Optimal orbits for temporal gravity recovery regarding temporal aliasing

2013 ◽  
Vol 88 (2) ◽  
pp. 113-126 ◽  
Author(s):  
Michael Murböck ◽  
Roland Pail ◽  
Ilias Daras ◽  
Thomas Gruber
Keyword(s):  
Optimal Orbits ◽  
Gravity Recovery ◽  
Temporal Gravity

On the separation of co- and post-seismic signals due to large earthquakes from GRACE observations

2020 ◽  
Author(s):  
Jin Li ◽  
Jianli Chen ◽  
Song-Yun Wang ◽  
Lu Tang ◽  
Xiaogong Hu
Keyword(s):  
Time Series ◽  
Seismic Signal ◽  
Seismic Signals ◽  
Fitting Method ◽  
Short Period ◽  
Gravity Recovery ◽  
Andaman Earthquake ◽  
Large Earthquakes ◽  
Temporal Gravity

<p>Satellite gravimetry observations from GRACE (Gravity Recovery and Climate Experiment) and GRACE Follow-On are widely used to study the co-seismic and post-seismic deformations caused by large earthquakes. Temporal gravity changes from GRACE provide good constraints to investigate the fault slips of large earthquakes especially for oceanic areas. However, reliable retrieval of seismic signals is still challenging due to large uncertainties and limited spatial and temporal resolutions of GRACE observations. To extract the co- and post-seismic signals from GRACE, the time series fitting method based on least squares is commonly used. In the time series fitting, the earthquake occurrence time parameter (t0) is usually set at the mid-month point, since most available GRACE time-variable data are monthly solutions. Nevertheless, a lot of large earthquakes did not occur exactly at mid-month. By simulative tests, we demonstrate that the commonly used mid-month approximation for the fitting parameter t0 can cause noticeable bias for the seismic signal extraction. The several-days deviation in the parameter t0 leads to obvious difference for the time series fitting of seismic signals, since the post-seismic changes are rapid and significant within a short period after the earthquake. With the case study of the 2004 Mw9.1 Sumatra-Andaman earthquake (which occurred on December 26), we indicate that the bias due to the commonly used mid-month t0 approximation reaches above 10 percent amplitude of the extracted co-seismic signals. Thus the exact date for the fitting parameter t0 should be used for more reliable separation of the co- and post-seismic signals from GRACE observations.</p>

Gravity Recovery and Interior Laboratory Simulations of Static and Temporal Gravity Field

2012 ◽  
Vol 49 (2) ◽  
pp. 390-400 ◽  
Author(s):  
Ryan S. Park ◽  
Sami W. Asmar ◽  
Eugene G. Fahnestock ◽  
Alex S. Konopliv ◽  
Wenwen Lu ◽  
...  
Keyword(s):  
Gravity Field ◽  
Laboratory Simulations ◽  
Gravity Recovery ◽  
Temporal Gravity

The re-analysis on the raw data processing of KBR and LRI on GRACE-FO

2021 ◽  
Author(s):  
Yihao Yan ◽  
Changqing Wang ◽  
Vitali Müller ◽  
Min Zhong ◽  
Lei Liang ◽  
...  
Keyword(s):  
Gravity Field ◽  
Science Data ◽  
Raw Data ◽  
Gravity Field Recovery ◽  
Offset Correction ◽  
Instrument Noise ◽  
Time Correction ◽  
Gravity Recovery ◽  
Temporal Gravity ◽  
Good Agreement

