A method, apparatus and storage medium for automatic processing of core samples

Abstract

The application discloses a kind of core sample automation processing method, equipment and storage medium.Method includes the following steps: receiving initial core sample, infrared thermal imaging is carried out to initial core sample;Initial core sample is carried out preliminary cutting, and segmented core is obtained;Segmented core is carried out preliminary measurement, and segmented core data is obtained;Three-dimensional model of core sample is constructed on data server;Segmented core is carried out axial cutting, and half profile core is obtained;Core color measurement and spectrum measurement are carried out to half profile core;According to thermal imaging data, segmented core data and core profile data, core report is generated in data server.The application can automatically collect core one hand information, retain drilling core initial data, reduce the degree of manual participation;Meanwhile, automatic warehousing can also be realized, and core report is generated in time, effectively improve the scientific research efficiency of core and large-scale flow operation efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of core drilling, and in particular to a core sample automatic processing method, device and storage medium. BACKGROUND

[0002] Core drilling is a common exploration method for solid mineral geological exploration. A cylindrical drill bit and drilling tool break rock along the circumference of the hole bottom in a ring shape, leaving a columnar core in the center of the hole bottom. The core sample is taken out of the hole to study the geological characteristics of the local block, hence the name core drilling. Unlike onshore core drilling, offshore core drilling is drilling a hole into the seabed to explore and obtain core samples from the seabed.

[0003] To analyze the geological characteristics of the core sample, the core sample needs to be transported to the laboratory for cutting and instrument measurement. In the traditional method, core cutting and measurement operations are mainly realized by manual operation. In the case of a large number of core samples, operation errors are likely to occur, resulting in unqualified core data. At the same time, the storage efficiency of core samples and core data is low, hindering the realization of large-scale flow operation. SUMMARY

[0004] Therefore, the present application provides a core sample automatic processing method, device and storage medium.

[0005] The first aspect of the present application provides a core sample automatic processing method, comprising the following steps:

[0006] Receiving an initial core sample, performing infrared thermal imaging on the initial core sample to obtain thermal imaging data;

[0007] Uploading the thermal imaging data to a data server;

[0008] Preliminary cutting of the initial core sample to obtain a segmented core;

[0009] Preliminary measurement of the segmented core to obtain segmented core data; the preliminary measurement includes microorganism detection, pore water detection, core image acquisition, X-ray scanning and physical and chemical information detection;

[0010] Uploading the segmented core data to the data server and constructing a three-dimensional model of the core sample on the data server;

[0011] Axial cutting of the segmented core to obtain a half-section core;

[0012] Core color measurement and spectral measurement of the half-section core to obtain core profile data;

[0013] Uploading the core profile data to the data server;

[0014] generating a core report according to the thermal imaging data, the segmented core data and the core profile data in the data server.

[0015] Further, after receiving the initial core sample, the method further comprises the following steps:

[0016] generating a core code for the core sample, the core code being in the format of date_cruise ID_site ID_core random code.

[0017] removing hydrogen sulfide deposits from the core sample.

[0018] Further, the infrared thermal imaging of the initial core sample is performed by a natural gas hydrate infrared thermal imager; and the thermal imaging data includes thermal imaging data with depth markers, the depth markers being used to represent the combined temperature of the core.

[0019] Further, after the step of uploading the thermal imaging data to the data server, the data server classifies the thermal imaging data by using a hydrate classification neural network.

[0020] The hydrate classification neural network is trained by the following steps:

[0021] collecting historical thermal imaging data;

[0022] classifying the historical thermal imaging data, and dividing the historical thermal imaging data into multiple categories according to the enrichment degree of hydrates; and dividing a training set and a validation set according to a preset proportion in each category.

[0023] training the hydrate classification neural network based on the training set, and verifying the training effect of the hydrate classification neural network by using the validation set, the training effect being indicated by an accuracy rate and a recall rate.

[0024] calculating a current score of the hydrate classification neural network according to the accuracy rate and the recall rate obtained by the verification, and determining that the training of the hydrate classification neural network is completed when the current score of the hydrate classification neural network exceeds a preset score threshold.

[0025] Further, the microorganism detection specifically comprises the following steps:

[0026] sampling microorganisms from the segmented core by using a microorganism sampler to obtain a microorganism sample;

[0027] detecting the microorganism sample by using a microfossil experimental instrument to obtain microorganism information;

[0028] The pore water detection specifically comprises the following steps:

[0029] Obtaining a pore water sample by taking the pore water from the core through a sampler;

[0030] Obtaining pore water information by detecting a chemical composition of the pore water sample using an organic chemical instrument;

[0031] The core image collection specifically includes the following steps:

[0032] Obtaining a plurality of core images by taking segmented cores through a CCD camera;

[0033] Classifying the core images through a core image classification neural network;

[0034] The X-ray scanning specifically scans the segmented cores through a CT scanner to obtain a plurality of core slices;

[0035] The physical and chemical information detection is specifically used to obtain mineral content, magnetic susceptibility, remanence intensity, and demagnetization rate of the segmented cores as physical and chemical data of the core;

[0036] The microorganism information, the pore water information, the core images, the core slices, and the physical and chemical data are output as segmented core data.

