Kyoto University · 지구·행성과학
Peiliang Xu 교수의 연구실은 주로 지구물리학적 역문제 해결을 위한 정규화 기반 수치해석 및 통계적 추정 기법에 중점을 두고 있습니다. 특히 불안정한 역문제(예: 중력장 이상 추정, 하향연속 문제)에 대한 안정적이고 정확한 해를 도출하기 위해 Tikhonov 정규화, 리지 회귀, 일반화 교차검증(GCV), L-곡선 기반 정규화 파rameter 결정법 등을 응용합니다. 또한, 데이터 오염에 강한 저항성과 높은 효율성을 동시에 확보하는 신호-제약형 강건추정법, 정수형 모델의 통계적 해석 및 Voronoi 세포 기반 정수 추정 이론 등 정밀측위(GPS) 및 공간지질측정 기법의 이론적 기초를 다룹니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Truncated singular value decomposition (TSVD) techniques have been widely used in inversion. The method of truncation determines the quality of a truncated SVD solution, but truncation has often been done arbitrarily. The first workable criterion for truncation was based on F-statistical testing, but has only rarely been used in geophysical inversion. Recently, the L-curve approach was proposed for the same purpose, and soon found many applications in interdisciplinary inverse problems. Up to th
The findings of this paper are summarized as follows: (1) We propose a sign-constrained robust estimation method, which can tolerate 50% of data contamination and meanwhile achieve high, least-squares-comparable efficiency. Since the objective function is identical with least squares, the method may also be called sign-constrained robust least squares. An iterative version of the method has been implemented and shown to be capable of resisting against more than 50% of contamination. As a by-prod
Determination of surface gravity anomalies from gradiometric observables poses one of the downward continuation problems in physical geodesy. The unstable characteristics of the problem have been well exposed on the base of spectral analysis. The purpose of this paper is to develop a new approach to obtaining the best resolutions of mean gravity anomalies in terms of mean square errors, biases and error variances from the point of view of biased estimation. The three algorithms of ridge regressi
The method of generalized cross-validation (GCV) has been widely used to determine the regularization parameter, because the criterion minimizes the average predicted residuals of measured data and depends solely on data. The data-driven advantage is valid only if the variance-covariance matrix of the data can be represented as the product of a given positive definite matrix and a scalar unknown noise variance. In practice, important geophysical inverse ill-posed problems have often been solved
Abtract Crustal deformation on land can now be measured and monitored routinely and precisely using space geodetic techniques. The same is not true of the seafloor, which covers about 70 percent of the earth surface, and is critical in terms of plate tectonics, submarine volcanism, and earthquake mechanisms of plate boundary types. We develop new data processing strategies for quantifying crustal deformation at the ocean floor: single- and double-difference methods. Theoretically, the single dif
Although real-valued linear models, whether or not of full rank, have been thoroughly investigated and are well documented, very little is known about statistical and probabilistic aspects of a mixed integer linear model, which arose from space geodesy and serves as the standard starting model for precise positioning using the global positioning system (GPS). Voronoi cells play a fundamental role in the least squares estimation of the integer unknowns of the model. In this paper, we first develo