MX2023012700A - Metodo para predecir las caracteristicas geologicas a partir de imagenes de seccion delgada por medio de un proceso de clasificacion de aprendizaje profundo. - Google Patents
Metodo para predecir las caracteristicas geologicas a partir de imagenes de seccion delgada por medio de un proceso de clasificacion de aprendizaje profundo.Info
- Publication number
- MX2023012700A MX2023012700A MX2023012700A MX2023012700A MX2023012700A MX 2023012700 A MX2023012700 A MX 2023012700A MX 2023012700 A MX2023012700 A MX 2023012700A MX 2023012700 A MX2023012700 A MX 2023012700A MX 2023012700 A MX2023012700 A MX 2023012700A
- Authority
- MX
- Mexico
- Prior art keywords
- thin section
- classification process
- fractions
- section images
- classes
- Prior art date
Links
- 238000000034 method Methods 0.000 title abstract 4
- 238000013135 deep learning Methods 0.000 title 1
- 238000013145 classification model Methods 0.000 abstract 1
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
- G06V20/698—Matching; Classification
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Multimedia (AREA)
- Computing Systems (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Molecular Biology (AREA)
- Artificial Intelligence (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Databases & Information Systems (AREA)
- Medical Informatics (AREA)
- Image Analysis (AREA)
- Geophysics And Detection Of Objects (AREA)
- Image Processing (AREA)
Abstract
Un método para predecir una aparición de una característica geológica en una imagen de sección delgada geológica usa un proceso de clasificación habilitado por retropropagación entrenado mediante la introducción de fracciones de imágenes de entrenamiento extraídas que tienen sustancialmente la misma longitud horizontal y vertical absoluta y etiquetas asociadas para clases de un conjunto predeterminado de características geológicas, y calcular iterativamente una predicción de la probabilidad de aparición de cada una de las clases para las fracciones de imágenes de entrenamiento extraídas. El modelo de clasificación habilitado por retropropagación entrenado se usa para predecir la aparición de las clases en fracciones extraídas de imágenes de sección delgada geológica sin entrenamiento que tienen sustancialmente la misma longitud horizontal y vertical absoluta que las fracciones de imágenes de entrenamiento.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US202163187144P | 2021-05-11 | 2021-05-11 | |
| PCT/EP2022/062162 WO2022238232A1 (en) | 2021-05-11 | 2022-05-05 | Method for predicting geological features from thin section images using a deep learning classification process |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| MX2023012700A true MX2023012700A (es) | 2023-11-21 |
Family
ID=81941164
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| MX2023012700A MX2023012700A (es) | 2021-05-11 | 2022-05-05 | Metodo para predecir las caracteristicas geologicas a partir de imagenes de seccion delgada por medio de un proceso de clasificacion de aprendizaje profundo. |
Country Status (6)
| Country | Link |
|---|---|
| US (1) | US20240193427A1 (es) |
| EP (1) | EP4338134A1 (es) |
| AU (1) | AU2022274992A1 (es) |
| BR (1) | BR112023023436A2 (es) |
| MX (1) | MX2023012700A (es) |
| WO (1) | WO2022238232A1 (es) |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20220207079A1 (en) * | 2019-05-09 | 2022-06-30 | Abu Dhabi National Oil Company | Automated method and system for categorising and describing thin sections of rock samples obtained from carbonate rocks |
| CN111563445A (zh) * | 2020-04-30 | 2020-08-21 | 徐宇轩 | 一种基于卷积神经网络的显微镜下岩性识别方法 |
-
2022
- 2022-05-05 MX MX2023012700A patent/MX2023012700A/es unknown
- 2022-05-05 US US18/555,346 patent/US20240193427A1/en active Pending
- 2022-05-05 AU AU2022274992A patent/AU2022274992A1/en active Pending
- 2022-05-05 BR BR112023023436A patent/BR112023023436A2/pt unknown
- 2022-05-05 EP EP22728111.0A patent/EP4338134A1/en active Pending
- 2022-05-05 WO PCT/EP2022/062162 patent/WO2022238232A1/en active Application Filing
Also Published As
| Publication number | Publication date |
|---|---|
| EP4338134A1 (en) | 2024-03-20 |
| US20240193427A1 (en) | 2024-06-13 |
| AU2022274992A1 (en) | 2023-10-26 |
| BR112023023436A2 (pt) | 2024-01-30 |
| WO2022238232A1 (en) | 2022-11-17 |
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