[0037] Further, before the microorganism information, the pore water information, the core images, the core slices, and the physical and chemical data are output as segmented core data, the following steps are further included:

[0038] Generating a microorganism code for the microorganism information, and the code format is date_cruise ID_station ID_core random code_microorganism information ID;

[0039] Generating a pore water code for the pore water information, and the code format is date_cruise ID_station ID_core random code_pore water information ID.

[0040] Further, the constructing the three-dimensional model of the core sample on the data server specifically includes the following steps:

[0041] According to the core slices, performing three-dimensional restoration on the segmented cores to obtain a preliminary model;

[0042] Adding the microorganism information and the pore water information to the preliminary three-dimensional model;

[0043] According to the core surface of the preliminary model, obtaining the three-dimensional model of the core sample according to the core images.

[0044] Further, after the generating a core report step, the following steps are further included:

[0045] Sending the core report to an object subscribing to the core report;

[0046] Waiting for a preset modification duration, and if no feedback from the object is received within the preset modification duration, it is confirmed that the core report data is accurate.

[0047] If the feedback from the object is received within the preset modification duration, the semi-split core is re-measured through manual operation.

[0048] The second aspect of the present application discloses an electronic device, comprising a processor and a memory.

[0049] The memory is used for storing a program.

[0050] The processor executes the program to realize a core sample automatic processing method.

[0051] The third aspect of the present application discloses a computer readable storage medium, the storage medium stores a program, and the program is executed by a processor to realize a core sample automatic processing method.

[0052] The embodiments of the present application have the following beneficial effects: the disclosed core sample automatic processing method, device and storage medium can automatically collect core information, retain initial data of drilling and mining cores, and reduce the degree of manual participation; meanwhile, automatic warehousing can be realized, and a core report can be generated in time, so that the scientific research efficiency and large-scale flow operation efficiency of cores are effectively improved.

[0053] Additional aspects and advantages of the present application will be described in the following description section, some of which will become apparent from the following description, or will be understood through practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0055] Figure 1 is a basic implementation flowchart of a core sample automatic processing method, device and storage medium of the present application;

[0056] Figure 2 is a core warehousing flowchart in a core sample automatic processing method, device and storage medium of the present application;

[0057] Figure 3 is a core coding schematic diagram in a core sample automatic processing method, device and storage medium of the present application;

[0058] Figure 4is a hydrate classification neural network training flowchart in the core sample automatic processing method, equipment and storage medium of the application;

[0059] Figure 5 is a microorganism information collection flowchart in the core sample automatic processing method, equipment and storage medium of the application;

[0060] Figure 6 is a pore water information collection flowchart in the core sample automatic processing method, equipment and storage medium of the application;

[0061] Figure 7 is a core image schematic diagram in the core sample automatic processing method, equipment and storage medium of the application;

[0062] Figure 8 is a core slice data schematic diagram in the core sample automatic processing method, equipment and storage medium of the application;

[0063] Figure 9 is a core sample three-dimensional model schematic diagram in the core sample automatic processing method, equipment and storage medium of the application;

[0064] Figure 10 is a core report sending flowchart in the core sample automatic processing method, equipment and storage medium of the application. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0066] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Rather, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0067] Before specifically explaining the embodiments of the present application, first, some industry terms related to the present application are explained:

[0068] Core drilling: Core drilling is a common exploration method for solid mineral geological exploration. Unlike onshore core drilling, offshore core drilling is a drilling ship drilling into the seabed for exploration, obtaining the core of the seabed. Through core analysis, the microbial characteristics, pore water characteristics, and physical and chemical characteristics of the geological area where the core is located can be investigated to further determine the stratigraphic age and conduct stratigraphic correlation; by observing the lithology and sedimentary structure of the core, the depositional environment of the geological area where the core is located can be determined, and the structure and fault conditions such as stratigraphic dip, stratigraphic contact relationship, and fault position can be understood. It can be seen that core drilling is an important geological exploration method for studying and understanding underground geology and mineral resources.

[0069] Drilling ship: A drilling ship is a ship specially designed for drilling operations on seabed geological structures, mainly used for marine geological exploration, and core drilling is an important application scenario of marine geological exploration. A drilling ship obtains a large number of cores during each voyage. Taking the "Resolution" drilling ship as an example, according to the data, the "Resolution" drilling ship drilled an average of 2000m per voyage in the Ocean Drilling Program, and collected about 1500 core samples (estimated at 1.5m) each time. Among them, the total length of the core of the IODP342 voyage is 5700m, and nearly 4000 core samples (estimated at 1.5m). Therefore, in the face of so many core samples, if manual intervention is required at each step of the core storage process, it is easy to cause human error and affect the storage efficiency.

