![\"Geophysics](\"https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/09\/1.webp\")
<\/a>What is Artificial Intelligence in Geophysics?<\/h2>\n\n
In geophysics, artificial intelligence works on the same principle as in other fields\u2014it recognizes patterns. Only in our case, these are not photographs or texts, but seismic data<\/strong>. Algorithms identify patterns in cubes and automatically highlight geological structures: horizons, faults, paleochannels, reefs, etc. Essentially, this is the same “object recognition” logic, but transferred to the language of geology. <\/p>\n\n If you’re interested in learning more about the types of neural networks, how convolutional models work, and how artificial intelligence differs from machine learning, read our separate article\u2014everything is explained in detail with examples: “How Artificial Intelligence Makes Mining Smarter”<\/em><\/a>.<\/em><\/p>\n\n Now, let’s look at how these approaches work specifically in seismic data interpretation. Previously, specialists spent months manually correlating horizons and faults. But with the advent of artificial intelligence-based tools, the process has changed. <\/p>\n\n Before discussing how work is changing with the advent of AI, it is important to clarify: processing and interpretation are different stages<\/strong>.<\/p>\n\n How does a geophysicist-interpreter typically<\/strong> work? They spend months at their desk, manually drawing horizon lines, outlining faults, and checking each section. Even a simple project can take six months, and a complex one even longer. This is still how most specialists work. <\/p>\n\n Now, consider a different scenario with artificial intelligence. First, a basic pre-trained model is calculated, and the geologist immediately identifies fault probabilities. Next, there is an option to fine-tune the model using labeled sections and Transfer Learning technology to incorporate their interpretation vision into the model. What used to take months is now completed in weeks, and sometimes even hours. For example, well tie-in to seismic, which previously took about 90 minutes, is now done in 2 minutes. How is this possible? Let’s explore further. <\/p>\n\n To understand exactly how artificial intelligence works in interpretation, let’s consider a specific case. As a test site, we will take North Sea data \u2013 one of the most famous test projects, combining complex salt structures, chaotic reflections, and calmer layers higher in the section. This diversity makes it convenient for testing technologies. The interpretation here was performed on the Geoplat platform. <\/p>\n\n This section is based on the webinar “Volumetric Classification of Seismic Patterns Using Deep Machine Learning”<\/a> by GridPoint Dynamics (Geoplat software).<\/p>\n\n Step 1: Load the Cube<\/strong> Step 2: Analyze Primary Data<\/strong> Step 3: Refine Labeling<\/strong> Step 4: Fine-tune the Model<\/strong> Step 5: Obtain the Structural Map<\/strong><\/p>\n\n In the final step, we obtain a ready-made three-dimensional cube: it can be rotated, transparency can be changed, and it can be compared with the original seismic data. The model not only reproduces the labeling but also identifies an additional seismic feature that was not provided for training. Its nature requires verification, but the fact itself demonstrates the additional value of AI \u2013 it functions both as an interpretation tool and as a quality control measure. <\/p>\n\n Thus, a sequence of five steps\u2014from loading the cube to a finished structural plan\u2014allows you to obtain in hours a result that previously required weeks. Everything looks simple: the system is already trained on models and can “recognize” basic patterns. But a natural question arises: what exactly was it trained on, and can this data be trusted? We will discuss this further. <\/p>\n\n To train an interpretation model, tens of thousands of seismic cubes are needed \u2013 but such arrays simply do not exist in nature. Such large and yet cleanly labeled datasets cannot be acquired or compiled. <\/p>\n\n This problem arose for the GridPoint Dynamics team several years ago. They were the first to create a synthetic seismic data generator. The solution proved challenging: they had to prove that artificial cubes were suitable for training. The proof came in the form of case studies \u2013 models trained on synthetic data worked confidently on real projects and yielded reproducible results. <\/p>\n\n Today, the question “can it be trusted” is rarely heard: the use of synthetic data has become standard, including among major companies.