Distinguishing between generative AI and conventional machine learning can be confusing, especially when applying them to practical problems like well-log data analysis. Let's clarify the differences using the rock facies example and explore how generative AI can play a role in improving our rock facies classification task.
I initially planned to create a series of blog posts on deep learning, using open data sources to demonstrate how it can be applied to rock facies classification problems. However, I got sidetracked due to other commitments, and the project was put on hold. That said, I was productive in other areas—we developed pre-recorded courses. Now, with more time available, I'm ready to dive back into the hot topics of AI and resume work on this project.
Our Current Approach with Conventional Machine Learning
Dataset Details:
Well Logs: Measurements like Gamma Ray (GR), Neutron Porosity (NPHI), Compressional Sonic Travel Time (DTC), among others.
Wells: Data from more than 100 wells.
Sample Size: More than 1 million readings.
Labels: 80% of the dataset includes rock facies labels (12 classes).
Current Method:
Algorithm Used: From Logsitic Regression to XGBoost, a powerful gradient boosting machine learning model.
Process:
Training: Model is trained on the labeled data.
Prediction: The trained model predicts rock facies for the unlabeled 50% of the dataset.
This is a classic supervised learning approach where the models learn to map input features to output labels based on labeled training data. These are all discussed in this course (Machine Learning in Geoscience).
Introducing Generative AI
Generative AI refers to models that can generate new data samples similar to our training data. Unlike discriminative models (like XGBoost) that focus on predicting labels, generative models learn the underlying distribution of the data.
How Generative AI Can Play a Role
Here, we will consider four important roles that Gen AI can play:
Data Augmentation
Problem: Limited labeled data (here 80% while in most cases labeled data is rare!).
Solution: Use generative models to create synthetic labeled data to augment the training set.
How It Works:
Train a generative model (e.g., Generative Adversarial Network or Variational Autoencoder) on our existing labeled data.
Generate new synthetic data points that mimic the statistical properties of your labeled data.
Combine synthetic data with your real labeled data to train your XGBoost model.
Benefits:
Increases the size of your training dataset. This is very helpful in imbalanced datasets.
Provides more diversity in training examples.
It can improve the model's generalization ability to new, unseen data.
Semi-Supervised Learning
Problem: A large portion of your data is unlabeled.
Solution: Utilize generative models that can learn from both labeled and unlabeled data.
How It Works:
Models like Semi-Supervised GANs (SGANs) can incorporate unlabeled data during training.
The generative model learns the overall data distribution, helping to extract useful features from unlabeled data.
Improves classification by leveraging patterns present in the unlabeled data.
Benefits:
Makes use of the entire dataset, not just the labeled portion.
Can improve model accuracy without the need for additional labeling efforts.
Feature Learning
Problem: Complex relationships between features and rock facies.
Solution: Use generative models to learn meaningful representations of your data.
How It Works:
Employ models like Autoencoders to learn compressed representations (latent features) of your data.
These latent features can capture intricate patterns and structures in the data.
Use these features as inputs to your classification model (e.g., XGBoost).
Benefits:
Enhances the quality of input features.
This can lead to better classification performance.
Anomaly Detection and Novel Class Discovery
Problem: Identifying rare or novel rock facies not present in labeled data. For example, we have a dataset from Clastic depositional systems while trying to classify carbonate classes without enough training samples.
Solution: Use generative models to detect data points that don't fit learned patterns.
How It Works:
Train a generative model on known classes.
Data points that the model cannot accurately reconstruct or generate might indicate anomalies or new classes.
Benefits:
Helps in discovering new rock facies.
Differences Between Generative AI and Conventional Machine Learning
Let's look at the three main differences:
Objective
Conventional ML (Discriminative Models):
Focus on modeling the decision boundary between classes.
Learn P(y∣x): the probability of label y given features x.
Generative AI (Generative Models):
Aim to model the entire data distribution.
Learn P(x) or P(x,y): the probability of data x (and labels y).
Data Utilization
Conventional ML:
Primarily uses labeled data.
Unlabeled data is often ignored unless using semi-supervised techniques.
Generative AI:
Can leverage both labeled and unlabeled data.
Unlabeled data helps in understanding the data's inherent structure.
Capabilities
Conventional ML:
Excellent for prediction and classification tasks.
Limited in generating new data samples.
Generative AI:
Can generate new, synthetic data samples.
Useful for data augmentation and understanding data distributions.
In the next post, we will try to cover implementing Gen AI steps and practical considerations.
I have just found your site. We have alot of similar ideas. I am trying to create data sets for production operations in the permain basin at this time!!