[0070] To solve the problem of too high manual participation in the core storage process, as shown in Figure 1 The present application provides a core sample automatic processing method, which comprises the following steps:

[0071] S1. Receive the initial core sample, perform infrared thermal imaging on the initial core sample, and obtain thermal imaging data;

[0072] S2. Upload the thermal imaging data to the data server;

[0073] S3. Preliminary cutting of the initial core sample to obtain segmented cores;

[0074] S4. Preliminary measurement of the segmented cores to obtain segmented core data; preliminary measurement includes microbial detection, pore water detection, core image acquisition, X-ray scanning, and physical and chemical information detection;

[0075] S5. Upload the segmented core data to the data server and construct a three-dimensional model of the core sample on the data server;

[0076] S6. Axial cutting of the segmented core to obtain a half-section core;

[0077] S7. Core color measurement and spectrum measurement are performed on the half-split core to obtain core profile data;

[0078] S8. The core profile data is uploaded to a data server;

[0079] S9. A core report is generated in the data server according to the thermal imaging data, segmented core data and core profile data.

[0080] The core storage process in the embodiment of the application is as shown in Figure 2 The drilling ship breaks the rock in a circular ring at the bottom of the hole through the cylindrical drill bit and drill tool, and a columnar core is reserved at the center part of the hole bottom, and then the core is taken out from the hole, and the core is transported to the laboratory through the core conveyor and other instruments for geological feature measurement; after the laboratory completes the geological feature measurement of the core, a core report is generated as the measurement result output to the researchers for reading, and the researchers further analyze the geological conditions according to the core report.

[0081] S1. An initial core sample is received, and infrared thermal imaging is performed on the initial core sample to obtain thermal imaging data.

[0082] In the embodiment of the application, after receiving the initial core sample, the core sample is first generated with a core code. As shown in Figure 3 The core code is composed of "date_cruise ID_site ID_core random code". The date represents the sampling time of the core sample; the cruise ID represents the exploration ship voyage for collecting the core sample; the site ID represents the well site for collecting the core sample; and the core random code is the core unique ID generated by the server. Generating the core code for the core sample is helpful for the subsequent logging work of the core sample, and various tables and reports generated by the subsequent logging work are associated with the core code.

[0083] In the embodiment of the application, after receiving the initial core sample, the hydrogen sulfide deposition in the core sample is also excluded. There are a large number of sulfur and hydrogen elements in the bodies of animals and plants, which can generate hydrogen sulfide compounds through high temperature and high pressure action or bacterial action. Especially in anoxic environment, microorganisms can convert organic sulfur and inorganic sulfur into hydrogen sulfide; therefore, from the environment of hydrogen sulfide generation, it can be found that the formation is an excellent hydrogen sulfide production and storage place, and the core sample obtained from the formation may also contain hydrogen sulfide. The present embodiment removes the hydrogen sulfide in the core sample by means of the core hydrogen sulfide removal device, effectively protecting the safety of researchers and experimental instruments.

[0084] The length of the core sample delivered by the core conveyor in this embodiment is about 950 cm, and the maximum diameter is 15 cm. In this embodiment, a natural gas hydrate infrared thermal imager is used to collect thermal imaging data of the distribution of natural gas hydrate inside the core. The thermal imager is precisely positioned through a slide rail system. After the thermal imaging data is collected, thermal imaging data with depth markers is obtained. The depth markers on the thermal imaging data are used to represent the combined temperature of the core.

[0085] S2. Upload the thermal imaging data to the data server;

[0086] The collected thermal imaging data is uploaded to the data server. The data server can draw images in JPEG format based on the obtained data and save them in the database. In this embodiment, a mysql database is used to store the thermal imaging data, and unstructured data is converted into base64 format data for storage.

[0087] On the data server, a hydrate classification neural network is preloaded for image classification. Specifically, as shown in Figure 4 the image classification process is divided into two parts, namely the model generation stage and the data verification stage. The specific process is as follows:

[0088] Step 1-1: Collect existing thermal imaging data. This data mainly comes from the existing thermal imaging data storage in the database. A certain amount of thermal imaging data is retrieved from the database for model training.

[0089] Step 1-2: Researchers label and classify the collected thermal imaging data. The classification can be binary based on whether it contains hydrates, or multi-classification based on the degree of hydrate enrichment (e.g., no hydrates as classification 1, general hydrate enrichment as classification 2, high hydrate enrichment as classification 3, etc.). Currently, it is binary based on whether it contains hydrates. The data is divided into a test set and a validation set. The test set is used for training, and the validation set is used for model verification.

