Intelligent lithology identification system and method based on images and spectrum technology

#The present disclosure relates to the technical field of rock identification and classification in engineering geology, in particular to an intelligent lithology identification system and method based on images and spectrum technology.

#Rock identification is a fundamental work in geological survey. During the field of geological survey, geologists may identify the lithology of rocks based on the color, the texture, the mineral composition and the like of rocks. With the rapid development of artificial intelligence (AI), more and more researchers have begun to identify the lithology of rocks using deep learning technology, and to analyze and process the image features of the rocks through intelligent algorithms such as machine learning, thereby reducing the dependence on professional knowledge and equipment, achieving the purpose of identifying the lithology of rocks from image identification, and effectively avoiding the problems of long time consumption, low accuracy and subjective influence in artificial identification of rocks.

#However, the inventors found in research that with the wide application of image-based deep learning technology in lithology judgment, some problems have been gradually exposed. The determination of rock lithology should consider not only the appearance but also the composition and content of minerals, and these factors are often not reflected in the images. Therefore, when a variety of rock types are to be identified, it is unrealistic to rely simply on images to identify rock lithology.

#The accompanying drawings constituting a part of the present disclosure are used for providing a further understanding of the present disclosure, and the schematic embodiments of the present disclosure and the descriptions thereof are used for interpreting the present disclosure, rather than constituting improper limitations to the present disclosure.

#FIG. 1 is a schematic structural diagram of a rock shape generation system according to an embodiment of the present disclosure;

#FIG. 2 is a schematic side view of part of a sample processing system according to an embodiment of the present disclosure;

#In which, 1, laser rangefinder; 2, clip; 3, first retractable member; 4, rock; 5, rock shape generation module; 6, rock XRF detection plane pre-selection module; 7, central analysis and control system; 8, first gear; 9, first drive motor; 10, second retractable member; 11, third retractable member; 12, fourth retractable member; 13, mobile base; 14, rail; 15, horizontal movement driving device; 16, grinding member; 17, support beam; 18, fifth retractable member; 19, second drive motor; 20, second gear; 21, clamping groove; 22, grinding stone; 23, rock debris; 24, grinding bowl; 25, XRF analyzer; 26, XRD analyzer; 27, second data processing unit; 28, camera; 29, rock particle detection model; 30, rock classification model; 31, third data processing unit.

#It should be noted that the following detailed descriptions are exemplary and are intended to provide further descriptions of the present disclosure. All technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skills in the technical filed to which the present disclosure belongs, unless otherwise indicated.

#It should be noted that the terms used here are merely used for describing specific embodiments, but are not intended to limit the exemplary embodiments of the present invention. As used herein, unless otherwise clearly stated in the context, singular forms are also intended to include plural forms. In addition, it should also be understood that when the terms “comprise” and/or “include” are used in the description, it indicates the presence of features, steps, operations, devices, components, and/or combinations thereof.