基于神经网络的地质勘测反分析研究

针对地质勘查中,土的力学参数的确定及土的分类这两类复杂问题,根据反问题理论的基本原理,提出了一种基于回归分析与RBF神经网络结合的新型智能方法,建立了从土的力学参数估计到模型分类的完整智能化分析系统。考虑到土的物理参数测定方法比较简单,且实测变异性小,而力学参数实测变异性大的特点,利用RBF神经网络的数值逼近的特性,建立了神经网络模型来逼近两者之间的函数关系,可以有效地反演力学参数。同时,利用RBF神经网络所具有的模式识别功能,为地质勘察中土层划分提供依据。通过对黄石地区岩土勘查资料的分析与预测表明,该方法简捷有效。     

中国海岸带环境问题的研究综述

简要介绍了海岸带的概念和中国海岸带的分布,从地质灾害、气候灾害、生态破坏、环境污染和全球变化等方面概述了中国海岸带目前和将要面临的环境问题,对中国海岸带的可持续发展提出了几点建议。     

Fully nonlinear wave-body interactions with surface-piercing bodies

Fully nonlinear wave-body interactions for stationary surface-piercing single and double bodies are studied by a potential-theory-based fully nonlinear 2D numerical wave tank (NWT). The NWT was developed in time domain by using boundary element method (BEM) with constant panels. MEL free surface treatment and Runge–Kutta fourth-order time integration with smoothing scheme was used for free-surface time simulation. The acceleration-potential scheme is employed to obtain accurate time derivative of velocity potential. Using the steady part of nonlinear force time histories, mean and a series of higher-harmonic force components are calculated and compared with the experimental and numerical results of other researchers. The slow-decaying second-harmonic vertical forces are investigated with particle velocities and corresponding body pressure. Typical patterns of two-body interactions, shielding effect, and the pumping/sloshing modes of water column in various gap distances are investigated. The pumping mode in low frequencies is demonstrated by the comparison of velocity magnitudes.     

Effects of the sea floor topography on the 1D inversion of time‐domain marine controlled source electromagnetic data

Time‐domain marine controlled source electromagnetic methods have been used successfully for the detection of resistive targets such as hydrocarbons, gas hydrate, or marine groundwater aquifers. As the application of time‐domain marine controlled source electromagnetic methods increases, surveys in areas with a strong seabed topography are inevitable. In these cases, an important question is whether bathymetry information should be included in the interpretation of the measured electromagnetic field or not. Since multi‐dimensional inversion is still not common in time‐domain marine controlled source electromagnetic methods, bathymetry effects on the 1D inversion of single‐offset and multi‐offset joint inversions of time‐domain controlled source electromagnetic methods data are investigated. We firstly used an adaptive finite element algorithm to calculate the time‐domain controlled source electromagnetic methods responses of 2D resistivity models with seafloor topography. Then, 1D inversions are applied on the synthetic data derived from marine resistivity models, including the topography in order to study the possible topography effects on the 1D interpretation. To evaluate the effects of topography with various steepness, the slope angle of the seabed topography is varied in the synthetic modelling studies for deep water (air interaction is absent or very weak) and shallow water (air interaction is dominant), respectively. Several different patterns of measuring configurations are considered, such as the systems adopting nodal receivers and the bottom‐towed system. According to the modelling results for deep water when air interaction is absent, the 2D topography can distort the measured electric field. The distortion of the data increases gradually with the enlarging of the topography's slope angle. In our test, depending on the configuration, the seabed topography does not affect the 1D interpretation significantly if the slope angle is less or around 10°. However, if the slope angle increases to 30° or more, it is possible that significant artificial layers occur in inversion results and lead to a wrong interpretation. In a shallow water environment with seabed topography, where the air interaction dominates, it is possible to uncover the true subsurface resistivity structure if the water depth for the 1D inversion is properly chosen. In our synthetic modelling, this scheme can always present a satisfactory data fit in the 1D inversion if only one offset is used in the inversion process. However, the determination of the optimal water depth for a multi‐offset joint inversion is challenging due to the various air interaction for different offsets.     