[0090] Step 1-3: Based on the training set, use a convolutional neural network (CNN) for training, and then use the validation set to verify the model effect. Repeat this process, and each verification will give an accuracy P and a recall R. Then calculate the comprehensive score Score = 2*(P*R) / (P+R). If the Score reaches the preset score threshold, which is 0.5 in this embodiment, the model effect meets the requirements and can be used for thermal imaging data classification.

[0091] Step 1-4: Generate the latest hydrate classification neural network. In this embodiment, the model will be retrained to generate a replacement model according to newly collected data when the ship is not in operation. The model cycle update time can be freely configured (such as daily, weekly, or monthly updates). In this embodiment, the hydrate classification neural network starts updating at 1 a.m. every day;

[0092] Step 1-5: After the data server receives the thermal imaging data generated for this core sample, the hydrate classification neural network is used for classification;

[0093] Step 1-6: The hydrate classification neural network will automatically generate classification data and store the classification results and pictures together in the database;

[0094] Step 1-7: The thermal imaging data in the library will eventually be output together in the final core report for researchers to read;

[0095] Step 1-8: After reading the core report, researchers may point out classification errors in the thermal imaging data. If the data server receives feedback from researchers about errors, the latest data will be used for training the next model update. If there is no feedback, the default model classification result is correct.

[0096] S3. Perform preliminary cutting on the initial core sample to obtain a segmented core.

[0097] The core sample is about 950 cm long and has a maximum diameter of 15 cm. In this embodiment, the core sample is cut into a segmented core of about 150 cm for subsequent measurement work.

[0098] S4. Perform preliminary measurement on the segmented core to obtain segmented core data; preliminary measurement includes microbial detection, pore water detection, core image acquisition, X-ray scanning, and physical and chemical information detection.

[0099] Microbial detection: Microorganisms that live in environments below the earth's surface are called lithobiotic microorganisms. Lithobiotic microorganisms have a wide distribution around the world, and where microorganisms exist, biochemical reactions promoted by microbial enzymes will occur. Therefore, lithobiotic microorganisms play an important role in the chemical substance cycle above and below the earth's surface. At the same time, lithobiotic microorganisms also have environmental benefits. For example, seabed lithobiotic microorganisms may play a role in carbon cycling and global warming because methane gas produced by seabed lithobiotic microorganisms can have a potential impact on the climate; therefore, extracting and detecting lithobiotic microorganisms that may be attached to the core have important scientific value.

[0100] For example, Figure 5As shown, the microbial detection in this embodiment mainly includes the following steps:

[0101] S4-a1. Microbial sampling is performed on the segmented core using a microbial sampler to obtain a microbial sample. Since the number of microorganisms in the core is very small, and the calcium compounds in the core have a high adsorption effect on microorganisms, extracting microorganisms by targeting calcium salts in the core is a more efficient approach. Existing microbial sampling methods include enzyme lysis, freeze-thaw, and freeze-grinding methods. This embodiment uses a lysis method to achieve this. Lysozyme, proteinase K, and poly-(A) are added to the microbial sampler to reduce the microorganism's adsorption of calcium compounds in the core. SDS lysis is then used to sample the microorganisms in the core as microbial samples.

[0102] S4-a2. Use micropaleontology experimental instruments to test microbial samples and obtain microbial information.

[0103] This embodiment of the micropaleontology experimental instrument analyzes foraminifera, calcareous nanofossils, and other information reflected by microorganisms in rock cores. Experimental methods include preparing microbial samples and observing their surface and internal details using an optical stereomicroscope; and using CT scans to determine their internal structure. Studying microbial samples in rock cores helps determine the age and oil-bearing potential of the rock layers depicted in the cores, and can serve as a guide for petroleum geologists.

[0104] Pore ​​water detection: The pores in the core may be formed during the mineral deposition process or in certain geological processes after sedimentation and diagenesis. Therefore, chemical composition analysis of the pore water in the core can effectively reflect the chemical reactions between the solid and liquid phases of the sediments and the exchange and diffusion of substances during the early diagenesis process.

[0105] like Figure 6 As shown, the pore water detection in this embodiment mainly includes the following steps:

[0106] S4-b1. Obtain pore water from the core using a sampler to obtain a pore water sample;

[0107] S4-b2. Use an organic chemical instrument to test the chemical composition of the pore water sample to obtain pore water information;

[0108] In this embodiment, the chemical composition test of the pore water sample mainly includes isotope analysis of various anions and cations in the pore water. For example, the anion Cl - 、SO4 2- and cation K + 、Na + , Ca 2+ Mg 2+The concentration analysis of the pore water sample is performed by ion chromatography. Before measurement, the pore water sample is diluted by dilute HNO3, and then Rh is added as an internal standard. The ion chromatograph is used for measurement. The measurement of carbon and oxygen isotopes is performed by gas isotope mass spectrometer. The pore water sample is first reacted with 100% phosphoric acid at room temperature for a period of time, and then the released CO 2 The gas is sent to the gas isotope mass spectrometer for measurement. The measurement of boron and chlorine isotopes is performed by positive thermal ionization mass spectrometry. The pore water sample is sent to the mass spectrometer, and then the static double-receiving method is used for measurement after vacuum treatment in the mass spectrometer.

