Planetary Seismology: Nearly 3 years on Mars, and a return to the Moon
No abstract provided
Yana, Charles↗
Engineering topics
Kedar, S.
Publications and source records attributed to Kedar, S..
The Lunar Geophysical Network Mission
Overarching Principles: Must be better than Apollo (coverage, duration, instrument performance); Learn from the Apollo experience. Lunar Geophysical Network (LGN) New Frontiers (NF)-class mission, as part of the NF-5 call. “This mission consists of several identical landers distributed across the lunar surface, each carrying geophysical instrumentation. The primary science objectives are to characterize the Moon’s internal structure, seismic activity, global heat flow budget, bulk composition, & magnetic field.” Global distribution of multiple stations. Each station should contain a seismometer, heat flow probe, electromagnetic sounder, laser retroreflector (lunar nearside). Each station must be long-lived (e.g., approximately10 years)to allow other stations (from other countries?) to be integrated with the anchor nodes to form the International Lunar Network. Why LGN? Planetary Science: Moon represents an end-member in planetary evolution (large small body, small rocky planet); Primary planetary differentiation preserved; Key to understanding terrestrial planet initial differentiation. Lunar Science: Heat flow probes yield crustal heat budget estimates; Combined with EMS (ElectroMagnetic Sounding), the temperature profile of the deep interior can be modeled along with mineralogy; Seismic and LLR (Lunar Laser Ranging) data also yield structure and compositional information of the lunar interior; High fidelity data from LGN would enhance the usefulness of the GRAIL (Gravity Recovery and Interior Laboratory) and SELENE (Selenological and Engineering Explorer) gravity data. Human Exploration: LGN must be established prior to renewed human lunar activity - we do not know the exact locations or causes of the shallow moonquakes (SMQs) - the largest magnitude seismic events recorded by Apollo (1 event per year of magnitude greater than or equal to 5); Establishing surface infrastructure near SMQ epicenters must be avoided.
Neal, C. R.↗
Spatiotemporal Filtering Using Principal Component Analysis and Karhunen-Loeve Expansion Approaches for Regional GPS Network Analysis
Spatial filtering is an effective way to improve the precision of coordinate time series for regional GPS networks by reducing so-called common mode errors, thereby providing better resolution for detecting weak or transient deformation signals. The commonly used approach to regional filtering assumes that the common mode error is spatially uniform, which is a good approximation for networks of hundreds of kilometers extent, but breaks down as the spatial extent increases. A more rigorous approach should remove the assumption of spatially uniform distribution and let the data themselves reveal the spatial distribution of the common mode error. The principal component analysis (PCA) and the Karhunen-Loeve expansion (KLE) both decompose network time series into a set of temporally varying modes and their spatial responses. Therefore they provide a mathematical framework to perform spatiotemporal filtering.We apply the combination of PCA and KLE to daily station coordinate time series of the Southern California Integrated GPS Network (SCIGN) for the period 2000 to 2004. We demonstrate that spatially and temporally correlated common mode errors are the dominant error source in daily GPS solutions. The spatial characteristics of the common mode errors are close to uniform for all east, north, and vertical components, which implies a very long wavelength source for the common mode errors, compared to the spatial extent of the GPS network in southern California. Furthermore, the common mode errors exhibit temporally nonrandom patterns.
displacement↗