1. A method for evaluating geological samples, comprising: acquiring spectral data using the gamma ray detector; processing the spectral data using principal component analysis to find the principle component coefficients; and transmitting the processed spectral data to an inverse filter to determine the physical properties and mineral composition of the sample.

2. The method of claim 1, further comprising acquiring high-resolution images of the sample using at least one selected from a scanning electron microscope or a transmission electron microscope.

3. The method of claim 2, further comprising analyzing image using the image analysis algorithms that perform at least one activity selected from image segmentation, feature extraction, and pattern recognition algorithms.

4. The method of claim 1, wherein processing of spectral data using machine learning algorithms comprise artificial neural networks, support vector machines, or decision trees.

5. The method of claim 1, further comprising using a calibration database to find coefficients for the main components and constructing the inverse filter from the coefficients.

6. The method of claim 1, further comprising generating a report of the physical properties and mineral composition of the sample for geological analysis.

7. The method of claim 1, further comprising selecting specific areas of the sample for analysis based on the image features obtained from the acquired images.

8. The method of claim 1, further comprising classifying the sample into different geological categories based on the physical properties and mineral composition determined by the machine learning algorithms.

9. A system for evaluating geological samples, comprising: a gamma ray detector for acquiring spectral data; and a computer system for processing the spectral data using principal component analysis to find the principle component coefficients and transmitting the processed spectral data to an inverse filter to determine the physical properties and mineral composition of the sample.

10. The system of claim 9, further comprising a report generator for generating a report of the physical properties and mineral composition of the sample for geological analysis. TECHQNIQUE FOR EVALATION OF GEOLOGICAL SAMPLE Abstract A method for analysing geological samples may include the step of gathering spectral data by employing the gamma ray detector. In other embodiments, processing the spectral data by using principal component analysis in order to determine the principal component coefficients is also included. Transmission of the processed spectral data to an inverse filter may also be included in certain embodiments. This allows one to identify the physical qualities of the sample as well as its mineral composition. Fig. 1 , C , Claims:Claims :

1. A method for evaluating geological samples, comprising: acquiring spectral data using the gamma ray detector; processing the spectral data using principal component analysis to find the principle component coefficients; and transmitting the processed spectral data to an inverse filter to determine the physical properties and mineral composition of the sample.

2. The method of claim 1, further comprising acquiring high-resolution images of the sample using at least one selected from a scanning electron microscope or a transmission electron microscope.

3. The method of claim 2, further comprising analyzing image using the image analysis algorithms that perform at least one activity selected from image segmentation, feature extraction, and pattern recognition algorithms.

4. The method of claim 1, wherein processing of spectral data using machine learning algorithms comprise artificial neural networks, support vector machines, or decision trees.

5. The method of claim 1, further comprising using a calibration database to find coefficients for the main components and constructing the inverse filter from the coefficients.

6. The method of claim 1, further comprising generating a report of the physical properties and mineral composition of the sample for geological analysis.

7. The method of claim 1, further comprising selecting specific areas of the sample for analysis based on the image features obtained from the acquired images.

8. The method of claim 1, further comprising classifying the sample into different geological categories based on the physical properties and mineral composition determined by the machine learning algorithms.

9. A system for evaluating geological samples, comprising: a gamma ray detector for acquiring spectral data; and a computer system for processing the spectral data using principal component analysis to find the principle component coefficients and transmitting the processed spectral data to an inverse filter to determine the physical properties and mineral composition of the sample.

10. The system of claim 9, further comprising a report generator for generating a report of the physical properties and mineral composition of the sample for geological analysis.

