systems <NUM>. For example, storage device <NUM> may include a storage area network (SAN), a network attached storage (NAS) system, and/or multiple storage units such as hard disks and/or solid-state disks in a redundant array of inexpensive disks (RAID) configuration.

In some embodiments, processor <NUM> may be operatively coupled to storage device <NUM> via a storage interface <NUM>. Storage interface <NUM> may be any component capable of providing processor <NUM> with access to storage device <NUM>. Storage interface <NUM> may include, for example, an Advanced Technology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, a Small Computer System Interface (SCSI) adapter, a RAID controller, a SAN adapter, a network adapter, and/or any component providing processor <NUM> with access to storage device <NUM>.

<FIG> illustrates a flow chart of an exemplary computer-implemented process <NUM> for visualizing gas leak detection data. The process <NUM> may be carried out by visualization system <NUM> (shown in <FIG>), and more specifically, may be carried out by visualization computing device <NUM> (shown in <FIG>).

Gas measurements are received <NUM> (e.g., by visualization computing device <NUM>) from one or more sensors (e.g., detection sensors <NUM>) for a plurality of measurement locations within a survey area. The gas measurements may include, for example, gas concentration measurements, global positioning system (GPS) measurements, wind velocity measurements, height and/or altitude measurements, and any other suitable measurements associated with visualizing gas detection data, as described further herein. A geographic representation of the survey area is received <NUM> (e.g., from database <NUM>, shown in <FIG>, by visualization computing device <NUM>). The geographic representation may be received <NUM> by visualization computing device <NUM> from database <NUM> based upon location data included in the gas measurements, or the geographic representation may be received <NUM> by visualization computing device <NUM> from database <NUM> before gas measurements are received <NUM> (e.g., a user may input a location of the survey area into visualization computing device <NUM> before gas measurements are taken by detection sensors <NUM>).

The received <NUM> gas measurement data is used to determine <NUM> (e.g., by visualization computing device <NUM>) estimated gas source locations. The gas source locations may be, for example, one or more measurement locations of the plurality of measurement locations where a gas concentration measurement was at or above a threshold. The threshold may be relative to all the gas concentration measurements (e.g., all the gas concentration measurements may be compared, and a threshold may be determined based upon the measurements more likely to be associated with a gas source), or the threshold may be predetermined based on factors like the geography of the survey area, wind conditions at the survey area, etc..

A smoothing algorithm is applied <NUM> (e.g., by visualization computing device <NUM>) to the estimated <NUM> gas source locations to create a multi-dimensional density plot. As described in more detail below, the smoothing algorithm may be a kernel density estimation (KDE) algorithm that utilizes point density data to estimate a corresponding probability density function, also referred to herein as the multi-dimensional density plot. The created multi-dimensional density plot may be superimposed on the received <NUM> geographical representation of the survey area to generate a graphic for display on a user device (e.g., user device <NUM>, shown in <FIG>).

<FIG> depicts a graphic <NUM> that may be generated (e.g., by visualization computing device <NUM> for display on user device <NUM>, shown in <FIG>) to illustrate a flight path <NUM> of an unmanned aerial vehicle (UAV) and gas measurements made (e.g., by detection sensors <NUM>, shown in <FIG>) along flight path <NUM> in an exemplary embodiment. In <FIG>, flight path <NUM> of the UAV is superimposed over a satellite image <NUM> of a portion of the earth that includes a survey area <NUM>. Satellite image <NUM> may be retrieved from database <NUM>, shown in <FIG>, of visualization computing device <NUM> based on GPS data from detection sensors <NUM>. It is contemplated that in other embodiments, flight path <NUM> of the UAV may be superimposed over other representations of the portion of the earth that includes survey area <NUM>, such as two-dimensional and three-dimensional maps, including physical, topographical, and street maps. In <FIG>, flight path <NUM> of the UAV is coded with a pattern to represent the concentration of a particular gas (e.g., methane) measured at each location. In other embodiments, flight path <NUM> of the UAV may be color-coded. Graphics similar to <FIG> may also be generated for walking and driving gas leak detection surveys to illustrate a path of a portable gas analyzer and its detection sensors, and the gas measurements made along that path.

