Detecting the signal cycle of the deep Earth’s dynamic processes based on GRACE satellite and CHAOS-7 model data

The GRACE (Gravity Recovery and Climate Experiment) space mission recorded temporal variation characteristics of the global gravity field at decadal timescales. The gravity data have been shown to capture the dynamics of flows within the outer core and their effects on the core–mantle boundary. We first aim to remove global surface process gravity signals from the GRACE data. We then construct the global core magnetic field according to the CHAOS-7 model. Finally, we apply the blind source separation method to decompose the processed gravity signals and core magnetic signals and compute the power spectral density of the gravity and magnetic field signals by using the Lomb–Scargle periodogram approach. We have discovered a signal cycle (of ~6 years) in the principal components of the core magnetic and gravity signals, potentially as a result of deep Earth processes. The main principal components of the core magnetic and gravity signals reveal that the variation trends in the second-order time derivative of the core magnetic field are similar to those in the gravity field. After 2014, the second-order time derivative of the core magnetic field exhibited linear and rapid change characteristics, which were the same as the change in the gravity field and are consistent with existing research results.

Authors

Publication Details

Journal
Earth and Planetary Physics
Published
2025-11-21
DOI
https://doi.org/10.26464/epp2026016
Primary Topic
Geophysics and Gravity Measurements
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Detecting the signal cycle of the deep Earth’s dynamic processes based on GRACE satellite and CHAOS-7 model data

Yonggang Zhang, Zhengtao Wang, Shize Zhang, Ni GangHua
Earth and Planetary Physics
Geophysics and Gravity Measurements
article

Detecting the signal cycle of the deep Earth’s dynamic processes based on GRACE satellite and CHAOS-7 model data

Yonggang Zhang, Zhengtao Wang, Shize Zhang, Ni GangHua
article en

Abstract

The GRACE (Gravity Recovery and Climate Experiment) space mission recorded temporal variation characteristics of the global gravity field at decadal timescales. The gravity data have been shown to capture the dynamics of flows within the outer core and their effects on the core–mantle boundary. We first aim to remove global surface process gravity signals from the GRACE data. We then construct the global core magnetic field according to the CHAOS-7 model. Finally, we apply the blind source separation method to decompose the processed gravity signals and core magnetic signals and compute the power spectral density of the gravity and magnetic field signals by using the Lomb–Scargle periodogram approach. We have discovered a signal cycle (of ~6 years) in the principal components of the core magnetic and gravity signals, potentially as a result of deep Earth processes. The main principal components of the core magnetic and gravity signals reveal that the variation trends in the second-order time derivative of the core magnetic field are similar to those in the gravity field. After 2014, the second-order time derivative of the core magnetic field exhibited linear and rapid change characteristics, which were the same as the change in the gravity field and are consistent with existing research results.

Earth and Planetary PhysicsVol. 10(1)
National Natural Science Foundation of China
Openalex Percentile: Top 65%
Geophysics and Gravity Measurements
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.