[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119067-en":3,"doc-seo-119067-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119067,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","INTERPRETATION OF GEORADAR DATA BASED ON MACHINE LEARNING TECHNOLOGIES - paper","Laboratory interpretation of geological data often suffers from inconsistent accuracy, where different laboratories can produce different end results for the same samples, undermining reliability and lowering assessment quality. Machine learning is used to accelerate radar data processing and reduce such “misunderstandings,” enabling objective evaluation of automatic interpretation of georadar profiles. The work presents machine-learning algorithms that incorporate regression, classification, and clustering methods to optimize georadar data processing. Quantitative validation includes correlation coefficient 0.7072, determination coefficient 0.5001, an average 22.37-unit deviation from the regression line, classification indicating wet soil, and errors in sets not exceeding 1%.","Original Research Article: full paper  \n(2024), «EUREKA: Physics and Engineering»  \nNumber 4  \nINTERPRETATION OF GEORADAR DATA BASED ON MACHINE LEARNING TECHNOLOGIES  \nDinara Omarkhanova  \nDepartment of Information System  \nS. Seifullin Kazakh Agro Technical Research University  \n62 Zhenis ave., Astana, Republic of Kazakhstan, 010011  \nZhanar Oralbekova *  \nDepartment of Computer and Software Engineering  \nL. N. Gumilyov Eurasian National University  \n2 Satpaev str., Astana, Republic of Kazakhstan, 010008 [oralbekova@bk.ru](oralbekova@bk.ru)  \n*Corresponding author  \nAbstract  \nIn the laboratory analysis of geological data, a number of problems arise due to the insufficient accuracy of the results. For example, different laboratories may provide different end results for the same samples, which creates a problem. This can lead to unreliable results, which can ultimately reduce the quality of the assessment.  \nMachine learning allows to speed up the processing of radar data, as well as avoid the above-mentioned «misunderstandings». The problem of conducting scientific research at specialized landfills for a comprehensive assessment of the possibilities of using computer technology in the interpretation of georadar profiles is urgent. This makes it possible to objectively evaluate the result of automatic interpretation of georadar data.  \nThe several machine-learning algorithms described in the article are designing to improve the analysis and interpretation of data by incorporating various methods for optimizing georadar data processing processes. These methods include regression, classification and clustering.  \nBy incorporating these methods of optimizing the processing of georadar data into several machine-learning algorithms, the software can provide a comprehensive analysis and interpretation of the data obtained. This allows for a better understanding of the relationships, patterns and trends in the data, which ultimately leads to more informed decision-making and improved understanding.  \nTo improve the understanding of the results, the following quantitative indicators were obtained: correlation coefficient – 0.7072, determination coefficient – 0.5001, all these indicators correspond to these models. The deviation from the regression line is on average 22.37 units. Based on the classification results, the soil was determined to be wet. Errors in the sets do not exceed 1 % .  \nKeywords: georadar, geodata, interpretation, radargram, method, experiment, Bessel filter, spectrum, object, machine learning.  \nDOI: 10.21303/2461-4262.2024.003289  \n1. Introduction  \nThe georadar method is a relatively new technology used to study the geological environment in our country. Although theoretical developments have been around for a long time, it is only with the current level of technological progress that it has become possible to implement promising scientific ideas. This is due to the creation of modern equipment and computer systems for data collection and processing [1].  \nDespite the increasing need for georadar research in the field of construction and geoecology, there is still a significant shortage of literature and specialized courses on the theory, technique, methods and geological interpretation of geo-radar data. This makes it difficult to find specialists who are able to conduct research and solve complex problems. As the demand for georadar continues to grow, there is an urgent need to increase the availability of resources and training to meet this demand.  \nAutomation of radargram processing based on machine learning technologies is an important task in the field of radar data analysis. A radargram is an image obtained using a radar system and containing information about various objects and their properties.  \nMachine learning allows the development of algorithms and models that can automatically process radargrams and extract useful information from them without the need for manual  \n193  \nOriginal Research Artic","cbCaieV9YlczVkVX","https://ap.wps.com/l/cbCaieV9YlczVkVX","pdf",1666306,1,12,"English","en",105,"# Introduction\n## Machine learning for radargram processing\n## Georadar method background and need for automation\n## Challenges in interpretation and filtering","[{\"question\":\"Why is georadar data interpretation difficult in laboratory analysis?\",\"answer\":\"Different laboratories may output different results for the same samples, leading to unreliable interpretation and reduced assessment quality.\"},{\"question\":\"How does machine learning improve georadar data processing?\",\"answer\":\"Machine learning speeds up radar data processing and supports automatic interpretation of radargrams, including tasks such as object classification, segmentation, and recognition.\"},{\"question\":\"What algorithms and evaluation indicators are used in the study?\",\"answer\":\"The study describes regression, classification, and clustering approaches and reports correlation coefficient 0.7072, determination coefficient 0.5001, and average deviation from the regression line of 22.37 units, with classification indicating wet soil.\"}]","INTERPRETATION OF GEORADAR DATA BASED ON MACHINE LEARNING TECHNOLOGIES - 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