1. A system for hyperspectral imaging methodology for geological sample analysis, comprising: a hyperspectral imaging device configured to capture a hyperspectral image of a geological sample; an artificial intelligence module configured to process the hyperspectral image and generate a mineral map of the geological sample; and a computer system configured to receive and store the mineral map and generate a report based on the mineral map.

2. The system of claim 1, wherein the hyperspectral imaging device comprises a spectrometer or an imaging spectrometer.

3. The system of claim 1, wherein the artificial intelligence module comprises a machine learning algorithm or a neural network.

4. The system of claim 1, further comprising a sample holder for holding the geological sample during imaging.

5. A method for hyperspectral imaging methodology for geological sample analysis and artificial intelligence technology, comprising: placing a geological sample in a sample holder; capturing a hyperspectral image of the geological sample using a hyperspectral imaging device; processing the hyperspectral image using an artificial intelligence module to generate a mineral map of the geological sample; and generating a report based on the mineral map using a computer system.

6. The method of claim 6, further comprising calibrating the hyperspectral imaging device and the artificial intelligence module using a calibration sample of known mineral composition.

7. The method of claim 6, further comprising analyzing the mineral map to determine the mineral composition of the geological sample.

8. The method of claim 6, further comprising generating a 3D image of the geological sample based on the mineral map.

9. The method of claim 6, further comprising adjusting drilling parameters based on the mineral map to optimize drilling efficiency and reduce drilling hazards.

10. The method of claim 6, wherein the artificial intelligence module comprises a deep learning neural network configured to identify patterns in the hyperspectral image and classify the mineral composition of the geological sample. HYPERSPECTRAL IMAGING METHODOLOGY FOR GEOLOGICAL SAMPLE ANALYSIS Abstract A system for hyperspectral imaging technique for geological sample analysis may be included in certain embodiments of the present disclosure. This system may contain a hyperspectral imaging device that is able to capture a hyperspectral picture of a geological sample. In certain implementations, there is also a possibility of including an artificial intelligence module that is able to analyse the hyperspectral picture and produce a mineral map of the geological sample. A computer system that is able to receive and store the mineral map as well as create a report based on the mineral map may also be included in certain embodiments of the invention. , C , Claims:Claims :

1. A system for hyperspectral imaging methodology for geological sample analysis, comprising: a hyperspectral imaging device configured to capture a hyperspectral image of a geological sample; an artificial intelligence module configured to process the hyperspectral image and generate a mineral map of the geological sample; and a computer system configured to receive and store the mineral map and generate a report based on the mineral map.

2. The system of claim 1, wherein the hyperspectral imaging device comprises a spectrometer or an imaging spectrometer.

3. The system of claim 1, wherein the artificial intelligence module comprises a machine learning algorithm or a neural network.

4. The system of claim 1, further comprising a sample holder for holding the geological sample during imaging.

5. A method for hyperspectral imaging methodology for geological sample analysis and artificial intelligence technology, comprising: placing a geological sample in a sample holder; capturing a hyperspectral image of the geological sample using a hyperspectral imaging device; processing the hyperspectral image using an artificial intelligence module to generate a mineral map of the geological sample; and generating a report based on the mineral map using a computer system.

6. The method of claim 6, further comprising calibrating the hyperspectral imaging device and the artificial intelligence module using a calibration sample of known mineral composition.

7. The method of claim 6, further comprising analyzing the mineral map to determine the mineral composition of the geological sample.

8. The method of claim 6, further comprising generating a 3D image of the geological sample based on the mineral map.

9. The method of claim 6, further comprising adjusting drilling parameters based on the mineral map to optimize drilling efficiency and reduce drilling hazards.

10. The method of claim 6, wherein the artificial intelligence module comprises a deep learning neural network configured to identify patterns in the hyperspectral image and classify the mineral composition of the geological sample.

Description:HYPERSPECTRAL IMAGING METHODOLOGY FOR GEOLOGICAL SAMPLE ANALYSIS
Field of the Invention
[0001] This invention pertains to methods of evaluating the contents of geological sample, including, for example, mineral content, volatile substances such as petroleum-related hydrocarbons.

Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Geological sample characterization is a critical area of research that is essential for a wide range of applications, including mineral exploration, environmental monitoring, and geotechnical engineering.
[0004] Plethora of techniques (e.g., electron microscopy, gamma ray, X-ray etc.) are discussed in patent literature for geological sample evaluation. Few of them are discussed below.
[0005] The US11280186B2 (By: SMITH MICHAEL) - Methods for determining the hardness and/or ductility of a material by compression of the material are provided as a first aspect of the invention. Typically, compression is performed on multiple sides of a geologic material sample in a contemporaneous manner. Devices and systems for performing such methods also are provided. These methods, devices, and systems can be combined with additional methods, devices, and systems of the invention that provide for the analysis of compounds contained in such samples, which can indicate the presence of valuable materials, such as petroleum-associated hydrocarbons. Alternatively, these additional methods, devices, and systems can also stand independently of the methods, devices, and systems for analyzing ductility and/or hardness of materials.
[0006] The AU2013308908B2 (By: SAUDI ARABIAN OIL) - Methods are provided for utilizing the results of compositional modeling analysis to obtain accurate total organic carbon values without the need for an oxidation step or lengthy sample preparation, and also to calculate the organic carbon value attributable to contaminants, such as drilling additives.
[0007] The WO202176529A1 (By: SMITH MICHAEL) - The invention described here provides new methods of analyzing materials, e.g., geologic materials, to identify wettability characteristics of such materials. Methods of the invention comprise analyzing the amount of easily extracted water obtained from samples of a material, such as a geologic area, analyzing release resistant water obtained from such samples or co located samples, and/or optionally calculating or analyzing the combined water in or obtained from such samples, and utilizing such values alone or in comparison to one another to assess the wettability characteristics of the material.
[0008] The CA3046903A1 (By: SOLETANCHE FREYSSINET) - A system having a terrestrial scanning device, a topographic benchmark secured permanently to a support, including a code that is readable automatically at a distance of at least 3 m by the scanning device, this code ss providing information as to the position of the benchmark in a given frame of reference and/or having an identifier listed in a database in which the position of the benchmark in said frame of reference is also recorded.
[0009] However, known techniques are time-consuming, expensive, and often provide limited information about the subsurface geology. Thus, there is need to develop new and improved methods for characterizing geological samples that are more accurate, efficient, and cost-effective than existing techniques.
[00010] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.

Summary
[00011] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00012] The following paragraphs provide additional support for the claims of the subject application.
[00013] This invention pertains to methods of evaluating the contents of geological sample, including, for example, mineral content, volatile substances such as petroleum-related hydrocarbons.
[00014] Embodiments of the present disclosure may include a system for hyperspectral imaging methodology for geological sample analysis, including a hyperspectral imaging device configured to capture a hyperspectral image of a geological sample. Embodiments may also include an artificial intelligence module configured to process the hyperspectral image and generate a mineral map of the geological sample. Embodiments may also include a computer system configured to receive and store the mineral map and generate a report based on the mineral map.
[00015] In some embodiments, the hyperspectral imaging device may include a spectrometer or an imaging spectrometer. In some embodiments, the artificial intelligence module may include a machine learning algorithm or a neural network. In some embodiments, the system may include a sample holder for holding the geological sample during imaging.
[00016] Embodiments of the present disclosure may also include a method for hyperspectral imaging methodology for geological sample analysis and artificial intelligence technology, including placing a geological sample in a sample holder. Embodiments may also include capturing a hyperspectral image of the geological sample using a hyperspectral imaging device. Embodiments may also include processing the hyperspectral image using an artificial intelligence module to generate a mineral map of the geological sample. Embodiments may also include generating a report based on the mineral map using a computer system.