Using the data collected by detection sensors <NUM> (e.g., gas concentration measurements associated with location and instrument/wind velocity measurements, as described above), estimated gas source locations can be predicted. For instance, visualization computing device <NUM> may receive the data collected by detection sensors <NUM> and calculate one or more estimated gas source locations from the data. In one illustrative embodiment, for each detection location at which the concentration of a gas of interest (e.g., methane) exceeds a threshold, visualization computing device <NUM> calculates an estimated location of the source of that gas, together with an associated spatial uncertainty ellipse.

<FIG> depicts a graphic that may be generated (e.g., by visualization computing device <NUM> for display on user device <NUM>, shown in <FIG>) to illustrate estimated gas source locations <NUM> and associated uncertainty spatial ellipses <NUM> determined by system <NUM>, shown in <FIG> in an exemplary embodiment. In the exemplary embodiment, to illustrate this information, spatial ellipses are superimposed over a satellite image <NUM> similar to and retrieved similarly to satellite image <NUM>, shown in <FIG>. In other embodiments, spatial ellipses <NUM> may be represented by a yellow marker and superimposed over satellite image <NUM>. Each spatial ellipse <NUM> has an apex <NUM> at a detection location (point at which the measured concentration of methane exceeded a threshold), contains a dot representing gas source location <NUM> for the gas measured at that detection location, and includes a periphery <NUM> representing uncertainty of spatial ellipse <NUM> associated with that estimated gas source location <NUM>. In some embodiments, a color of the dot may contrast with a color of spatial ellipse <NUM>. For example, when spatial ellipse <NUM> is a yellow marker, the dot may be white. As with <FIG>, it is contemplated that satellite image <NUM> of <FIG> could be replaced with other representations of the portion of the earth that includes the survey area.

In the illustrative embodiment of <FIG>, there are fifty-six individual detection locations (points at which the measured concentration of methane exceeded a threshold), each with a corresponding estimated gas source location <NUM> and an associated uncertainty spatial ellipse <NUM>. While the graphic of <FIG> fully describes all the raw data from detection sensors <NUM> (shown in <FIG>) and computed information on the estimated gas source locations <NUM> by visualization computing device <NUM>, there are both technical and aesthetic reasons to simplify this graphic <NUM>. From a technical perspective, multiple closely spaced gas detections are likely associated with the same gas source and, therefore, should often be aggregated as a single gas source. In other words, it is unlikely that there are fifty-six separate gas sources and much more likely that a few sources have been detected by detection sensors <NUM> repeatedly. From an aesthetic and actionability perspective, the number of markers in graphic <NUM> of <FIG> make it difficult for users (e.g., of user devices <NUM>) to ascertain the most likely gas source locations <NUM>.

As shown in <FIG>, one approach to simplify the presentation of this information is to aggregate (e.g., average) all the data represented by the fifty-six markers of <FIG> to arrive at a single estimated gas source location <NUM> and generate a graphic <NUM> including a single marker illustrating that estimated gas source location <NUM> and its associated uncertainty spatial ellipse <NUM>. While fewer markers clearly offer a simpler view, it often oversimplifies the true picture (e.g., where multiple gas sources are present) and obscures the underlying data.

Another approach to improving the visualization of the gas leak detection data described above is to apply a smoothing algorithm to the estimated gas source locations to create a more accessible display of this information. In one illustrative embodiment of the present disclosure, a kernel density estimation (KDE) algorithm is used as the smoothing algorithm. KDE is a non-parametric approach that utilizes point density data to estimate a corresponding probability density function. Mathematically, the kernel density estimator is given by: <MAT> where K is the kernel function and h is a smoothing parameter referred to as the "bandwidth. " A variety of different mathematical functions may be used as the kernel function K. In the illustrative embodiment corresponding to <FIG>, a two-dimensional Gaussian function is used as the kernel function. It is contemplated that other embodiments may use a uniform (rectangular or "boxcar") function, a triangular function, an Epanechnikov (parabolic) function, quartic (biweight) function, triweight function, tricube function, cosine function, logistic function, sigmoid function, or another suitable function as the kernel function K.

