# US 12,762,829 B2 · Method for determining ichnological subsurface geologic formation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,762,829 B2 - Document ID: US12762829B2 - Status: Granted - Kind: B2 - Application number: 18/916,924 - Filed: 2024-10-16 - Published: 2025-01-30 - Granted: 2026-10-13 - Earliest claimed: 2021-12-28 - Assignees: King Fahd University of Petroleum and Minerals - Applicants: King Fahd University of Petroleum and Minerals - Inventors: Korhan Ayranci; Umair Bin Waheed - Web page: https://patentgrep.com/patent/US12762829B2 ## CPC - G06V 10/454: {Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]} - E21B 25/00: Apparatus for obtaining or removing undisturbed cores, e.g. core barrels or core extractors (core bits E21B10/02) - G06V 10/764: using classification, e.g. of video objects - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - E21B: EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS ## Abstract A system, method, and non-transitory computer readable medium for ichnological classification of geological images are described. The method of ichnological classification of geological images includes receiving a geological image by a computing device having circuitry including a memory storing program instructions and one or more processors configured to perform the program instructions, formatting the geological image to generate a formatted geological image, applying the formatted geological image to a deep convolutional neural network (DCNN) trained to classify bioturbation indices, and matching the formatted geological image to a bioturbation index class. ## Claims ### Claim 1 A method of determining an ichnological classification of subsurface geological images, comprising: collecting a geological image from a subsurface core, receiving the geological image with a computing device having circuitry including a memory storing program instructions and one or more processors configured to perform the program instructions; formatting, by the computing device, the geological image to generate a formatted geological image; applying the formatted geological image to a deep convolutional neural network (DCNN) trained to classify bioturbation indices; and matching, by a classifier of the DCNN, the formatted geological image to a bioturbation index class, wherein the DCNN is trained on a training set of geological images pre-labeled with bioturbation indices and classifies the training set into bioturbation index classes, wherein each geological image of the training set is a 224×224 pixel, three channel image, wherein the three channels comprise a red channel, a blue channel and a green channel, and each geological image of the training set has low-level features including lines, edges and dots and high level features including objects, applying the formatted geological image to a series of 3×3 convolution filters, wherein each convolution filter generates a set of weights; freezing the weights of a first portion of the series of 3×3 convolution filters; training, using the training set, the weights of a second portion of the series of 3×3 convolution filters; and recognizing, by the DCNN, the objects. ### Claim 2 (depends on 1) The method of claim 1, wherein the bioturbation index classes include: unbioturbated images; moderately bioturbated images; and intensely bioturbated images. ### Claim 3 (depends on 2) The method of claim 2, wherein the unbioturbated images have no visible bioturbation. ### Claim 4 (depends on 2) The method of claim 2, wherein the moderately bioturbated images have 1% to 30% bioturbation. ### Claim 5 (depends on 2) The method of claim 2, wherein the intensely bioturbated images have 31% to 100% bioturbation. ### Claim 6 (depends on 1) The method of claim 1, further comprising: formatting the geological image by resizing the image to match an image input size of the DCNN. ### Claim 7 (depends on 1) The method of claim 1, further comprising: augmenting, by the computing device, the training set before applying the training set to the DCNN, wherein augmenting includes any one of rotating, cropping, flipping, scaling and shifting. ### Claim 8 (depends on 7) The method of claim 7, further comprising: augmenting the training set by randomly selecting an image to augment; applying a horizontal flip to the selected image to generate a flipped image; and adding the flipped image to the training set. ### Claim 9 (depends on 7) The method of claim 7, further comprising: augmenting the training set by randomly selecting an image to augment; applying a 10% shift to one of a height and a width of the selected image to generate a shifted image; and adding the shifted image to the training set. ### Claim 10 (depends on 1) The method of claim 1, further comprising: dividing the first portion into four blocks of the 3×3 convolution filters, the four blocks including a first block, a second block, a third block and a fourth block; applying max pooling to the first block to downsample the training set to 112×112 pixel images; applying max pooling to the second block to downsample the 112×112 pixel images to 56×56 pixel images; applying max pooling to the third block to downsample the 56×56 pixel images to 28×28 pixel images; and applying max pooling to the fourth block to downsample the 28×28 pixel images to 14×14 pixel images. ### Claim 11 (depends on 10) The method of claim 10, further comprising: dividing the second portion into a fifth block of the 3×3 convolution filters; training the weights of each of the 3×3 convolution filters of the fifth block; and applying max pooling to the fifth block to downsample the 14×14 pixel images to 7×7 pixel images. ### Claim 12 (depends on 11) The method of claim 11, further comprising: flattening the 7×7 pixel images to generate a one dimensional array of pixel images; applying the one dimensional array to a classifier; matching the one dimensional array to a bioturbation class; and outputting the bioturbation index for each geological image of the training set.