LSTM-Based Forecasting Model for GRACE Accelerometer Data

2023-08-16 · Neda Darbeheshti, Elahe Moradi

The Gravity Recovery and Climate Experiment (GRACE) satellite mission, spanning from 2002 to 2017, has provided a valuable dataset for monitoring variations in Earth's gravity field, enabling diverse applications in geophysics and hydrology. The mission was followed by GRACE Follow-On in 2018, continuing data collection efforts. The monthly Earth gravity field, derived from the integration different instruments onboard satellites, has shown inconsistencies due to various factors, including gaps in observations for certain instruments since the beginning of the GRACE mission. With over two decades of GRACE and GRACE Follow-On data now available, this paper proposes an approach to fill the data gaps and forecast GRACE accelerometer data. Specifically, we focus on accelerometer data and employ Long Short-Term Memory (LSTM) networks to train a model capable of predicting accelerometer data for all three axes. In this study, we describe the methodology used to preprocess the accelerometer data, prepare it for LSTM training, and evaluate the model's performance. Through experimentation and validation, we assess the model's accuracy and its ability to predict accelerometer data for the three axes. Our results demonstrate the effectiveness of the LSTM forecasting model in filling gaps and forecasting GRACE accelerometer data.

📄 PDF Abstract BibTeX arXiv:2308.08621

Code (1)

darbeheshti/lstm-based-analysis-for-grace-accelerometers 공식 구현 tf

Tasks

Geophysicsmodel

Methods 이 논문이 사용한 방법론

Tanh Activation 설명 없음

Sigmoid Activation 설명 없음

Gravity Gravity is a kinematic approach to optimization based on gradients.

LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…

Focus 설명 없음

GRACE-FO accelerometers and data processing

2018-03-20 · 2018 2018 3 · Phuong-Anh Huynh, Françoise Liorzou, Damien Boulanger, Bruno Christophe 외

The GRACE FO mission, led by the JPL (Jet Propulsion Laboratory), is an Earth-orbiting gravity mission, continuation of the GRACE mission, which will produce an accurate model of the Earth’s gravity field variation pro…

Machine Learning Algorithms for Transplanting Accelerometer Observations in Future Satellite Gravimetry Missions

2025-08-05 · Mohsen Romeshkani, Jürgen Müller, Sahar Ebadi, Alexey Kupriyanov 외 arxiv

Accurate and continuous monitoring of Earth's gravity field is essential for tracking mass redistribution processes linked to climate variability, hydrological cycles, and geodynamic phenomena. While the GRACE and GRACE …

Reconstructing GRACE Terrestrial Water Storage with Spatio-Temporal Graph Neural Networks: An Application to South America

2026-06-22 · Lukas Arzoumanidis, Lara Johannsen, Klara Middendorf, Annette Eicker 외 arxiv

Terrestrial water storage (TWS) integrates snow, soil moisture, surface water, and groundwater and is a key indicator of how climate variability and human activity reshape the global water cycle. The GRACE and GRACE-FO s…

Graph Neural Network

Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data

2021-02-28 · Bolu Oluwalade, Sunil Neela, Judy Wawira, Tobiloba Adejumo 외

In recent years, human activity recognition has garnered considerable attention both in industrial and academic research because of the wide deployment of sensors, such as accelerometers and gyroscopes, in products such …

Activity Recognition Human Activity Recognition

Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks

2022-06-03 · Chrisogonas O. Odhiambo, Sanjoy Saha, Corby K. Martin, Homayoun Valafar

The use of sensors available through smart devices has pervaded everyday life in several applications including human activity monitoring, healthcare, and social networks. In this study, we focus on the use of smartwatch…

Activity Recognition Human Activity Recognition Time Series Time Series Analysis