As noted above, the smoothing (e.g., KDE) algorithm is applied, by visualization computing device <NUM>, shown in <FIG>, to the estimated gas source locations (e.g., estimated gas source locations <NUM>, shown in <FIG>) determined from the gas leak survey data generated by detection sensors <NUM>, shown in <FIG>. As such, and as described at least with respect to <FIG>, the methods of the present disclosure may include obtaining estimated gas source locations. In various embodiments, "obtaining" these estimated gas source locations may involve receiving the estimated gas source locations from another source (e.g., from another computer), retrieving the estimated gas source locations from a local memory (e.g., database <NUM>, shown in <FIG>), and/or calculating, by visualization computing device <NUM>, the estimated gas source locations from other data generated by detection sensors <NUM> (e.g., gas concentration measurements associated with location and instrument/wind velocity measurements received from a gas leak detection survey platform, as described above).

<FIG> depicts a graphic <NUM> (e.g., generated by visualization computing device <NUM> to be displayed on user device <NUM>, shown in <FIG>) illustrating just estimated gas source locations <NUM> (e.g., just illustrating estimated gas source locations <NUM>, shown in <FIG>) in an exemplary embodiment. Whereas graphic <NUM> is embodied as a two-dimensional (overhead) view, graphic <NUM> is embodied as a quasi-three-dimensional (perspective) view. It is contemplated that any of the graphics of the present disclosure can be embodied in either two- or three-dimensional style views. In the illustrative embodiment of <FIG>, all the estimated gas source locations <NUM> are equally weighted (e.g., each assigned an amplitude of <NUM> at the x-y coordinate corresponding to that location).

A smoothing algorithm is then used to calculate a density plot over the spatial extent of the data. As discussed above, in the illustrative embodiment, a KDE algorithm using a two-dimensional Gaussian function as the kernel function K is applied to the estimated gas source locations: <MAT> In the illustrative embodiment, a fixed bandwidth of h = <NUM> is used in both the x and y dimensions. When this KDE algorithm is applied by visualization computing device <NUM> to the data points of <FIG> (i.e., estimated gas source locations <NUM>), visualization computing device <NUM> produces a kernel density plot <NUM> illustrated in <FIG>.

<FIG> depicts a graphic <NUM> illustrating a result of applying a smoothing algorithm to the estimated gas source locations (e.g., estimated gas source locations <NUM>, shown in <FIG> and/or estimated gas source locations <NUM>, shown in <FIG>) in an exemplary embodiment. Graphic <NUM> may be generated, for example, by visualization computing device <NUM> for display on user device <NUM> (shown in <FIG>). In this embodiment, graphic <NUM> includes kernel density plot <NUM>, which is a result of applying the KDE algorithm described above to the estimated gas source locations, superimposed over a satellite image <NUM> similar to and retrieved in a similar way to that of satellite image <NUM> (shown in <FIG>). As with each of the graphics discussed above, it is contemplated that satellite image <NUM> could be replaced with other representations (e.g., maps) of the portion of the earth that includes the survey area (e.g., survey area <NUM>, shown in <FIG>). In graphic <NUM>, different patterns are used to represent different likelihoods of the presence of a gas source. Specifically, in this embodiment, the dark and closely patterned regions correspond to areas that have the highest likelihood of containing a gas source, and the lighter and not-as-closely-patterned a region is, the less likelihood the region has of containing a gas source. For example, the medium darkness and medium-closeness-patterned regions correspond to areas that have a moderately high likelihood of containing a gas source, the light darkness and further spaced-apart-patterned regions correspond to areas that have a moderately low likelihood of containing a gas source, the very light and very spaced-apart-patterned regions correspond to areas that have a low likelihood of containing a gas source, and transparent regions correspond to areas that have almost zero likelihood of containing a gas source.