[0109] After the microbial information and the pore water information are measured, the microbial information is generated into a microbial code, and the pore water information is generated into a pore water code. Specifically, the coding format of the microbial code is: date_cruise ID_station ID_core random code_microbial information ID; and the coding format of the pore water code is: date_cruise ID_station ID_core random code_pore water information ID. As can be seen from the coding format, the microbial code and the pore water code are associated with the core code, so that the source core can be retrieved through the microbial code and the pore water code.

[0110] In this embodiment, the core image acquisition specifically includes the following steps:

[0111] S4-c1. The segmented core is photographed by a CCD camera to obtain a plurality of core images.

[0112] S4-c2. The core profile data is classified by a core image classification neural network.

[0113] In this embodiment, the core is photographed by a CCD camera with a six-direction freedom support. The CCD camera is installed at the end of a six-degree-of-freedom support, and the six-degree-of-freedom support enables the CCD camera to move in six degrees of freedom (i.e., three degrees of translational freedom and three degrees of rotational freedom) within a certain range, thereby further photographing core images representing the surface features of the segmented core. The core image is shown in Figure 7 After the core image is photographed by the CCD camera, the real-time photographed data is transmitted to the data server through the CCD camera, and a core image classification neural network is preloaded in the data server for image classification. The core image classification neural network is similar to the above-mentioned hydrate classification neural network training and verification process. The existing core image data is artificially classified and marked to divide the training set and the verification set to train the core image classification neural network. Whether the core image classification neural network can be put into actual work is determined according to the comprehensive score.

[0114] The X-ray scanning in this embodiment is specifically performed by a CT scanner to scan the segmented core, and a plurality of core slice CT (Computed Tomography) scans are performed. The CT scan refers to dividing the segmented core into a plurality of core slices, and performing three-dimensional reconstruction on the core sample through the plurality of core slices. The effect of the core slice is shown in Figure 8 Due to the different compositions of the internal parts of the core, the absorption intensity of X-rays is also different, so by performing X-ray scanning on the segmented core, the corresponding gray value can be generated according to the absorption intensity of the rays by each part in the core to represent different structural components; the part with greater density in the segmented core absorbs more X-rays, so the gray value on the core slice is greater. In this embodiment, the CT scanner includes an X-ray source, a stage, and an X-ray detector that collects the rays after scanning. The X-ray source emits X-rays to the segmented core on the stage, and the X-rays are received by the X-ray detector after a series of actions with the segmented core. The X-ray detector processes and converts it into an electrical signal to form a core slice that records the gray information of different positions. The CT scanning of the core sample helps to understand the bedding and its direction of the core sample, and also helps to play a screening role for subsequent analysis work, that is, to screen the segmented core with complete structure for subsequent research, which can improve the core recovery rate and the like.

[0115] In this embodiment, the physical and chemical information detection is specifically used to obtain the mineral content, magnetic susceptibility, remanent intensity, demagnetization rate and the like of the segmented core, and the data is written into the report as the physical and chemical data of the core. In an example, the mineral mass of the core sample is obtained by γ measurement, which can be used to analyze the mineral reserves of the rock layer where the core sample is located. The measurement of the magnetic susceptibility, remanent intensity, demagnetization rate and the like of the segmented core can obtain information such as the type, content, particle size and particle size arrangement direction of the magnetic minerals in the segmented core. Since the movement, deposition and transformation of magnetic minerals are closely related to the change of the deposition environment and the evolution of the paleoclimate, the magnetic minerals in the segmented core can be used as a substitute index for analyzing the environmental change and climate process of the rock layer where the core sample is located, and providing a basis for paleomagnetic chronology research.

[0116] S5. Upload the segmented core data to the data server, and construct a three-dimensional model of the core sample on the data server. In this embodiment, the three-dimensional reconstruction of the core sample is mainly based on the core slice, and the core slice is reconstructed in the data server to obtain a three-dimensional model of the core sample. An example of the constructed three-dimensional model is shown in Figure 9 The construction of the three-dimensional model of the core sample on the data server specifically includes the following steps:

[0117] S5-1. According to the core slice, perform three-dimensional reconstruction on the segmented core to obtain a preliminary model;

[0118] S5-2. Adding microorganism information and pore water information in the preliminary three-dimensional model;

[0119] S5-3. Restoring the core surface of the preliminary model according to the core image to obtain the three-dimensional model of the core sample.