Brief Description of the Drawings
[00017] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00018] FIG. 1 is a block diagram illustrating a system for hyperspectral imaging methodology for geological sample analysis, according to some embodiments of the present disclosure.
[00019] FIG. 2 is a modified block diagram further illustrating the system (from FIG. 1) for hyperspectral imaging methodology for geological sample analysis, according to some embodiments of the present disclosure.
[00020] FIG. 3 is a flowchart illustrating a method for hyperspectral imaging methodology for geological sample analysis, according to some embodiments of the present disclosure.
Detailed Description
[00021] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00022] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00023] Following are more detailed descriptions of various concepts related to, and implementations of, methods, apparatuses, and systems of present disclosure. The various concepts introduced above and discussed in greater detail below may be implemented in any of numerous ways.
[00024] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00025] This invention pertains to methods of evaluating the contents of geological sample, including, for example, mineral content, volatile substances such as petroleum-related hydrocarbons.
[00026] FIG. 1 is a block diagram that describes a system 100 for hyperspectral imaging methodology for geological sample analysis, according to some embodiments of the present disclosure. In some embodiments, the system 100 may include a hyperspectral imaging device 110 configured to capture a hyperspectral image of a geological sample, an artificial intelligence module 120 configured to process the hyperspectral image and generate a mineral map of the geological sample, and a computer system 130 configured to receive and store the mineral map and generate a report based on the mineral map. In some embodiments, the artificial intelligence module 120 may include a machine learning algorithm or a neural network. In some embodiments, the system 100 may include a sample holder for holding the geological sample during imaging.
[00027] FIG. 2 is a modified block diagram that further describes the system 100 from FIG. 1 for hyperspectral imaging methodology for geological sample analysis, according to some embodiments of the present disclosure. In some embodiments, the hyperspectral imaging device 110 may include a spectrometer 222 and an imaging spectrometer 224, which can be used to separate and measure spectral components of a physical phenomenon.
[00028] FIG. 3 is a flowchart that describes a method for hyperspectral imaging methodology for geological sample analysis, according to some embodiments of the present disclosure. In some embodiments, at 310, the method may include placing a geological sample in a sample holder. At 320, the method may include capturing a hyperspectral image of the geological sample using a hyperspectral imaging device. At 330, the method may include processing the hyperspectral image using an artificial intelligence module 120 to generate a mineral map of the geological sample. At 340, the method may include generating a report based on the mineral map using the computer system 130.
[00029] A system for hyperspectral imaging technique for geological sample analysis may be included in certain embodiments of the present disclosure. This system may contain a hyperspectral imaging device 110 that is able to capture a hyperspectral picture of a geological sample. In certain implementations, there is also a possibility of including an artificial intelligence module 120 that is able to analyse the hyperspectral picture and produce a mineral map of the geological sample. The computer system 130 that is able to receive and store the mineral map as well as create a report based on the mineral map may also be included in certain embodiments of the invention.
[00030] The spectrometer 222 or the imaging spectrometer 224 may be a component of the hyperspectral imaging device 110 in some implementations. In certain implementations, the artificial intelligence module 120 might take the form of a neural network or a machine learning algorithm. Depending on the specific implementation, the system could come with a sample holder that keeps the geological sample in place while it's being imaged.
[00031] The current disclosure may additionally comprise a method for using artificial intelligence technology with hyperspectral imaging methods for analysing geological samples. This method may include inserting a geological sample in a sample holder. In other embodiments, it is also possible to take a hyperspectral picture of the geological sample using a device that is designed specifically for that purpose. In certain embodiments, the hyperspectral picture is processed using an artificial intelligence module 120 in order to build a mineral map of the geological sample. In some embodiments, one of the steps includes utilising a computer system to produce a report that is based on the mineral map.
[00032] Variations of those preferred embodiments may become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventors expect skilled artisans to employ such variations as appropriate, and the inventors intend for the invention to be practiced otherwise than as specifically described herein. Accordingly, this invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the invention unless otherwise indicated herein or otherwise clearly contradicted by context
[00033] As used herein, the term “wireless communication network” or “network interface” refers to a network following any suitable wireless communication standards, such as LTE-Advanced (LTE-A), LTE, Wideband Code Division Multiple Access (WCDMA), High-Speed Packet Access (HSPA), and so on. Furthermore, the communications between network devices in the wireless communication network may be performed according to any suitable generation communication protocols, including, but not limited to, the first generation (1G), the second generation (2G), 2.5G, 2.75G, the third generation (3G), the fourth generation (4G), 4.5G, the fifth generation (5G) communication protocols, and/or any other protocols either currently known or to be developed in the future.
[00034] As used herein, the term “network device” refers to a device in a wireless communication network via which a terminal device accesses the network and receives services therefrom. The network device may refer to a base station (BS) or an access point (AP), for example, a node B (NodeB or NB), an evolved NodeB (eNodeB or eNB), a Remote Radio Unit (RRU), a radio header (RH), a remote radio head (RRH), a relay, a low power node such as a femto, a pico, and so forth, depending on the applied terminology and technology. The “network device” or “terminal device” or “computing device” may represent any suitable device (or group of devices) capable, configured, arranged, and/or operable to enable and/or provide a terminal device access to the wireless communication network or to provide some service to a terminal device that has accessed the wireless communication network. The terminal device may include, but not limited to, a mobile phone, a cellular phone, a smart phone, a tablet, a wearable device, a personal digital assistant (PDA), portable computers, image capture terminal devices such as digital cameras, gaming terminal devices, music storage and playback appliances, wearable terminal devices, vehicle-mounted wireless terminal devices and the like. In the following description, the terms “terminal device”, “terminal”, “user equipment”, “computing device”, “network device” and “UE” may be used interchangeably.
[00035] Processing device may be provided by one or more processors such as a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00036] In addition, the present disclosure may also provide a memory containing the computer program as mentioned above, which includes machine-readable media and machine-readable transmission media. The machine-readable media may also be called computer-readable media, and may include machine-readable storage media, for example, magnetic disks, magnetic tape, optical disks, phase change memory, or an electronic memory terminal device like a random access memory (RAM), read only memory (ROM), flash memory devices, CD-ROM, DVD, Blue-ray disc and the like. The machine-readable transmission media may also be called a carrier, and may include, for example, electrical, optical, radio, acoustical or other form of propagated signals—such as carrier waves, infrared signals, and the like.
[00037] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
[00038] All references to “a/an/the element, apparatus, component, means, step, etc.” are to be interpreted as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated. The discussion above and below in respect of any of the aspects of the present disclosure is also in applicable parts relevant to any other aspect of the present disclosure.
[00039] The wordings such as “include”, “including”, “comprise” and “comprising” do not exclude elements or steps which are present but not listed in the description and the claims.
[00040] It also shall be noted that as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. This invention can be achieved by means of hardware including several different elements or by means of a suitably programmed computer. In the unit claims that list several means, several ones among these means can be specifically embodied in the same hardware item. The use of such words as first, second, third does not represent any order, which can be simply explained as names.
[00041] Various techniques may be described herein in the general context of software, hardware elements, or program modules. Generally, such modules include routines, programs, objects, elements, components, data structures, and so forth that perform particular tasks or implement particular abstract data types. The terms “module,” “functionality,” and “component” as used herein generally represent software, firmware, hardware, or a combination thereof. The features of the techniques described herein are platform-independent, meaning that the techniques may be implemented on a variety of commercial computing platforms having a variety of processors
 