<div> <p>The KBR (K-Band ranging instrument) and LRI (Laser Interferometer) are used to measure the distance variations between the twin spacecraft, which is one of the most important observations used for temporal gravity field recovery. The data pre-processing from raw or so-called Level-1A into the Level-1B format, which is suited for gravity field recovery, is a key step. Although Level-1B files are made publicly available by the GRACE-FO Science Data System (SDS), it has been shown that alternative Level-1B datasets may yield improved the results of gravity field<sup>[1]</sup>. Investigations of the pre-processing may allow us to improve the gravity recovery strategy and are essential to support developments of gravimetric satellite missions in China, such as TianQin-2 project. The pre-processing normally includes the time-tag synchronization, filtering and resampling, and other corrections, e.g. light-time correction for both instruments and antenna offset correction for KBR. We re-processed the Level-1A data of KBR and LRI to the Level1B using code developed at IGG/Wuhan. The results show good agreement in case of the RL04 KBR data, i.e. the differences between IGG-KBR1B and SDS-KBR1B are about three orders of magnitude lower than the instrument noise level for KBR. For the LRI, we found that phase jumps are not removed completely in the SDS-LRI1B products. As shown by Abich<sup>[2]</sup>, these phase jumps in the LRI phase observations are mainly coincident with thruster activations. Our work will analyze the impacts of different processing methods of the raw data on post-fit residuals and the gravity field recovery based on IGG-KBR1B and IGG-LRI1B datasets.</p> <p> </p> <div> <p>[1] Wiese, D.: SDS Level-2/-3 JPL, GRACE/GRACE-FO Science Team Meeting 2020, online, 27 October–29 Oct 2020, GSTM2020-75, https://doi.org/10.5194/gstm2020-75, 2020.</p> <p>[2]    Abich K, Abramovici A, Amparan B, et al. In-Orbit Performance of the GRACE Follow-on Laser Ranging Interferometer [J]. Phys Rev Lett, 2019, 123(3): 031101, https://doi.org/10.1103/PhysRevLett.123.031101.</p> </div> </div><p> </p>

scholarly journals Study of water storage variations at the Pantanal wetlands area from GRACE monthly mass grids

2019 ◽  
Vol 9 (1) ◽  
pp. 133-143
Author(s):  
Ayelen Pereira ◽  
Cecilia Cornero ◽  
Ana C. O. C. Matos ◽  
M. Cristina Pacino ◽  
Denizar Blitzkow
Keyword(s):  
Water Mass ◽  
Spatial Scales ◽  
Water Storage ◽  
Transport Processes ◽  
Satellite Gravity ◽  
The North ◽  
Paraguay River ◽  
Gravity Recovery ◽  
Grace Mission ◽  
Phase Differences

Abstract The continental water storage is significantly in-fluenced by wetlands, which are highly affected by climate change and anthropogenic influences. The Pantanal, located in the Paraguay river basin, is one of the world’s largest and most important wetlands because of the environmental biodiversity that represents. The satellite gravity mission GRACE (Gravity Recovery And Climate Experiment) provided until 2017 time-variable Earth’s gravity field models that reflected the variations due to mass transport processes-like continental water storage changes-which allowed to study environments such as wetlands, at large spatial scales. The water storage variations for the period 2002-2016, by using monthly land water mass grids of Total Water Storage (TWS) derived from GRACE solutions, were evaluated in the Pantanal area. The capability of the GRACE mission for monitoring this particular environment is analyzed, and the comparison of the water mass changes with rainfall and hydrometric heights data at different stations distributed over the Pantanal region was carried out. Additionally, the correlation between the TWS and river gauge measurements, and the phase differences for these variables, were also evaluated. Results show two distinct zones: high correlations and low phase shifts at the north, and smaller correlation values and consequently significant phase differences towards the south. This situation is mainly related to the hydrogeological domains of the area.

scholarly journals Temporal gravity model for important node identification in temporal networks

2021 ◽  
Vol 147 ◽  
pp. 110934
Author(s):  
Jialin Bi ◽  
Ji Jin ◽  
Cunquan Qu ◽  
Xiuxiu Zhan ◽  
Guanghui Wang ◽  
...  
Keyword(s):  
Gravity Model ◽  
Temporal Networks ◽  
Temporal Gravity

scholarly journals Spatio-Temporal Patterns of Mass Changes in Himalayan Glaciated Region from EOF Analyses of GRACE Data

2021 ◽  
Vol 13 (2) ◽  
pp. 265
Author(s):  
Harika Munagapati ◽  
Virendra M. Tiwari
Keyword(s):  
Temperature Anomaly ◽  
Mass Gain ◽  
Snow Accumulation ◽  
Empirical Orthogonal Functions ◽  
Total Water ◽  
Orthogonal Functions ◽  
Grace Data ◽  
Grace Satellite ◽  
Spatio Temporal ◽  
Gravity Recovery