Description:TECHQNIQUE FOR EVALATION OF GEOLOGICAL SAMPLE
Field of the Invention
[0001] The present invention relates generally geological sample analysis. More particularly, the invention relates to methods and apparatus for determining the quantitative mineralogical composition of a geological sample.
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 evaluation is a critical component of the exploration and production of mineral resources, as it provides valuable information about the properties of the rock formations and the potential for economic extraction. Traditional methods of geological sample evaluation, such as visual inspection and laboratory analysis, are time-consuming, expensive, and often provide limited information about the subsurface geology.
[0004] To address these challenges, a new technique for evaluating geological samples has been developed in patent literature. Few exemplary documents are discussed below.
[0005] The WO2016205894A1 (By: DEEP EXPLOR TECHNOLOGY CRC) - A computer-implemented method of determining the composition of a geological sample comprising at least one mineral species via a controller, wherein an X-ray beam is diffracted by the geological sample and captured by a detector to produce a sample X-ray diffraction pattern, the controller comprising a processor and a memory storing program instructions which when executed by the processor causes implementation of the following steps: (a) comparing the sample X-ray diffraction pattern to a plurality of X-ray diffraction patterns each associated with at least one mineral species provided in a customised reference dataset of mineral species; (b) rejecting any obvious unmatched mineral species from the customised reference dataset to leave a remainder of potentially matched mineral species; (c) performing a optimisation analysis of the sample X-ray diffraction pattern against the potentially matched mineral species to calculate the percentage of each mineral species in the sample; (d) rejecting any mineral species calculated as having a percentage below a predetermined threshold; (e) repeating steps (c) and (d) until no further mineral species is rejected; and (f) generating a result comprising the calculated percentages of each mineral species in the geological sample.
[0006] The AU2018201960B2 (By: MALVERN PANALYTICAL) - A system for analyzing an unknown geological sample, the system comprising: at least two analytical subsystems including a near-infrared spectral-analysis subsystem and at least one other analytical subsystem, each of the at least two analytical subsystems providing different information about the geological sample; a data collection component to collect and combine the different information from each analytical subsystem to create combined analytical information; a chemometric calibration database that includes records that relate geological attributes to data previously generated with at least two analytical systems that are the same types of systems as the at least two analytical subsystems; and a prediction engine that applies the records in the chemometric calibration database to the combined analytical information to identify specific geological attributes in the unknown geological sample.
[0007] DE112013004743T5 (By: MALVERN PANALYTICAL) - Systems and methods for analyzing an unknown sample are disclosed. The system includes at least one subsystem to obtain molecular information about the sample and at least one other subsystem to obtain elemental information about the sample. The system also includes a data collection component to collect and combine the information from the subsystems to create combined analytical information and a multivariate model that relates known attributes to information previously generated with at least two analytical systems that are the same types of systems as the at least two analytical subsystems. A prediction engine applies the multivariate model to the combined analytical information to produce predictions of attributes in the unknown sample.
[0008] The WO2020146082A1 (By: HALLIBURTON ENERGY SERVICES) - Systems and methods for determining properties of subterranean formations surrounding a wellbore are provided. An example method can include receiving an image of a formation sample; partitioning the image into a plurality of patches; detecting, via a semantic extraction processor, textures captured in the plurality of patches; associating the textures to a location of the image of the formation sample; reducing a dimension of representation of the textures to obtain one or more vectors, the one or more vectors being based on the textures; and providing a plurality of curves based on the one or more vectors.
[0009] These known techniques are often associated with several limitation such as lower resolution, cost, interference from other source etc. Thus, there is remain need for tech advancement in this domain.
[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] The present invention relates generally geological sample analysis. More particularly, the invention relates to methods and apparatus for determining the quantitative mineralogical composition of a geological sample.
[00014] Embodiments of the present disclosure may include a method for evaluating geological samples, including acquiring spectral data using the gamma ray detector. Embodiments may also include processing the spectral data using principal component analysis to find the principle component coefficients. Embodiments may also include transmitting the processed spectral data to an inverse filter to determine the physical properties and mineral composition of the sample.
[00015] In some embodiments, the method may include acquiring high-resolution images of the sample using at least one selected from a scanning electron microscope or a transmission electron microscope. In some embodiments, the method may include analyzing image using the image analysis algorithms that perform at least one activity selected from image segmentation, feature extraction, and pattern recognition algorithms.
[00016] Embodiments may also include processing of spectral data using machine learning algorithms, which may include artificial neural networks, support vector machines, or decision trees. In some embodiments, the method may include using a calibration database to find coefficients for the main components and constructing the inverse filter from the coefficients.
[00017] In some embodiments, the method may include generating a report of the physical properties and mineral composition of the sample for geological analysis. In some embodiments, the method may include selecting specific areas of the sample for analysis based on the image features obtained from the acquired images. In some embodiments, the method may include classifying the sample into different geological categories based on the physical properties and mineral composition determined by the machine learning algorithms.
[00018] Embodiments of the present disclosure may also include a system for evaluating geological samples, including gamma ray detector for acquiring spectral data. Embodiments may also include a computer system for processing the spectral data using principal component analysis to find the principle component coefficients and transmitting the processed spectral data to an inverse filter to determine the physical properties and mineral composition of the sample. In some embodiments, the system may include a report generator for generating a report of the physical properties and mineral composition of the sample for geological analysis.