<\/p>\n\n The main advantage of this approach is that synthetic data allows modeling “clean” geological scenarios: different types of tectonics, noise, and distortions. Algorithms learn from reference examples where it is precisely known what is located where. <\/p>\n\n Then the expert gets involved. Several real sections are manually labeled, and the model is fine-tuned on the data of a specific project. This achieves a balance: the scalability of synthetic data is combined with the precision of a geophysicist’s live experience. <\/p>\n\n This is a common question: the machine can highlight something unnecessary or, conversely, miss something important. And this is precisely why the model always produces a result in the form of a “baseline” that the user sees. <\/p>\n\n Next, an expert steps in if they want to incorporate their vision and interpretation experience into the model. The geophysicist marks up several key sections, the model retrains, and rebuilds the cube. This is a fundamental point: the system is not closed and not a “black box”\u2014every action remains transparent and verifiable. <\/p>\n\n And here, a specialist is indispensable. Only a geophysicist considers the tectonics, stratigraphy, and specific features of a particular field. Their knowledge and experience transform a machine’s sketch into a complete interpretation. <\/p>\n\n Essentially, AI does not replace the expert<\/a>, but rather augments them: it removes routine burdens and allows them to focus on complex, analytical tasks where the final decision always rests with the human.<\/p>\n\n Perfect seismic data almost never exists. Especially when dealing with old surveys: noise, “footprints,” and low resolution obscure details. In such cases, special AI modules for cleaning and quality enhancement<\/strong> are employed. They are trained on synthetic data: the system is shown a noisy cube and its “clean” reference, and the algorithm learns to restore the original quality. <\/p>\n\n These modules allow for:<\/p>\n\n Thanks to this, what was previously considered “junk” material becomes a working basis for interpretation again.<\/p>\n\n And this applies not only to 3D cubes. Algorithms also work with 2D lines \u2013 including archival ones. Even old formats can be brought to a state where they return to circulation as a valuable source of geological information. <\/p>\n\n Yes, everything looks convincing. Now the main question is: how do you implement this in your company?<\/strong> Implementing AI software is not a one-time service, but rather a customization to the company’s processes. After purchasing the product, the vendor helps install the system, integrate it into the workflow, and trains specialists. Support and refinement of algorithms also remain with the vendor’s development team. <\/p>\n\n Training formats:<\/p>\n\n Important: users do not need to know AI mathematics or understand neural network architecture. Geophysical expertise is sufficient \u2013 the ability to label data, verify results, and use the tool to accelerate routine steps. <\/p>\n\n Thus, implementation does not require a separate R&D department. It is enough to purchase the product, undergo training \u2013 and you can start working. <\/p>\n\n If you are not yet representing a company, but are studying or working and want to master modern seismic data interpretation tools, there are several ways to try them in practice.<\/p>\n\n For Students<\/strong> For Specialists<\/strong> In other words, even if you do not have corporate access to AI software, there is still an opportunity to experience these technologies. There are enough options \u2013 from student licenses to championships and author’s courses. The main thing is the desire to learn and the readiness to try new things. <\/p>\n\n AI in geological and geophysical data interpretation is no longer an experiment \u2013 it is already working in real projects. Its main advantages are clear to anyone who has dealt with seismic data arrays. AI allows for: <\/p>\n\n What used to take months of manual work can now be done in hours. This not only accelerates project launches but also makes it possible to “revive” archival data that was previously considered outdated. And most importantly, it frees up time for real geology: analyzing structures, searching for new scenarios, and formulating hypotheses. <\/p>\n\n The prospects for AI in geophysics are excellent. Specialists can finally engage in geophysics itself, rather than drowning in routine and tedious tasks. <\/p>\nsays Daniil Bogoedov<\/cite><\/blockquote>\n\n AI in geophysics is not a trend, but a working tool that is shaping the future of the industry. The question now is not “whether,” but “when exactly” you will start using it. <\/p>\n\n What tasks in geophysics do you think AI should be applied to first? Share your opinion in the comments. <\/p>\n\n The material was prepared with the support of the Russian Ministry of Education and Science within the framework of the Decade of Science and Technology.