[0120] S6. Axially cutting the segmented core to obtain a half-section core.

[0121] In some embodiments, after the segmented core is axially cut, the half-section core is further coded as half-section core data. The coding format is: date_cruise ID_station ID_core random code_half-section core ID. Among them, the half-section core ID is sequentially recorded, that is, the first half-section core of the same core sample is recorded as 1, the second half-section core is recorded as 2, and so on. The data coding of the half-section core is obtained to realize the association of the half-section core and the core sample.

[0122] S7. Core color measurement and spectrum measurement are performed on the half-section core to obtain core profile data;

[0123] S8. Uploading the core profile data to the data server;

[0124] The core is cut into a half-section to obtain the vertical variation information of the core sample and determine the distribution and content of the main minerals in the core. Specifically, the mineral spectrum usually contains a series of characteristic absorption bands, and the characteristic spectral band information extracted in different minerals is different. The absorption characteristics of minerals to light can be characterized by spectral absorption characteristic parameters such as absorption band wavelength position, depth, width, symmetry, area, etc. From these parameters, qualitative and quantitative information of various minerals can be extracted. After obtaining the core profile data in the embodiment of the present application, the image classification is performed on the data server using the core spectrum classification neural network. The core spectrum classification neural network is similar to the training and verification process of the hydrate classification neural network described above, that is, the existing core profile data is artificially marked and classified, and is divided into a training set and a verification set to train the core spectrum classification neural network. Whether the core spectrum classification neural network can be put into actual work is determined according to the comprehensive score.

[0125] S9. Generating a core report in the data server according to the thermal imaging data, the segmented core data and the core profile data.

[0126] After the step S9 of generating the core report in the embodiment, the following steps are further included:

[0127] S9-1. Sending the core report to the object subscribing to the core report;

[0128] S9-2. Waiting for a preset modification time, and if no feedback opinion from the object is received within the preset modification time, it is confirmed that the core report data is accurate;

[0129] S9-3. If feedback is received from the subject within the preset modification time, the half-cut core is re-measured through manual operation.

[0130] like Figure 10 As shown, in this embodiment, the data server generates a core report based on existing thermal imaging data, segmented core data, and core profile data. The report is an online document and supports editing and review functions, which is pushed to researchers who subscribe to the report. The core report information specifically includes basic information about the core (such as voyage, station, etc.), as well as further information obtained by analysis, such as hydrate infrared images and classifications, core images, core profile images, core three-dimensional models, pore water, microorganisms, and other biochemical information. In this embodiment, the core report is based on a subscription-based publishing model, that is, interested researchers can subscribe to the initial report of a specific core sample based on the core code. After the report is generated and archived, the data server will actively remind the scientist to read it through timely software and text messages.

[0131] In some embodiments, if a researcher has objections to certain data (e.g., incorrect graphic classification results, abnormal biochemical indicators, etc.), he or she can provide feedback to the data server, and the feedback here will be processed according to the researcher's authority. If the user has editing and correction permissions, the corresponding modification opinions will be processed directly on the data server, otherwise an application for modification needs to be initiated, and it will be processed on the data server after the final approval. There is a default delay time (the delay time in this embodiment supports active configuration, and the default is 1 day). If the default delay time is exceeded, the data initially reported by the human is normal. At the same time, a reminder message is sent at each established time interval within this time range to remind researchers to read the core report. In this embodiment, the delayed feedback time of the core report is associated with the update time of the neural network model. For the core report information that has not received feedback, it will be added to the database later to update the neural network model.

[0132] In summary, the present invention implements automated collection of first-hand core information after initial core cutting and cross-sectioning, retaining initial core data and automatically storing it in a database. Using methods such as convolutional neural networks, the data is promptly processed, reports are generated, and the data's potential is tapped. This automated processing of core samples reduces human involvement and improves the efficiency of large-scale assembly line operations.

[0133] By way of example, the automated processing flow of core samples according to an embodiment of the present invention is described as follows:

[0134] First step: Obtain core samples by drilling ship, and transport the core samples by core conveyor; use the natural gas hydrate infrared thermal imager to detect the internal natural gas hydrate distribution of the core samples, and actively upload the thermal imaging data with depth marks to the data server; after classification by the hydrate classification neural network, the data server draws JPEG format images according to the data and saves them in the database.

[0135] Second step: Core coding generation, coding format is date_cruise ID_site ID_core random code.

[0136] Third step: Use the core hydrogen sulfide removal device to remove H2S in the core sample.

[0137] Fourth step: Preliminary cutting of the core sample, cutting into segmented cores with a length of about 150 cm.

[0138] Fifth step: Use the microbial sampler to sample microorganisms from the segmented core.