In other embodiments, graphic <NUM> may include different colors used to represent different likelihoods of the presence of a gas source. For example, in these embodiments, red regions may correspond to areas that have the highest likelihood of containing a gas source, yellow regions may correspond to areas that have a moderately high likelihood of containing a gas source, green regions may correspond to areas that have a moderately low likelihood of containing a gas source, blue regions may correspond to areas that have a low likelihood of containing a gas source, and transparent regions may correspond to areas that have almost zero likelihood of containing a gas source.

<FIG> depicts another graphic <NUM> illustrating a result of applying a smoothing algorithm to the estimated gas source locations (e.g., estimated gas source locations <NUM>, shown in <FIG>, and estimated gas source locations <NUM>, shown in <FIG>) in an exemplary embodiment. Graphic <NUM> may be generated, for example, by visualization computing device <NUM> for display on user device <NUM>, as shown in <FIG>. Whereas graphic <NUM> is depicted as a quasi-three-dimensional (perspective) view, graphic <NUM> is depicted as a two-dimensional (overhead) view. Like graphic <NUM>, a kernel density plot <NUM> in graphic <NUM> uses different patterns to represent different likelihoods of the presence of a gas source (with the pattern, or lack thereof, having the same meanings described above with respect to <FIG>). Also like graphic <NUM>, kernel density plot <NUM> in graphic <NUM> may also use different colors to represent different likelihoods of the presence of a gas source (with the colors, or lack thereof, having the same meanings described above with respect to <FIG>). <FIG> also includes a plurality of graphical elements <NUM> (in this embodiment, dots) representing gas sources superimposed over kernel density plot <NUM> and a satellite image <NUM>. In other embodiments, graphical elements <NUM> may include dots with corresponding text labels, and the text labels may indicate the likelihood of the dot being the gas source and/or label the different gas sources.

In the illustrative embodiment described above, all the estimated gas source locations were equally weighted (e.g., each assigned an amplitude of <NUM> at the x-y coordinate corresponding to that location). In other embodiments, however, the amplitude assigned to each estimated gas source location can vary. For instance, in some embodiments, each estimated gas source location may be assigned a value that more accurately describes the magnitude of the measured gas concentration used to calculate that estimated gas source location. For example, a determined flux (scfh) or the product of measured methane concentration (ppm) multiplied by measured wind speed (m/s) could be used as a weighting factor in some embodiments.

In the illustrative embodiment described above, the same fixed bandwidth (h) was used along both x and y dimensions. In other embodiments, however, different bandwidth values for each dimension and/or a variable bandwidth (h) can be used to improve the visualization.

The choice of bandwidth (h) can significantly affect the accuracy of the resulting distribution, as illustrated by <FIG> depicts a set <NUM> of randomly chosen two-dimensional point density data. <FIG> depicts a resulting kernel density plot <NUM> after application of a KDE algorithm using a bandwidth that is too small, resulting in under-smoothing. <FIG> depicts a resulting kernel density plot <NUM> after application of a KDE algorithm using an optimal bandwidth that properly smooths the point density data. Finally, <FIG> depicts a resulting kernel density plot <NUM> after application of a KDE algorithm using a bandwidth that is too large, resulting in over-smoothing.

In some embodiments, the bandwidth (h) may be defined with respect to a cost function corresponding to an error relevant to the problem, and minimizing that error can yield a more optimal bandwidth. For embodiments using a KDE algorithm, the cost function may be defined as the mean integrated squared error or an equivalent function. For instance, assuming a survey area that contains one or more small (point) sources that disperse gas on a relatively calm day, the radius of the gas plume will expand as it moves away from the source. In the case of a UAV survey, this behavior could be accounted for by using a bandwidth that changes as a function of the altitude of the UAV when the corresponding measurement was taken. In contrast to small (point) sources, gas plumes from extended sources (e.g., ponds) will be expected to expand in radius differently. As such, some embodiments of the smoothing algorithm may utilize an adaptive bandwidth technique that varies the numerical value of h spatially based on expected physical properties of the source.

