This repository contains an implementation of a Long Short-Term Memory (LSTM) network tailored for analyzing data from the GRACE (Gravity Recovery and Climate Experiment) accelerometers. The GRACE mission provides valuable insights into Earth's gravitational field variations and their correlation with climate changes.
In this repository, you will find Python code and documentation that showcase the utilization of LSTM neural networks for processing and interpreting accelerometer data from the GRACE satellites. The LSTM architecture's ability to capture temporal dependencies makes it well-suited for analyzing the complex and time-sensitive measurements obtained from the GRACE accelerometers.
Key Features:
Preprocessing scripts for cleaning and formatting GRACE accelerometer data.
Implementation of an LSTM model using TensorFlow.
Training and evaluation pipelines with customizable hyperparameters.
Visualizations to illustrate the LSTM model's performance and its ability to extract meaningful patterns from accelerometer readings.
Example notebooks demonstrating how to load, process, train, and analyze GRACE accelerometer data using the LSTM model.
Contribution guidelines to encourage collaborative development and improvements.
Whether you're an Earth scientist, a data enthusiast, or a machine learning practitioner, this repository aims to facilitate the application of LSTM-based analysis to GRACE accelerometer data, fostering a deeper understanding of Earth's gravitational field dynamics and its implications for climate research.
Feel free to fork this repository, experiment with different architectures, contribute enhancements, and engage in discussions to advance our understanding of Earth's gravitational variations and their environmental significance. Your contributions are highly appreciated!
Additional Information Available in this ArXiv Paper: