Seismic facies analysis is the interpretation of groups of seismic reflections based on their geometry, amplitude, continuity, frequency, and configuration, to infer the geological meaning such as sedimentary environment or rock type.

Seismic facies are typically recognized by patterns in:

• Amplitude – strength of reflection (indicates acoustic impedance contrasts). 
• Continuity – degree of lateral persistence of reflectors (related to depositional uniformity). 
• Frequency / bandwidth – sharpness or smoothness of reflections (related to layer thickness and lithologic variability). 
• Configuration / geometry – reflection terminations (onlap, downlap, truncation) and shapes of reflectors (parallel, chaotic, hummocky, etc.). 
• Phase – can indicate polarity or tuning effects.

      1. Qualitative (visual) analysis 

• Done by the interpreter visually examining seismic sections or time slices. 
• Based on classical criteria defined by Mitchum et al. (1977) in seismic stratigraphy. 

      2. Quantitative (attribute-based) analysis 

• Uses seismic attributes (e.g., RMS amplitude, instantaneous frequency, similarity) or machine learning techniques (e.g., PCA, SOM, K-means) to classify seismic facies automatically. 
• Provides facies maps that can be correlated with well data.

Typical workflow for seismic facies analysis:

1. Define objective (e.g., reservoir delineation, channel detection). 
2. Select seismic interval (horizon or volume). 
3. Compute relevant seismic attributes. 
4. Apply classification or clustering techniques (manual or automatic). 
5. Interpret facies in geological terms (e.g., channel sand, shale, reef, etc.). 
6. Validate with well logs, core data, or known geology.
    

Seismic facies interpretation helps geoscientists to:

• Identify depositional environments (fluvial, deltaic, turbiditic, reefal, etc.). 
• Map reservoir distribution and quality. 
• Detect stratigraphic traps or unconformities. 
• Support 3D reservoir modeling.

In seismic studies, we may have dozens to hundreds of attributes (amplitude, frequency, coherence, curvature, etc.). Many are redundant (e.g., RMS amplitude, reflection strength, and envelope all measure signal energy). PCA helps by: 

• Removing redundancy.
• Reducing dimensionality.
• Highlighting the most significant variations in the data.
• Creating composite attributes that better separate geological features.

          1. Standardize the data 

• Normalize each attribute so they have comparable scales.
• Example: RMS amplitude values might range in 100s, while coherence is between 0–1.

         2. Compute the covariance matrix 

• Measures how attributes vary with each other.

         3. Calculate eigenvalues and eigenvectors 

• Eigenvectors = direction of maximum variance (new attribute axes).
• Eigenvalues = amount of variance explained by each axis.

         4. Rank the components 

• PC1 (first principal component) explains the maximum variance.
• PC2 explains the next largest variance (orthogonal to PC1).
• PC3, PC4, etc., explain progressively less.

         5. Project data into new space 

• The seismic attributes are transformed into a smaller set of PCs.

In facies analysis, the goal is to group parts of the subsurface (seismic traces or well log intervals) that share similar properties, such as:

• Seismic attributes (amplitude, frequency, phase, semblance, coherency, etc.) 
• Well log features (gamma ray, density, neutron, sonic, resistivity, etc.) 
• Derived features (inversion results, AVO attributes, etc.)

These groups are interpreted as facies, which can correspond to lithological, depositional, or structural differences.

1. Select Input Attributes:
Choose meaningful features that represent subsurface variability (e.g. RMS amplitude, instantaneous frequency, or GR + RHOB logs). 

2. Normalize Data:
Scale all features to have similar ranges (important because K-means is distance-based). 

3. Choose Number of Clusters (K):
Can be guided by domain knowledge or methods like the Elbow method or Silhouette score. 

4. Apply K-means Algorithm: 

• Randomly assign K centroids. 
• Assign each data point to the nearest centroid. 
• Update centroids as the mean of points in each cluster. 
• Iterate until centroids stabilize. 

5. Interpret Clusters:
Map each cluster to a facies class — often validated using core or well data.
6. Visualize Results:
Display facies maps, cross-sections, or 3D volumes showing different facies regions.

In facies analysis, the SOM is used to automatically cluster well-log or seismic attribute data into facies classes (lithological or seismic). The key strength is that SOM learns nonlinear relationships and high-dimensional feature patterns, something K-means can’t easily capture.

1. Input Data Selection
Choose relevant features: 

• Well-log data: GR, NPHI, RHOB, DT, Rt, etc. 
• Seismic data: amplitude, frequency, phase, coherency, curvature, etc. 

2. Data Normalization
Scale features (usually 0–1 or z-score).
SOM is sensitive to magnitude differences.

3. Define the SOM Grid
A 2D lattice (e.g. 10×10 neurons) — each node (neuron) will represent one “prototype” pattern of the data.
4. Training
Each data vector is compared to all neurons. 

• The Best Matching Unit (BMU) (the neuron most similar to the data vector) and its neighbors are updated to become more like the input. 
• Over many iterations, the map “self-organizes” — similar data points end up close together on the 2D grid. 

5. Clustering / Facies Identification
After training, neurons are grouped (manually or via another algorithm like K-means) into facies classes.
These clusters are then mapped spatially or along wells. 

6. Interpretation
Interpret each cluster as a distinct facies, based on geological knowledge or core calibration.

Type: Boundary-based classifier (geometrical / hyperplane)

SVM finds an optimal separating boundary (hyperplane) between facies classes in the attribute space (e.g., PCA or seismic attributes). It tries to maximize the margin between classes  i.e., the distance between the closest points (called support vectors).

• SVM works very well when the classes are well-separated in attribute space. 
• You can use non-linear kernels (RBF, polynomial) to model curved decision boundaries. 

• Excellent performance in high-dimensional attribute spaces (like PCA). 
• Handles non-linear separations using kernels. 
• Usually robust to small training datasets (few wells). 

• Computationally expensive for large seismic volumes. 
• Doesn’t provide probability outputs easily.
   

Type: Rule-based (hierarchical partitioning)

A decision tree splits the attribute space step-by-step based on conditions like: Each node represents a question; each branch a decision; leaves represent predicted facies.

The tree automatically finds thresholds in seismic attributes that best separate facies.

• Easy to interpret (geologists can trace attribute thresholds). 
• Fast training and prediction. 
• Works with non-linear and non-normally distributed data. 

• Individual trees may overfit training data. 
• Sensitive to noise — often used inside an ensemble for stability.

Type: Instance-based (distance-based local method)

KNN does no explicit training. It classifies each new sample based on the K closest samples in attribute space.

KNN is ideal for capturing local variability in seismic attributes and subtle facies transitions.

• Simple and intuitive. 
• Captures nonlinear and irregular facies boundaries. 
• Works well when classes overlap or have complex shapes. 

• Computationally heavy on large data (requires comparing every new sample). 
• Sensitive to attribute scaling → normalization is essential. 
• “K” must be tuned (too small → noisy; too large → over-smoothed).

Type: Multiple-model voting (ensemble learning)

Instead of one tree, many decision trees are trained on random subsets of data and attributes. Each tree votes for a facies class, and the majority vote gives the final prediction. This is what we call Bagging (Bootstrap Aggregation) and when random subsets of features are also used, it becomes a Random Forest.

Ensembles are extremely robust to noise and variability in seismic attributes. They can capture complex non-linear facies patterns without overfitting.

• High accuracy and stability. 
• Can handle mixed-scale attributes easily. 
• Provides class probabilities and variable importance (which attributes matter most). 

• Less interpretable than a single tree. 
• Slightly slower to train.