In still other embodiments, the bandwidth (h) and orientation of the kernel (K) can be determined by properties of the individual emission measurements. For instance, the smoothing function of the algorithm can be increased when multiple gas peaks are detected very near to one another and/or when the local wind variability is large. As another alternative, an angular and distance uncertainty of each emission calculated by the location estimation algorithm could be used as the smoothing kernel. In certain circumstances, this approach may result in a more accurate representation of the source location probability distribution in the smoothed visualization. In other words, emission indications which have a larger uncertainty would be smoothed over a larger area, while emission indications with narrower positional uncertainty would be smoothed over a smaller area.

For example, implementation of the KDE calculation may require that the survey area be divided into a grid (e.g., <NUM> x <NUM>), with each grid square having its own unique GPS coordinates or other numerical designation. These GPS coordinates or other numerical designations correspond to x and y in the formula below. Additionally, all n estimated source locations may have their own unique GPS coordinates or other numerical designations, and these GPS coordinates or other numerical designations correspond to xi and yi in the formula below. The image of the kernel density estimate is then obtained by calculating a summed value, using the formula below, for every location (e.g., every x and y coordinate pair) on the grid: <MAT> With this calculation, every location (e.g., every x and y coordinate pair) has a summed value. The summed value may be converted into a color or pattern, as described above, which forms the relevant KDE image.

Some embodiments of the present disclosure may further utilize techniques for detecting and/or removing potential sampling bias from the data. Surveyors do not necessarily measure gas properties at all locations in an equal manner. Indeed, the number of detections in one area may outnumber the number of detections in another area simply because the analyzer remained in one area longer than the other area. This may artificially increase the point density in that area (a sampling bias), giving the impression that the probability associated with finding an emission location is incorrectly higher in one area than another. One technique that may be used to detect sampling bias is k-fold validation, in which the data is randomly separated into multiple subgroups, the smoothing algorithm is applied to each subgroup, and the error associated with the difference between the results from each subgroup provides an indication of the extent of sampling bias. In some embodiments, sampling bias can be removed by calculating the likelihood that a kernel location was over-sampled and normalizing for the calculated sample density. For example, the expected origination path measurements looking upwind can be calculated (using either the local wind properties or a wind field model), and the amplitude of the kernel can then be normalized by the number of path measurements that are downwind. Since few or no wind paths will pass exactly through the emission point, nearby wind paths can be included with a weight that decreases with distance from the emission point.

While the present disclosure has primarily referred to the use of a KDE algorithm, it is contemplated that other smoothing algorithms can be applied to the estimated gas source location data. In some embodiments, the estimated gas source location data may first be binned into regular (or semi-regular) bins and then smoothed in the spatial dimensions. Additionally, it is contemplated that the KDE (or other) smoothing algorithm could be applied to the detection locations (i.e., points where the measured gas concentration exceeded a threshold) and/or to the spatial uncertainty ellipses (see <FIG>) in a similar manner to the estimated gas source locations.

An example technical effect of the system and methods described herein includes one or more of (a) receiving and analyzing gas leak sensor data; (b) optimizing a smoothing algorithm for visualizing gas leak sensor data; (c) providing a visualization of gas leak sensor data that is easily read; and (d) allowing users to quickly and efficiently locate and repair a gas leak source.

Although specific features of various embodiments of the disclosure may be shown in some drawings and not in others, this is for convenience only. In accordance with the principles of the disclosure, any feature of a drawing may be referenced and/or claimed in combination with any feature of any other drawing.

Claim 1:
A visualization system for visualizing gas leak detection data including a processor (<NUM>) in communication with at least one memory device (<NUM>), wherein the processor is configured to:
receive (<NUM>), from one or more sensors (<NUM>), gas measurement data for a plurality of measurement locations within a survey area (<NUM>), wherein the gas measurement data includes gas concentration data;
receive (<NUM>), from the at least one memory device (<NUM>), a geographic representation of the survey area (<NUM>);
determine (<NUM>), based upon the gas measurement data, estimated gas source locations (<NUM>, <NUM>, <NUM>) of the plurality of measurement locations, wherein the estimated gas source locations include one or more measurement locations of the plurality of measurement locations where the gas concentration data is at or above a threshold; and
utilize (<NUM>), by a smoothing algorithm, point density data of the estimated gas source locations (<NUM>, <NUM>, <NUM>) to create a multi-dimensional density plot (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>).