[0139] Sixth step: Analyze information such as foraminifera and calcareous nannofossils on the microbial sample through micro-paleontological experimental instruments.

[0140] Seventh step: Data coding of microbial information, coding format is date_cruise ID_site ID_core random code_microbial information ID, and after coding, upload the microbial information to the data server for storage.

[0141] Eighth step: Use the sampler to obtain pore water from the segmented core.

[0142] Ninth step: Use organic chemical experimental instruments to analyze the main components of the pore water (such as trace elements, oxygen and carbon isotope determination, etc.).

[0143] Tenth step: Data coding of pore water information, coding format is date_cruise ID_site ID_core random code_pore water information ID, and after coding, upload the pore water information to the data server for storage.

[0144] Eleventh step: Use a CCD camera to collect core images; upload the collected core images to the data server, and after classification by the core image classification neural network, save the core images in the database.

[0145] Twelfth step: Scan the segmented core using an X-ray CT scanner to obtain core slice two-dimensional data; upload the core slice data to the data server, and perform three-dimensional reconstruction of the core sample according to the core slice data.

[0146] Thirteenth step: Physical and chemical information detection is performed on the segmented core, and parameters such as mineral content, magnetic susceptibility, remanence strength, and demagnetization rate collected are stored in the data server.

[0147] Fourteenth step: The segmented core is cut in the axial direction to obtain a half-section core.

[0148] Fifteenth step: Half-section core data encoding is generated for the half-section core, and the encoding format is date_cruise ID_site ID_core random code_half-section core ID.

[0149] Sixteenth step: Core color measurement and spectrum measurement are performed on the half-section, and further measurement of pore water and microorganisms is performed based on three-dimensional data.

[0150] Seventeenth step: The half-section core data is uploaded to the data server, and after classification by the convolutional neural network, the data is stored in the database.

[0151] Eighteenth step: A core report is generated based on thermal imaging data, segmented core data, and core profile data.

[0152] Nineteenth step: The core report is sent to subscribing researchers, and feedback is received from the receiving researchers; the report and model parameters are updated according to the feedback of the researchers.

[0153] Twentieth step: After the foregoing steps are completed, the core sample is finished with the preliminary measurement, and then more detailed measurement is performed in the laboratory according to the feedback of the researchers, and finally the core sample is sent to the storage.

[0154] The embodiments of the present application also provide an electronic device. The electronic device can have relatively large differences due to different configurations or performances, and can include one or more processors (central processing units, CPUs) and one or more memories, wherein the memories store at least one program code, the at least one program code is loaded and executed by the processors to implement the core sample automatic processing method provided in the method embodiments. Of course, the electronic device can also have a wired or wireless network interface, a keyboard, and an input and output interface, and other components for realizing the functions of the device, which are not described here.

[0155] In the exemplary embodiments, a computer readable storage medium, such as a memory including program codes executable by a processor in a computer device to perform the core sample automatic processing method in the above embodiments, is also provided. For example, the computer readable storage medium can be a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc Read-Only Memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0156] In some alternative embodiments, the functions / operations referred to in the block diagrams can not occur in the order as referred to in the operational illustrations. For example, two blocks shown in succession can in fact be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality / operations involved. Also, the embodiments presented and described in the flowcharts are only examples. The flowcharts are presented to provide further assistance in understanding the technology. The disclosed methods are not limited to the operation and logic flow presented in the flowcharts. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub operations described as part of a larger operation are performed independently.

[0157] Further, while the present application has been described in the context of functional modules, it is to be understood that one or more of the functions and / or features described can be integrated in a single physical device and / or software module, or one or more functions and / or features can be implemented in separate physical devices or software modules. It will also be appreciated that detailed discussion of the actual implementation of each module is not necessary for an understanding of the application. Rather, the actual implementation is a matter of choice apart from the conception of the modules involved. Accordingly, the application is not limited to the details of the implementation as set forth in the following description. Further, the disclosed particular concepts are merely illustrative and are not intended to limit the scope of the present application as defined by the appended claims and equivalents thereof.

[0158] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions of the present application can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0159] The memory can also include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The electronic device can further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system can be used for reading from and writing to a non-removable, non-volatile magnetic media (e.g., a "hard drive"). Figure 7 A magnetic disk drive can also be provided for reading from and writing to a removable, non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive can be provided for reading from and writing to a removable, non-volatile optical disk (e.g., a CD-ROM, DVD-ROM or other optical media). In these instances, each drive can be connected to the bus by one or more data media interfaces. The memory can include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the disclosure.

[0160] The logic and / or steps represented in the flowcharts and / or otherwise described herein, for example, can be considered a list of executable instructions for implementing logic functions and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, processor, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions, or a combination of these. For the purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0161] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0162] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the embodiments described above, various steps or methods can be implemented, for example, by software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following techniques can be used to implement the hardware: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0163] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0164] Although embodiments of the present application have been shown and described, it would be recognized by those of ordinary skill in the art that various changes, modifications, alternatives, and variations can be made thereto without departing from the principles and spirit of the present application, the scope of which is defined by the claims and their equivalents.