<\/p>\n","protected":false},"excerpt":{"rendered":" Artificial intelligence is everywhere now. ChatGPT, neural networks for images, automation of everything. It seems that AI has already penetrated all spheres, but in reality, machine learning has been applied in our industry for a long time\u2014it just wasn’t as noticeable or popular before. The boom in generative neural networks made the topic trendy and […]<\/p>\n","protected":false},"author":12,"featured_media":57325,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_seopress_robots_primary_cat":"none","_seopress_titles_title":"Artificial Intelligence in Seismic Exploration: How it is Transforming Data Interpretation","_seopress_titles_desc":"Discover how Artificial Intelligence accelerates seismic data interpretation: less routine, more accuracy, and new opportunities for geophysicists","_seopress_robots_index":"","footnotes":""},"categories":[559,572],"tags":[574,602,603],"tag-cat":[596,599],"class_list":{"0":"post-57324","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-geophysics","8":"category-it","9":"tag-artificial-intelligence-in-geology","10":"tag-geological-exploration-data-interpretation","11":"tag-seismic-exploration","12":"tag-cat-geology-and-geophysics","13":"tag-cat-it-and-artificial-intelligence"},"acf":[],"pbg_featured_image_src":{"full":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/09\/oblozhka.webp",1707,1139,false],"thumbnail":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/09\/oblozhka-150x150.webp",150,150,true],"medium":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/09\/oblozhka-300x200.webp",300,200,true],"medium_large":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/09\/oblozhka-768x512.webp",768,512,true],"large":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/09\/oblozhka-1024x683.webp",1024,683,true],"1536x1536":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/09\/oblozhka-1536x1025.webp",1536,1025,true],"2048x2048":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/09\/oblozhka.webp",1707,1139,false],"bricks_large_16x9":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/09\/oblozhka-1200x675.webp",1200,675,true],"bricks_large":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/09\/oblozhka-1200x801.webp",1200,801,true],"bricks_large_square":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/09\/oblozhka-1200x1139.webp",1200,1139,true],"bricks_medium":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/09\/oblozhka-600x400.webp",600,400,true],"bricks_medium_square":["https:\/\/geoconversation.org\/wp-content\/uploads\/2025\/09\/oblozhka-600x600.webp",600,600,true]},"pbg_author_info":{"display_name":"\u042e\u043b\u0438\u044f \u0424\u0440\u043e\u043b\u043e\u0432\u0430","author_link":"https:\/\/geoconversation.org\/en\/author\/giulia-nikolaevna\/","author_img":false},"pbg_comment_info":" No Comments","pbg_excerpt":"Artificial intelligence is everywhere now. ChatGPT, neural networks for images, automation of everything. It seems that AI has already penetrated all spheres, but in reality, machine learning has been applied in our industry for a long time\u2014it just wasn’t as noticeable or popular before. The boom in generative neural networks made the topic trendy and…","_links":{"self":[{"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/posts\/57324","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/users\/12"}],"replies":[{"embeddable":true,"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/comments?post=57324"}],"version-history":[{"count":1,"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/posts\/57324\/revisions"}],"predecessor-version":[{"id":57326,"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/posts\/57324\/revisions\/57326"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/media\/57325"}],"wp:attachment":[{"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/media?parent=57324"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/categories?post=57324"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/tags?post=57324"},{"taxonomy":"tag-cat","embeddable":true,"href":"https:\/\/geoconversation.org\/en\/wp-json\/wp\/v2\/tag-cat?post=57324"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}<\/a>How AI Recognizes Faults and Paleo-structures<\/h2>\n\n
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How AI Recognizes Faults and Paleo-structures<\/h2>\n\n
<\/a><\/a><\/a><\/a>Synthetic Data Instead of Real Data \u2013 Can It Be Trusted?<\/h2>\n\n
What if AI makes a mistake?<\/h2>\n\n
What if the data is poor \u2013 can AI handle it?<\/h2>\n\n
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How to Implement AI in Your Workflow<\/h2>\n\n
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<\/a>Where to Try AI in Practice<\/h2>\n\n
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<\/a><\/a><\/a>AI in Geophysics: From Experiment to Working Tool<\/h2>\n\n
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