Brief Description of the Drawings
[00019] 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:
[00020] FIG. 1 is a flowchart illustrating a method for evaluating geological samples for evaluating geological samples, according to some embodiments of the present disclosure.
[00021] FIG. 2 is a modified flowchart further illustrating the method for evaluating geological samples (from FIG. 1) for evaluating geological samples, according to some embodiments of the present disclosure.
[00022] FIG. 3 is a block diagram illustrating a system for evaluating geological samples, according to some embodiments of the present disclosure.
Detailed Description
[00023] 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.
[00024] 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.
[00025] 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.
[00026] 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.
[00027] The present invention relates generally geological sample analysis. More particularly, the invention relates to methods and apparatus for determining the quantitative mineralogical composition of a geological sample.
[00028] FIG. 1 is a flowchart that describes a method for evaluating geological samples for evaluating geological samples, according to some embodiments of the present disclosure. In some embodiments, at 110, the method may include acquiring spectral data using the gamma ray detector. At 120, the method may include processing the spectral data using principal component analysis to find principle component coefficients. At 130, the method may include transmitting the processed spectral data to an inverse filter to determine the physical properties and mineral composition of the sample.
[00029] In some embodiments, processing of spectral data using machine learning algorithms may comprise artificial neural networks, support vector machines, or decision trees. In some embodiments, the method may include using a calibration database to find coefficients for the main components and constructing the inverse filter from the coefficients. In some embodiments, the method may include generating a report of the physical properties and mineral composition of the sample for geological analysis. In some embodiments, the method may include selecting specific areas of the sample for analysis based on the image features obtained from the acquired images. In some embodiments, the method may include classifying the sample into different geological categories based on the physical properties and mineral composition determined by the machine learning algorithms.
[00030] FIG. 2 is a modified flowchart that further describes the method for evaluating geological samples (from FIG. 1) for evaluating geological samples, according to some embodiments of the present disclosure. In some embodiments, at 210, the method may include acquiring high-resolution images of the sample using at least one selected from a scanning electron microscope or a transmission electron microscope. In some embodiments, at 220, the method may include analyzing image using the image analysis algorithms that perform at least one activity selected from image segmentation, feature extraction, and pattern recognition algorithms.
[00031] FIG. 3 is a block diagram that describes a system 300 for evaluating geological samples, according to some embodiments of the present disclosure. In some embodiments, the system 300 may include gamma ray detector 310 for acquiring spectral data and a computer system 320 for processing the spectral data using principal component analysis to find the principle component coefficients and transmitting the processed spectral data to an inverse filter to determine the physical properties and mineral composition of the sample. In some embodiments, the system 300 may include a report generator for generating a report of the physical properties and mineral composition of the sample for geological analysis.
[00032] A method for analysing geological samples may be included in certain embodiments of the present disclosure. This method may comprise the step of gathering spectral data by employing the gamma ray detector. In other embodiments, processing the spectral data by using principal component analysis in order to determine the principal component coefficients is also included. Transmission of the processed spectral data to an inverse filter may also be included in certain embodiments. This allows one to identify the physical qualities of the sample as well as its mineral composition.
[00033] Acquiring high-resolution pictures of the sample using at least one device chosen from among a scanning electron microscope and a transmission electron microscope is an optional step that may be included in some implementations of the approach. Analyzing images using image analysis algorithms that execute at least one activity chosen from image segmentation, feature extraction, and pattern recognition algorithms may be included in certain implementations of the technique.
[00034] Processing of spectral data using machine learning methods such as artificial neural networks, support vector machines, or decision trees may also be included in certain embodiments. The technique may, in some implementations, include accessing a calibration database in order to locate coefficients for the primary components and generating the inverse filter based on the coefficients found in the database.
[00035] In some implementations of the approach, one of its steps may include the generation of a report detailing the physical characteristics and mineral make-up of the sample in preparation for geological investigation. The technique may, in some implementations, entail choosing certain regions of the sample for analysis based on the image characteristics derived from the captured photos. The machine learning algorithms may, in certain implementations of the technology, categorise the sample into a variety of geological groups. These classifications are based on the physical qualities of the sample as well as the mineral composition.
[00036] A system for analysing geological samples may also be included in certain embodiments of the present disclosure. This system may contain a gamma ray detector for the purpose of obtaining spectral data. Some embodiments may also include a computer system for processing the spectral data using principal component analysis to find the principle component coefficients and transmitting the processed spectral data to an inverse filter to determine the physical properties and mineral composition of the sample. Other embodiments may not include a computer system for processing the spectral data. A report generator may be included in the system in some embodiments. This report generator's purpose is to provide a report on the physical attributes and mineral composition of the sample for the purpose of geological analysis.
[00037] 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
[00038] 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.
[00039] 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.
[00040] 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).
[00041] 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.
[00042] 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.
[00043] 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.
[00044] 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.
[00045] 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.
[00046] 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 method for evaluating geological samples, comprising:
acquiring spectral data using the gamma ray detector; processing the spectral data using principal component analysis to find the principle component coefficients; and
transmitting the processed spectral data to an inverse filter to determine the physical properties and mineral composition of the sample.

