Velocity model building is a fundamental aspect of seismic data processing and subsurface imaging, playing a key role in industries like oil and gas exploration. Traditionally, this process involves the manual picking of velocity values, followed by inversion techniques like tomography and full waveform inversion (FWI) to map the subsurface. While effective, these methods are time-consuming and computationally expensive, especially when dealing with large datasets or complex geological settings.
Machine learning (ML) offers a promising approach to automate and enhance the velocity model-building process. By leveraging large datasets of seismic shot gathers, machine learning algorithms can potentially deliver faster and more accurate velocity models, significantly transforming the seismic processing workflow. This article explores Velocity Model Building from Raw Shot Gathers Using Machine Learning, covering key methods, data preparation, model selection, and real-world applications.
Seismic Data and Velocity Model Building: Traditional Approach
What Are Raw Shot Gathers?
Raw shot gathers are the fundamental data collected during seismic surveys. In seismic acquisition, a source generates seismic waves that travel through the subsurface and are recorded by sensors called geophones. These recordings are organized into shot gathers, representing the response of the subsurface to the seismic energy at various offsets. Shot gathers provide critical information about the Earth’s subsurface structure and are the basis for generating velocity models.
Traditional Workflow of Velocity Model Building
- Data Acquisition: Seismic data is gathered in the field by deploying sources and receivers. The recorded waveforms are then organized into shot gathers.
- Velocity Analysis: Velocity analysis traditionally involves manually picking velocities from common-midpoint (CMP) gathers. This process is labor-intensive and prone to human error.
- Tomography: Seismic tomography uses travel times of reflected or refracted waves to invert for velocity models. This method involves solving complex mathematical equations to estimate the velocity distribution.
- Full Waveform Inversion (FWI): FWI is a high-resolution technique that uses the entire seismic waveform to produce velocity models. While accurate, FWI is computationally expensive and highly sensitive to noise and the initial model.
Challenges in Traditional Approaches
- Time-Consuming: Manual velocity picking and traditional inversion methods are slow and require expert interpretation.
- Computational Cost: Techniques like tomography and FWI are resource-intensive, especially when applied to large 3D datasets.
- Geological Complexity: Complex subsurface structures such as salt bodies or faulted regions can be difficult to model accurately using traditional methods.
Introduction to Machine Learning in Seismic Processing
What Is Machine Learning?
Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn from data without being explicitly programmed. By recognizing patterns in data, ML algorithms can make predictions, classify information, and optimize complex processes. In the context of seismic processing, ML models can learn relationships between raw shot gathers and subsurface velocity models, automating the traditionally manual process of velocity model building.
Why Apply Machine Learning to Velocity Model Building?
- Automation: Machine learning can significantly reduce the time and effort required for velocity analysis, especially when dealing with large datasets.
- Improved Accuracy: ML models can capture complex patterns and relationships that may be missed by manual methods, particularly in challenging geological settings.
- Scalability: ML models can handle vast amounts of seismic data more efficiently, making them ideal for large-scale surveys or 3D seismic datasets.
Data Preparation for Machine Learning
Understanding Shot Gather Data
Shot gathers consist of time-series data representing the seismic response recorded at various offsets from the source. The goal is to use this data to estimate the velocity at different subsurface depths. Key attributes of shot gathers include amplitude variations, arrival times, and frequency content, all of which can provide valuable information about the subsurface.
Velocity Models as Labels
To train machine learning models, labeled datasets are required. In this case, the labels are velocity models—representations of how seismic wave velocities vary within the subsurface. These velocity models can come from previous studies, synthetic models generated using geological rules, or existing manual velocity picks.
Preprocessing Shot Gathers
- Noise Reduction: Shot gathers often contain noise from various sources, such as environmental conditions or equipment. Techniques like deconvolution, filtering, and amplitude normalization can improve data quality.
- Amplitude Normalization: Normalizing amplitude variations ensures consistent input data, which helps the machine learning model focus on meaningful patterns rather than outliers.
- Format Conversion: Raw seismic data is often stored in formats like SEG-Y, which may need to be converted into formats like HDF5 or CSV for easy manipulation in machine learning frameworks.
Data Augmentation
Given the limited availability of labeled velocity models, data augmentation techniques can be used to generate synthetic datasets. By varying geological parameters and shot gather properties, a diverse set of training examples can be created to improve the robustness of the model.
Machine Learning Models for Velocity Prediction
Supervised Learning Models
Convolutional Neural Networks (CNNs)
CNNs are powerful tools for image-like data, making them well-suited for seismic data processing. By applying convolutional filters, CNNs can identify important spatial features in shot gathers, such as reflections, diffractions, and faults. These features can then be used to predict the underlying velocity structure.
Recurrent Neural Networks (RNNs)
RNNs are designed to handle sequential data, making them a good fit for seismic shot gathers, which contain time-series information. RNNs can capture temporal dependencies in the data, such as the arrival times of seismic waves, and use this information to infer velocity models.
U-Net
U-Net is a popular deep-learning architecture for segmentation tasks. Its encoder-decoder structure allows it to capture both local and global features in seismic data. This makes it particularly useful for predicting continuous velocity models, as it can account for both fine-scale features and broader geological trends.