Claims
I/We Claim:
1. A system for hyperspectral imaging methodology for geological sample analysis, comprising:
a hyperspectral imaging device configured to capture a hyperspectral image of a geological sample;
an artificial intelligence module configured to process the hyperspectral image and generate a mineral map of the geological sample; and
a computer system configured to receive and store the mineral map and generate a report based on the mineral map.
2. The system of claim 1, wherein the hyperspectral imaging device comprises a spectrometer or an imaging spectrometer.
3. The system of claim 1, wherein the artificial intelligence module comprises a machine learning algorithm or a neural network.
4. The system of claim 1, further comprising a sample holder for holding the geological sample during imaging.
5. A method for hyperspectral imaging methodology for geological sample analysis and artificial intelligence technology, comprising:
placing a geological sample in a sample holder;
capturing a hyperspectral image of the geological sample using a hyperspectral imaging device;
processing the hyperspectral image using an artificial intelligence module to generate a mineral map of the geological sample; and
generating a report based on the mineral map using a computer system.
6. The method of claim 6, further comprising calibrating the hyperspectral imaging device and the artificial intelligence module using a calibration sample of known mineral composition.
7. The method of claim 6, further comprising analyzing the mineral map to determine the mineral composition of the geological sample.
8. The method of claim 6, further comprising generating a 3D image of the geological sample based on the mineral map.
9. The method of claim 6, further comprising adjusting drilling parameters based on the mineral map to optimize drilling efficiency and reduce drilling hazards.
10. The method of claim 6, wherein the artificial intelligence module comprises a deep learning neural network configured to identify patterns in the hyperspectral image and classify the mineral composition of the geological sample.

HYPERSPECTRAL IMAGING METHODOLOGY FOR GEOLOGICAL SAMPLE ANALYSIS
Abstract
A system for hyperspectral imaging technique for geological sample analysis may be included in certain embodiments of the present disclosure. This system may contain a hyperspectral imaging device that is able to capture a hyperspectral picture of a geological sample. In certain implementations, there is also a possibility of including an artificial intelligence module that is able to analyse the hyperspectral picture and produce a mineral map of the geological sample. A computer system that is able to receive and store the mineral map as well as create a report based on the mineral map may also be included in certain embodiments of the invention. , C , Claims:Claims
I/We Claim:
1. A system for hyperspectral imaging methodology for geological sample analysis, comprising:
a hyperspectral imaging device configured to capture a hyperspectral image of a geological sample;
an artificial intelligence module configured to process the hyperspectral image and generate a mineral map of the geological sample; and
a computer system configured to receive and store the mineral map and generate a report based on the mineral map.
2. The system of claim 1, wherein the hyperspectral imaging device comprises a spectrometer or an imaging spectrometer.
3. The system of claim 1, wherein the artificial intelligence module comprises a machine learning algorithm or a neural network.
4. The system of claim 1, further comprising a sample holder for holding the geological sample during imaging.
5. A method for hyperspectral imaging methodology for geological sample analysis and artificial intelligence technology, comprising:
placing a geological sample in a sample holder;
capturing a hyperspectral image of the geological sample using a hyperspectral imaging device;
processing the hyperspectral image using an artificial intelligence module to generate a mineral map of the geological sample; and
generating a report based on the mineral map using a computer system.
6. The method of claim 6, further comprising calibrating the hyperspectral imaging device and the artificial intelligence module using a calibration sample of known mineral composition.
7. The method of claim 6, further comprising analyzing the mineral map to determine the mineral composition of the geological sample.
8. The method of claim 6, further comprising generating a 3D image of the geological sample based on the mineral map.
9. The method of claim 6, further comprising adjusting drilling parameters based on the mineral map to optimize drilling efficiency and reduce drilling hazards.
10. The method of claim 6, wherein the artificial intelligence module comprises a deep learning neural network configured to identify patterns in the hyperspectral image and classify the mineral composition of the geological sample.