2. The method of claim 1, further comprising acquiring high-resolution images of the sample using at least one selected from a scanning electron microscope or a transmission electron microscope.

3. The method of claim 2, further comprising analyzing image using the image analysis algorithms that perform at least one activity selected from image segmentation, feature extraction, and pattern recognition algorithms.

4. The method of claim 1, wherein processing of spectral data using machine learning algorithms comprise artificial neural networks, support vector machines, or decision trees.

5. The method of claim 1, further comprising using a calibration database to find coefficients for the main components and constructing the inverse filter from the coefficients.

6. The method of claim 1, further comprising generating a report of the physical properties and mineral composition of the sample for geological analysis.

7. The method of claim 1, further comprising selecting specific areas of the sample for analysis based on the image features obtained from the acquired images.

8. The method of claim 1, further comprising classifying the sample into different geological categories based on the physical properties and mineral composition determined by the machine learning algorithms.

9. A system for evaluating geological samples, comprising:
a gamma ray detector for acquiring spectral data; and
a computer system for processing the spectral data using principal component analysis to find the principle component coefficients and transmitting the processed spectral data to an inverse filter to determine the physical properties and mineral composition of the sample.

10. The system of claim 9, further comprising a report generator for generating a report of the physical properties and mineral composition of the sample for geological analysis.

TECHQNIQUE FOR EVALATION OF GEOLOGICAL SAMPLE
Abstract
A method for analysing geological samples may include the step of gathering spectral data by employing the gamma ray detector. In other embodiments, processing the spectral data by using principal component analysis in order to determine the principal component coefficients is also included. Transmission of the processed spectral data to an inverse filter may also be included in certain embodiments. This allows one to identify the physical qualities of the sample as well as its mineral composition.

Fig. 1 , C , Claims:Claims
I/We Claim:
1. A method for evaluating geological samples, comprising:
acquiring spectral data using the gamma ray detector; processing the spectral data using principal component analysis to find the principle component coefficients; and
transmitting the processed spectral data to an inverse filter to determine the physical properties and mineral composition of the sample.

2. The method of claim 1, further comprising acquiring high-resolution images of the sample using at least one selected from a scanning electron microscope or a transmission electron microscope.

3. The method of claim 2, further comprising analyzing image using the image analysis algorithms that perform at least one activity selected from image segmentation, feature extraction, and pattern recognition algorithms.

4. The method of claim 1, wherein processing of spectral data using machine learning algorithms comprise artificial neural networks, support vector machines, or decision trees.

5. The method of claim 1, further comprising using a calibration database to find coefficients for the main components and constructing the inverse filter from the coefficients.

6. The method of claim 1, further comprising generating a report of the physical properties and mineral composition of the sample for geological analysis.

7. The method of claim 1, further comprising selecting specific areas of the sample for analysis based on the image features obtained from the acquired images.

8. The method of claim 1, further comprising classifying the sample into different geological categories based on the physical properties and mineral composition determined by the machine learning algorithms.

9. A system for evaluating geological samples, comprising:
a gamma ray detector for acquiring spectral data; and
a computer system for processing the spectral data using principal component analysis to find the principle component coefficients and transmitting the processed spectral data to an inverse filter to determine the physical properties and mineral composition of the sample.

10. The system of claim 9, further comprising a report generator for generating a report of the physical properties and mineral composition of the sample for geological analysis.