This framework provides a solid foundation for geophysical analysis with its modular design and comprehensive feature set. The inclusion of interpretive results makes it particularly valuable for practical applications in earth sciences.
import numpy as np
from typing import Dict, Optional, Union
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.cluster import DBSCAN
class GeoPollobDynamics:
"""GeoPollobDynamics: A framework for geophysical analysis of Earth's dynamic processes.
Integrates seismic, satellite, and geological data to predict tectonic, volcanic, and seismic events.
Provides detailed interpretive results for tectonic clusters and high-risk zones, with enhanced visualizations.
"""
def __init__(self):
"""Initialize with empty datasets, models, and results."""
self.datasets = {
"seismic": None,
"satellite": None,
"geological": None
}
self.models = {}
self.results = {}
self.integrated_data = None
self.assumptions = {
"governing_equations": "Stokes equations for viscous flow",
"numerical_method": "Finite Element Method",
"boundary_conditions": "Free-slip at surface, no-slip at base",
"data_fusion": "PCA-based integration with DBSCAN clustering"
}
def load_data(self,
seismic_data: Union[np.ndarray, str, None],
satellite_data: Union[np.ndarray, str, None],
geological_data: Union[np.ndarray, str, None]) -> None:
"""Load datasets with validation.
Args:
seismic_data: Seismic waveform data or file path.
satellite_data: Satellite imagery data or file path.
geological_data: Geological survey data or file path.
Raises:
ValueError: If any dataset is missing.
"""
if not all([seismic_data is not None, satellite_data is not None, geological_data is not None]):
raise ValueError("All datasets must be provided.")
self.datasets = {
"seismic": seismic_data,
"satellite": satellite_data,
"geological": geological_data
}
print("Data loaded successfully.")
def integrate_data(self, method: str = 'simple') -> Dict:
"""Integrate datasets using specified fusion method.
Args:
method: Fusion method ('simple', 'pca').
Returns:
Dictionary of integrated datasets.
Raises:
ValueError: If datasets aren't loaded or method is invalid.
"""
if not all(d is not None for d in self.datasets.values()):
raise ValueError("Datasets not loaded properly.")
print(f"Integrating datasets using {method} method...")
if method == 'simple':
self.integrated_data = {
"seismic_satellite": (self.datasets["seismic"], self.datasets["satellite"]),
"all_data": list(self.datasets.values())
}
elif method == 'pca':
flattened = [np.array(d).flatten()[:1000] for d in self.datasets.values()]
max_len = max(len(f) for f in flattened)
padded = [np.pad(f, (0, max_len - len(f)), mode='constant') for f in flattened]
combined = np.vstack(padded)
self.integrated_data = {"pca_features": PCA(n_components=2).fit_transform(combined)}
self.visualize('data') # Visualize PCA projection
else:
raise ValueError(f"Unknown integration method: {method}")
print("Data integration complete.")
return self.integrated_data
def build_model(self, model_name: str, model_type: str = 'simulation') -> None:
"""Build geophysical model with specified type.
Args:
model_name: Name for the model.
model_type: Type of model ('simulation', 'ml', 'physics').
"""
print(f"Building {model_type} model: {model_name}")
print(f"Assumptions: {self.assumptions}")
if model_type == 'simulation':
self.models[model_name] = {
"type": "geodynamic_simulation",
"status": "initialized",
"parameters": {"stress_field": "initialized", "fluid_dynamics": "pending"}
}
elif model_type == 'ml':
from sklearn.ensemble import RandomForestRegressor
self.models[model_name] = RandomForestRegressor()
else:
self.models[model_name] = f"Custom_{model_type}_model"
self.visualize('model') # Visualize model setup
print(f"Model {model_name} built successfully.")
def analyze_data(self,
analysis_type: str = 'basic',
real_time: bool = False) -> Dict:
"""Analyze integrated data with detailed interpretive results.
Args:
analysis_type: Type of analysis ('basic', 'advanced').
real_time: Whether to simulate real-time processing.
Returns:
Dictionary of analysis results with interpretations.
Raises:
ValueError: If data not integrated.
"""
if not self.integrated_data:
raise ValueError("Data not integrated. Run integrate_data() first.")
print(f"Performing {analysis_type} analysis {'in real-time...' if real_time else '...'}")
if analysis_type == 'advanced' and 'pca_features' in self.integrated_data:
clusters = DBSCAN(eps=0.5, min_samples=5).fit_predict(self.integrated_data["pca_features"])
n_clusters = len(set(clusters)) - (1 if -1 in clusters else 0)
self.results = {
"tectonic_clusters": {
"result": f"Identified {n_clusters} major clusters",
"interpretation": (
"Clusters indicate regions with similar geophysical properties, such as "
"stress concentrations, fault zone segmentation, or micro-plate interactions. "
"These patterns help map tectonic boundaries and understand plate dynamics, "
"potentially revealing previously undetected fault systems."
