Inversion of potential-field data involve large and dense coefficient matrices that often exceed the limitations of physical memory in commonly available computers. The repeated multiplications of such matrices to vectors during inversion require an immense amount of CPU power. These two factors pose a significant challenge to solving large-scale problems in practice and can render many realist...

We develop a method for improving the parallel scalability of the recently developed parallel selected inversion algorithm [Jacquelin, Lin and Yang 2014], named PSelInv, on massively parallel distributed memory machines. In the PSelInv method, we compute selected elements of the inverse of a sparse matrix A that can be decomposed as A = LU , where L is lower triangular and U is upper triangular...

Seismic inversion is a method that extracts acoustic impedance data from the seismic traces. Source wavelets are band-limited, and thus seismic traces do not contain low and high frequency information. Therefore, there is a serious problem when the deterministic seismic inversion is applied to real data and the result of deterministic inversion is smooth. Low frequency component is obtained fro...

Resolving the discrepancies between NEE estimates based upon (1) ground studies and (2) atmospheric inversion results, demands increasingly sophisticated techniques. In this paper we present a high-resolution inversion based upon a regional meteorology model (RAMS) and an underlying biosphere (SiB3) model, both running on an identical 40 km grid over most of North America. Current operational s...

The objective of seismic full waveform inversion (FWI) is to estimate a model of the subsurface that minimizes the difference between recorded seismic data and synthetic data simulated for that model. Although FWI can yield accurate and high-resolution models, multiple problems have prevented widespread application of this technique in practice. First, FWI is computationally intensive, in part ...

The aim of seismic inversion is mapping all of the subsurface structures from seismic data. Due to the band-limited nature of the seismic data, it is difficult to find a unique solution for seismic inversion. Deterministic methods of seismic inversion are based on try and error techniques and provide a smooth map of elastic properties, while stochastic methods produce high-resolution maps of el...

The northern wetland methane emission estimates have large uncertainties. Inversion models are a qualified method to estimate the fluxes and emissions in latitudes but when atmospheric observations sparse, only as good their priori estimates. Thus, improving is competent way reduce uncertainties enhance sparsely sampled regions. Here, we use novel integrate remote sensing soil freeze/thaw (F/T)...

With sparse and uneven site distribution, Global Positioning System (GPS) data is just barely able to infer low-degree coefficients in the surface mass field. The unresolved higher-degree coefficients turn out to introduce aliasing errors into the estimates of low-degree coefficients. To reduce the aliasing errors, the optimal truncation degree should be employed. Using surface displacements si...

projection and Fourier methods [1, 2] are used to reconstruct high resolution images in a variety of applications In this work, we develop statistically based algorithms to reconstruct binary polygonal objects from sparse and noisy [1]. Although these methods produce high quality images, tomographic-based observation data. Traditional approaches they require a large number of projections and a ...