A high-performance, distributed solution for processing large-scale seismic data using Ray for parallel processing and Delta Lake for efficient storage and analytics.
This solution accelerator provides an efficient way to process 3D seismic SEGY files by leveraging Ray's distributed computing capabilities to split and process data in parallel, then storing the results in Delta format for advanced analytics and further processing.
- Distributed Processing: Uses Ray to split SEGY files into chunks and process them in parallel across multiple worker nodes
- Scalable Architecture: Configurable worker nodes and CPU allocation for optimal resource utilization
- Delta Lake Integration: Outputs processed data in Delta format for ACID transactions and time travel capabilities
- Memory Efficient: Processes data in configurable chunks to handle large seismic datasets
- Databricks Optimized: Designed to run efficiently on Databricks with integrated utilities
SEGY Files → Ray Distributed Processing → Parquet Files → Delta Lake → Analytics
- Ray Cluster Setup: Configures distributed computing cluster with specified worker nodes and resources
- Parallel Processing: Splits SEGY files into chunks and processes them concurrently across Ray workers
- Data Flattening: Converts 3D seismic data into flattened format suitable for analytics
- Parquet Output: Saves processed data as Parquet files for efficient storage
- Delta Integration: Parquet files are then ingested into Delta Lake for advanced analytics
segy_processor.py:Handles SEGY file metadata extraction and chunk-based processing- Implements Ray remote functions for distributed execution
segy_to_parquet.py: Databricks notebook for orchestrating the entire processing pipeline- Configurable parameters for input/output paths, chunk sizes, and Ray cluster settings
- Python 3.8+
- Ray 2.0+
- Databricks environment (for notebook execution)
- Access to SEGY files and Delta Lake storage
pip install -r requirements.txt