The nature of hydrological seasonality over the Himalayan Glaciated Region (HGR) is complex due to varied precipitation patterns. The present study attempts to exemplify the spatio-temporal variation of hydrological mass over the HGR using time-variable gravity from the Gravity Recovery and Climate Experiment (GRACE) satellite for the period of 2002–2016 on seasonal and interannual timescales. The mass signal derived from GRACE data is decomposed using empirical orthogonal functions (EOFs), allowing us to identify the three broad divisions of HGR, i.e., western, central, and eastern, based on the seasonal mass gain or loss that corresponds to prevailing climatic changes. Further, causative relationships between climatic variables and the EOF decomposed signals are explored using the Granger causality algorithm. It appears that a causal relationship exists between total precipitation and total water storage from GRACE. EOF modes also indicate certain regional anomalies such as the Karakoram mass gain, which represents ongoing snow accumulation. Our causality result suggests that the excessive snowfall in 2005–2008 has initiated this mass gain. However, as our results indicate, despite the dampening of snowfall rates after 2008, mass has been steadily increasing in the Karakorum, which is attributed to the flattening of the temperature anomaly curve and subsequent lower melting after 2008.

scholarly journals Evaluation of GRACE/GRACE Follow-On Time-Variable Gravity Field Models for Earthquake Detection above Mw8.0s in Spectral Domain

2021 ◽  
Vol 13 (16) ◽  
pp. 3075
Author(s):  
Ming Xu ◽  
Xiaoyun Wan ◽  
Runjing Chen ◽  
Yunlong Wu ◽  
Wenbing Wang
Keyword(s):  
Signal To Noise Ratio ◽  
Time Variable ◽  
Model Errors ◽  
Signal To Noise ◽  
Time Variable Gravity ◽  
Earthquake Detection ◽  
Gravity Fields ◽  
Variable Gravity ◽  
Gravity Recovery ◽  
Gravity Field Models

This study compares the Gravity Recovery And Climate Experiment (GRACE)/GRACE Follow-On (GFO) errors with the coseismic gravity variations generated by earthquakes above Mw8.0s that occurred during April 2002~June 2017 and evaluates the influence of monthly model errors on the coseismic signal detection. The results show that the precision of GFO monthly models is approximately 38% higher than that of the GRACE monthly model and all the detected earthquakes have signal-to-noise ratio (SNR) larger than 1.8. The study concludes that the precision of the time-variable gravity fields should be improved by at least one order in order to detect all the coseismic gravity signals of earthquakes with M ≥ 8.0. By comparing the spectral intensity distribution of the GFO stack errors and the 2019 Mw8.0 Peru earthquake, it is found that the precision of the current GFO monthly model meets the requirement to detect the coseismic signal of the earthquake. However, due to the limited time length of the observations and the interference of the hydrological signal, the coseismic signals are, in practice, difficult to extract currently.

scholarly journals Analysis of Ocean Bottom Pressure Anomalies and Seismic Activities in the MedRidge Zone

2021 ◽  
Vol 13 (7) ◽  
pp. 1242
Author(s):  
Hakan S. Kutoglu ◽  
Kazimierz Becek
Keyword(s):  
Time Series ◽  
Mass Change ◽  
Ocean Bottom ◽  
Bottom Pressure ◽  
Vertical Movements ◽  
Ocean Bottom Pressure ◽  
Continuous Mass ◽  
Mediterranean Ridge Accretionary Complex ◽  
Gravity Recovery ◽  
Periodic Components

The Mediterranean Ridge accretionary complex (MAC) is a product of the convergence of Africa–Europe–Aegean plates. As a result, the region exhibits a continuous mass change (horizontal/vertical movements) that generates earthquakes. Over the last 50 years, approximately 430 earthquakes with M ≥ 5, including 36 M ≥ 6 earthquakes, have been recorded in the region. This study aims to link the ocean bottom deformations manifested through ocean bottom pressure variations with the earthquakes’ time series. To this end, we investigated the time series of the ocean bottom pressure (OBP) anomalies derived from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite missions. The OBP time series comprises a decreasing trend in addition to 1.02, 1.52, 4.27, and 10.66-year periodic components, which can be explained by atmosphere, oceans, and hydrosphere (AOH) processes, the Earth’s pole movement, solar activity, and core–mantle coupling. It can be inferred from the results that the OBP anomalies time series/mass change is linked to a rising trend and periods in the earthquakes’ energy time series. Based on this preliminary work, ocean-bottom pressure variation appears to be a promising lead for further research.

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