{"title":"利用深度学习技术准确识别盐穹:变压器、生成式人工智能和液态机器","authors":"Kamal Souadih, Anis Mohammedi, Sofia Chergui","doi":"10.1111/1365-2478.13603","DOIUrl":null,"url":null,"abstract":"<p>Across various global regions abundant in oil and natural gas reserves, the presence of substantial sub-surface salt deposits holds significant relevance. Accurate identification of salt domes becomes crucial for enterprises engaged in oil and gas exploration. Our research introduces a precise method for the automatic detection of salt domes, leveraging advanced deep learning architectures such as U-net, transformers, artificial intelligence generative models and liquid state machines. In comparison with state-of-the-art techniques, our model demonstrates superior performance, achieving a stable and validated <span></span><math>\n <semantics>\n <mrow>\n <mn>96</mn>\n <mo>%</mo>\n </mrow>\n <annotation>$96\\%$</annotation>\n </semantics></math> intersection over the union metric, indicating high accuracy and robustness. Furthermore, the Dice similarity coefficient attaining <span></span><math>\n <semantics>\n <mrow>\n <mn>90</mn>\n <mo>%</mo>\n </mrow>\n <annotation>$90\\%$</annotation>\n </semantics></math> underscores the model's proficiency in closely aligning with ground truth across diverse scenarios. This evaluation, conducted on 1000 seismic images, reveals that our proposed architecture is not only comparable but often surpasses existing segmentation models in effectiveness and reliability.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"72 9","pages":"3280-3294"},"PeriodicalIF":1.8000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Accurate identification of salt domes using deep learning techniques: Transformers, generative artificial intelligence and liquid state machines\",\"authors\":\"Kamal Souadih, Anis Mohammedi, Sofia Chergui\",\"doi\":\"10.1111/1365-2478.13603\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Across various global regions abundant in oil and natural gas reserves, the presence of substantial sub-surface salt deposits holds significant relevance. Accurate identification of salt domes becomes crucial for enterprises engaged in oil and gas exploration. Our research introduces a precise method for the automatic detection of salt domes, leveraging advanced deep learning architectures such as U-net, transformers, artificial intelligence generative models and liquid state machines. In comparison with state-of-the-art techniques, our model demonstrates superior performance, achieving a stable and validated <span></span><math>\\n <semantics>\\n <mrow>\\n <mn>96</mn>\\n <mo>%</mo>\\n </mrow>\\n <annotation>$96\\\\%$</annotation>\\n </semantics></math> intersection over the union metric, indicating high accuracy and robustness. Furthermore, the Dice similarity coefficient attaining <span></span><math>\\n <semantics>\\n <mrow>\\n <mn>90</mn>\\n <mo>%</mo>\\n </mrow>\\n <annotation>$90\\\\%$</annotation>\\n </semantics></math> underscores the model's proficiency in closely aligning with ground truth across diverse scenarios. This evaluation, conducted on 1000 seismic images, reveals that our proposed architecture is not only comparable but often surpasses existing segmentation models in effectiveness and reliability.</p>\",\"PeriodicalId\":12793,\"journal\":{\"name\":\"Geophysical Prospecting\",\"volume\":\"72 9\",\"pages\":\"3280-3294\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geophysical Prospecting\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/1365-2478.13603\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"GEOCHEMISTRY & GEOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geophysical Prospecting","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/1365-2478.13603","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
Accurate identification of salt domes using deep learning techniques: Transformers, generative artificial intelligence and liquid state machines
Across various global regions abundant in oil and natural gas reserves, the presence of substantial sub-surface salt deposits holds significant relevance. Accurate identification of salt domes becomes crucial for enterprises engaged in oil and gas exploration. Our research introduces a precise method for the automatic detection of salt domes, leveraging advanced deep learning architectures such as U-net, transformers, artificial intelligence generative models and liquid state machines. In comparison with state-of-the-art techniques, our model demonstrates superior performance, achieving a stable and validated intersection over the union metric, indicating high accuracy and robustness. Furthermore, the Dice similarity coefficient attaining underscores the model's proficiency in closely aligning with ground truth across diverse scenarios. This evaluation, conducted on 1000 seismic images, reveals that our proposed architecture is not only comparable but often surpasses existing segmentation models in effectiveness and reliability.
期刊介绍:
Geophysical Prospecting publishes the best in primary research on the science of geophysics as it applies to the exploration, evaluation and extraction of earth resources. Drawing heavily on contributions from researchers in the oil and mineral exploration industries, the journal has a very practical slant. Although the journal provides a valuable forum for communication among workers in these fields, it is also ideally suited to researchers in academic geophysics.