Unsupervised Learning Models
Autoencoders
Autoencoders are neural networks designed to learn compressed representations of data. In the context of velocity model building, they can be used to extract key features from shot gathers without needing labeled data. These features can then be used as input to supervised models or directly for velocity estimation.
Clustering Algorithms
Clustering techniques, such as k-means or DBSCAN, can group seismic data based on similar attributes, helping to identify distinct geological layers. These clusters can be used to generate initial velocity models or to segment the subsurface into regions with similar properties.
Physics-Informed Neural Networks (PINNs)
PINNs integrate physical laws, such as the wave equation, into the training process. By incorporating known physical constraints, PINNs can produce more accurate and physically consistent velocity models. This approach is especially useful in complex geological settings where pure data-driven models might struggle.
Feature Engineering and Selection
Key Features from Shot Gathers
- Amplitude Variations: Amplitudes change as seismic waves pass through different subsurface materials. These variations provide clues about velocity contrasts between layers.
- Arrival Times: The time at which seismic waves arrive at a receiver can be used to estimate travel times and infer velocity distributions.
- Frequency Content: High-frequency waves attenuate faster, which can provide information about the subsurface material’s properties and its impact on wave propagation.
Feature Engineering Techniques
Feature engineering techniques like Fourier transforms, wavelet decompositions, and cross-correlation can help extract meaningful features from raw shot gather data. Multichannel data can also be processed to capture spatial coherence across receivers, improving the model’s ability to detect geological features.
Model Training and Evaluation
Training Process
Training involves feeding labeled shot gather data and corresponding velocity models into the machine learning model. The model learns to map seismic data to velocity values by adjusting its internal parameters (weights) through backpropagation.
Loss Functions for Velocity Model Prediction
Common loss functions, such as mean squared error (MSE), are used to measure the difference between the predicted and actual velocity models. For physics-informed models, the loss function may include terms derived from the physical laws governing seismic wave propagation.
Optimization Techniques
Popular optimization algorithms like Adam and RMSProp are used to minimize the loss function and update the model weights. Regularization techniques such as L2 regularization and dropout can prevent overfitting by limiting the complexity of the model.
Model Validation and Testing
Cross-Validation
Cross-validation involves splitting the dataset into multiple training and validation subsets. By training the model on different portions of the data, the generalization ability of the model can be tested. K-fold cross-validation is commonly used to ensure robust performance across different subsets.
Evaluation Metrics
- Mean Squared Error (MSE): Measures the average squared difference between predicted and true velocity models.
- Structural Similarity Index (SSIM): Assesses how closely the predicted velocity model matches the structural features of the actual model.
- Accuracy of Arrival Times: Evaluates how accurately the model predicts seismic wave travel times, which directly impact velocity estimation.
Comparison with Traditional Methods
ML-generated velocity models can be benchmarked against traditional methods such as manual velocity picking or tomography. By comparing speed, accuracy, and computational requirements, the benefits of machine learning can be assessed.
Deploying the Machine Learning Model
Application in Real-World Scenarios
Once trained, the machine learning model can be applied to new shot gathers to automatically generate velocity models. This greatly speeds up seismic processing, enabling faster decision-making in exploration or subsurface imaging.
Model Refinement
ML-generated velocity models can serve as a starting point for more refined techniques like full waveform inversion (FWI). This hybrid approach leverages the speed of ML while preserving the accuracy of traditional inversion methods.
Scalability and Performance
To handle large-scale seismic datasets, ML models can be deployed on cloud infrastructure or distributed computing platforms. This ensures that large 3D seismic surveys can be processed efficiently.
Challenges and Future Directions
Challenges in Data Quality
Real-world seismic data is often noisy or incomplete, posing challenges for machine learning models. Robust preprocessing techniques and data augmentation can help mitigate these issues, but handling poor-quality data remains an ongoing challenge.
Generalization Across Geological Settings
Machine learning models trained on one dataset may struggle to generalize to new areas with different geological conditions. Transfer learning and domain adaptation techniques are being explored to address this limitation.
Interpretability of Machine Learning Models
ML models, especially deep neural networks, are often seen as “black boxes,” making it difficult to interpret their predictions. Ongoing research is focused on developing explainable AI (XAI) techniques to link ML predictions with geological insights.
Emerging Techniques
- Unsupervised Learning: Future work may focus on reducing reliance on labeled data by using unsupervised or semi-supervised learning techniques.
- Quantum Computing: Quantum computing holds the potential to revolutionize seismic data processing, particularly for large-scale models.
- Reinforcement Learning: RL could be used to dynamically adjust the velocity model-building process based on feedback from ongoing seismic surveys.
Conclusion About Velocity Model Building from Raw Shot Gathers Using Machine Learning
Machine learning is poised to transform the way velocity models are built from raw shot gathers, offering faster and more accurate solutions than traditional methods. By automating the velocity analysis process and leveraging advanced algorithms like CNNs, RNNs, and physics-informed networks, ML opens new possibilities for subsurface imaging. While challenges remain, ongoing research and development in this field promise to unlock even greater potential in the future.