[0165] The above is a specific description of the preferred embodiments of the present application, but the present application is not limited to the described embodiments, and those skilled in the art can make various equivalent modifications or replacements without departing from the spirit of the present application, and these equivalent modifications or replacements are included in the scope defined by the claims of the present application.

Claims

1. A method for automated processing of core samples, characterized in that: The following steps are involved: receiving an initial core sample, and performing infrared thermal imaging on the initial core sample to obtain thermal imaging data; Upload thermal imaging data to the data server; Performing preliminary cutting on the initial core sample to obtain segmented cores; Performing preliminary measurements on the segmented cores to obtain segmented core data; the preliminary measurements include microbial detection, pore water detection, core image acquisition, X-ray scanning, and physical and chemical information detection; Uploading the segmented core data to a data server, and constructing a three-dimensional model of the core sample on the data server; The segmented core is cut axially to obtain a half-cut core; Performing core color measurement and spectrum measurement on the half-cut core to obtain core profile data; Uploading the core profile data to a data server; generating a core report in a data server based on the thermal imaging data, the segmented core data, and the core profile data; In the preliminary measurements: The microbial detection specifically includes the following steps: performing microbial sampling on the segmented cores by using a microbial sampler to obtain microbial samples; Testing the microbial sample using a micropaleontology experimental instrument to obtain microbial information; The pore water detection specifically includes the following steps: Pore ​​water is obtained from the core by a sampler to obtain a pore water sample; Using an organic chemical instrument to test the chemical composition of the pore water sample to obtain pore water information; The core image acquisition specifically includes the following steps: The segmented cores are photographed by a CCD camera to obtain several core images; classifying the core image using a core image classification neural network; The X-ray scanning is specifically performed by scanning the segmented core with a CT scanner to obtain a plurality of core slices; The physical and chemical information detection is specifically used to obtain the mineral content, magnetic susceptibility, remanence intensity, and demagnetization rate of the segmented core as the physical and chemical data of the core; After completing the preliminary measurement, the microbial information, pore water information, core image, core slice and physical and chemical data are output as segmented core data; Before outputting the microbial information, pore water information, core image, core slice and physicochemical data as segmented core data, the method further includes the following steps: Generate a microbial code for the microbial information, the coding format is: date_voyage ID_station ID_core random code_microbial information ID; Generate a pore water code for the pore water information, the coding format is: date_voyage ID_station ID_core random code_pore water information ID; The three-dimensional model of the core sample is constructed on the data server, specifically comprising the following steps: Based on the core slices, the segmented cores are three-dimensionally restored to obtain a preliminary model; adding the microbial information and pore water information to the preliminary model; The core surface of the preliminary model is restored according to the core image to obtain a three-dimensional model of the core sample.

2. The method for automated processing of core samples according to claim 1, characterized in that: After receiving the initial core sample, the method further includes the following steps: Generate a core code for the core sample in the format of: date_voyage ID_station ID_core random code; Hydrogen sulfide deposition in the core samples was excluded.

3. The method for automated processing of core samples according to claim 1, characterized in that: The infrared thermal imaging of the initial core sample is performed using a natural gas hydrate infrared thermal imager; the thermal imaging data includes thermal imaging data with depth marks, and the depth marks are used to represent the combined temperature of the core.

4. The method for automated processing of core samples according to claim 1, characterized in that: After the step of uploading the thermal imaging data to the data server, the data server classifies the thermal imaging data using a hydrate classification neural network; The hydrate classification neural network is trained by the following steps: Collect historical thermal imaging data; Classify and label historical thermal imaging data, dividing them into multiple categories based on hydrate enrichment. Within each category, divide the training set and validation set into pre-set proportions. Training the hydrate classification neural network based on the training set, and verifying the training effect of the hydrate classification neural network using the validation set, wherein the training effect is represented by accuracy and recall; The current score of the hydrate classification neural network is calculated based on the accuracy and recall rate obtained through verification. When the current score of the hydrate classification neural network exceeds a preset score threshold, it is determined that the hydrate classification neural network has completed training.

5. The method for automated processing of core samples according to claim 1, characterized in that: After the step of generating a core report, the method further includes the following steps: Sending a core report to an object that subscribes to the core report; Waiting for a preset modification time. If no feedback is received from the subject within the preset modification time, the core report data is confirmed to be accurate; If feedback from the subject is received within the preset modification time, the half-cut core is re-measured through manual operation.

6. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that The storage medium stores a program, and the program is executed by a processor to implement the method according to any one of claims 1 to 5.

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