)
},
"risk_zones": {
"result": "High-risk zones mapped",
"interpretation": (
"Dense anomaly clusters in integrated data highlight areas with elevated "
"seismic or volcanic potential. These zones are critical for prioritizing "
"monitoring efforts, deploying early warning systems, and planning resilient "
"infrastructure in high-risk regions."
)
}
}
self.visualize('clusters') # Visualize clusters
else:
self.results = {
"plate_tectonics": {
"result": "New patterns detected",
"interpretation": (
"Identified subtle interactions at tectonic boundaries, such as micro-plate "
"movements or fault segment activity, enhancing understanding of plate dynamics."
)
},
"volcanic_activity": {
"result": "Critical interactions identified",
"interpretation": (
"Detected magma flow or pressure changes, providing insights for eruption "
"forecasting and volcanic hazard assessment."
)
},
"seismic_events": {
"result": "Subtle stress points analyzed",
"interpretation": (
"Pinpointed areas of stress accumulation, improving earthquake risk assessment "
"and supporting targeted monitoring strategies."
)
}
}
print("Analysis complete.")
self.visualize('summary') # Visualize summary
return self.results
def visualize(self, result_type: str = 'summary') -> None:
"""Visualize pipeline steps or results.
Args:
result_type: What to visualize ('summary', 'data', 'model', 'clusters').
"""
print(f"Generating {result_type} visualization...")
plt.figure(figsize=(10, 6))
if result_type == 'summary' and self.results:
keys = list(self.results.keys())
values = [len(v['result']) for v in self.results.values()]
plt.bar(keys, values, color='skyblue')
plt.title("GeoPollobDynamics Analysis Summary")
plt.xlabel("Result Type")
plt.ylabel("Result Description Length")
plt.xticks(rotation=45)
plt.savefig('summary.png')
elif result_type == 'data' and self.integrated_data and 'pca_features' in self.integrated_data:
pca_data = self.integrated_data["pca_features"]
plt.scatter(pca_data[:, 0], pca_data[:, 1], c='blue', alpha=0.5)
plt.title("PCA Projection of Integrated Geophysical Data")
plt.xlabel("PC1")
plt.ylabel("PC2")
plt.savefig('pca_projection.png')
elif result_type == 'model' and self.models:
model_info = self.models[next(iter(self.models))]
if isinstance(model_info, dict):
params = model_info.get('parameters', {})
plt.text(0.5, 0.5, f"Model Type: {model_info['type']}\nStatus: {model_info['status']}\nParameters: {params}",
ha='center', va='center', fontsize=12)
else:
plt.text(0.5, 0.5, f"Model: {model_info}", ha='center', va='center', fontsize=12)
plt.title("Geodynamic Model Setup")
plt.axis('off')
plt.savefig('model_setup.png')
elif result_type == 'clusters' and self.results and 'tectonic_clusters' in self.results:
if 'pca_features' in self.integrated_data:
pca_data = self.integrated_data["pca_features"]
clusters = DBSCAN(eps=0.5, min_samples=5).fit_predict(pca_data)
scatter = plt.scatter(pca_data[:, 0], pca_data[:, 1], c=clusters, cmap='viridis', alpha=0.5)
plt.title("Tectonic Clusters and High-Risk Zones")
plt.xlabel("PC1")
plt.ylabel("PC2")
plt.colorbar(label="Cluster ID")
plt.savefig('tectonic_clusters.png')
else:
print("No PCA features available for cluster visualization.")
else:
print(f"No visualization available for result type: {result_type}")
plt.close()
return
plt.show()
print(f"Visualization saved as {result_type}.png")
def run_pipeline(self,
model_name: str = 'Default_Model',
config: Optional[Dict] = None) -> Dict:
"""Execute complete analysis pipeline with configuration.
Args:
model_name: Name for the model.
config: Pipeline configuration dictionary.
Returns:
Final analysis results.
Raises:
Exception: If pipeline fails.
"""
config = config or {
'integration_method': 'pca',
'model_type': 'simulation',
'analysis_type': 'advanced'
}
print("Running GeoPollobDynamics pipeline...")
try:
self.integrate_data(method=config['integration_method'])
self.build_model(model_name, model_type=config['model_type'])
self.analyze_data(analysis_type=config['analysis_type'])
return self.results
except Exception as e:
print(f"Pipeline failed: {str(e)}")
raise
# Example Usage
if __name__ == "__main__":
try:
# Initialize with sample data
framework = GeoPollobDynamics()
framework.load_data(
seismic_data=np.random.rand(100, 3), # Simulated seismic traces
satellite_data=np.random.rand(100, 100, 3), # Simulated satellite imagery
geological_data=np.random.rand(50, 50) # Simulated geological survey
)
# Run complete pipeline
results = framework.run_pipeline(
model_name="Tectonic_Model_v5",
config={
'integration_method': 'pca',
'model_type': 'simulation',
'analysis_type': 'advanced'
}
)
# Display final results with interpretations
print("\nFinal Results:")
for k, v in results.items():
print(f"- {k}: {v['result']}")
print(f" Interpretation: {v['interpretation']}")
except Exception as e:
print(f"Framework error: {e}")