Estimation of a non-stationary prior covariance from seismic data

Non-stationarity in statistical properties of the subsurface is often ignored. In a classical linear Bayesian inversion setting of seismic data, the prior distribution of physical parameters is often assumed to be stationary. Here we propose a new method of handling non-stationarity in the variance of physical parameters in seismic data. We propose to infer the model variance prior to inversion using maximum likelihood estimators in a sliding window approach. A traditional, and a localized shrinkage estimator is defined for inferring the prior model variance. The estimators are assessed in a synthetic base case with heterogeneous variance of the acoustic impedance in a zero-offset seismic cross section. Subsequently, this data is inverted for acoustic impedance using a non-stationary model set up with the inferred variances. Results indicate that prediction as well as posterior resolution is greatly improved using the non-stationary model compared with a common prior model with stationary variance. The localized shrinkage predictor is shown to be slightly more robust than the traditional estimator in terms of amplitude differences in the variance of acoustic impedance and size of local neighbourhood. Finally, we apply the methodology to a real data set from the North Sea basin. Inversion results show a more realistic posterior model than using a conventional approach with stationary variance.     

Imaging of elastic seismic data by least-squares reverse time migration with weighted L2-norm multiplicative and modified total-variation regularizations

Least-squares reverse time migration has the potential to yield high-quality images of the Earth. Compared with acoustic methods, elastic least-squares reverse time migration can effectively address mode conversion and provide velocity/impendence and density perturbation models. However, elastic least-squares reverse time migration is an ill-posed problem and suffers from a lack of uniqueness; further, its solution is not stable. We develop two new elastic least-squares reverse time migration methods based on weighted L2-norm multiplicative and modified total-variation regularizations. In the proposed methods, the original minimization problem is divided into two subproblems, and the images and auxiliary variables are updated alternatively. The method with modified total-variation regularization solves the two subproblems, a Tikhonov regularization problem and an L2-total-variation regularization problem, via an efficient inversion workflow and the split-Bregman iterative method, respectively. The method with multiplicative regularization updates the images and auxiliary variables by the efficient inversion workflow and nonlinear conjugate gradient methods in a nested fashion. We validate the proposed methods using synthetic and field seismic data. Numerical results demonstrate that the proposed methods with regularization improve the resolution and fidelity of the migration profiles and exhibit superior anti-noise ability compared with the conventional method. Moreover, the modified-total-variation-based method has marginally higher accuracy than the multiplicative-regularization-based method for noisy data. The computational cost of the proposed two methods is approximately the same as that of the conventional least-squares reverse time migration method because no additional forward computation is required in the inversion of auxiliary variables.     

Towards large-scale stochastic refraction tomography: a comparison of three evolutionary algorithms

The main goal of this study is to assess the potential of evolutionary algorithms to solve highly non-linear and multi-modal tomography problems (such as first arrival traveltime tomography) and their abilities to estimate reliable uncertainties. Classical tomography methods apply derivative-based optimization algorithms that require the user to determine the value of several parameters (such as regularization level and initial model) prior to the inversion as they strongly affect the final inverted model. In addition, derivative-based methods only perform a local search dependent on the chosen starting model. Global optimization methods based on Markov chain Monte Carlo that thoroughly sample the model parameter space are theoretically insensitive to the initial model but turn out to be computationally expensive. Evolutionary algorithms are population-based global optimization methods and are thus intrinsically parallel, allowing these algorithms to fully handle available computer resources. We apply three evolutionary algorithms to solve a refraction traveltime tomography problem, namely the differential evolution, the competitive particle swarm optimization and the covariance matrix adaptation–evolution strategy. We apply these methodologies on a smoothed version of the Marmousi velocity model and compare their performances in terms of optimization and estimates of uncertainty. By performing scalability and statistical analysis over the results obtained with several runs, we assess the benefits and shortcomings of each algorithm.     

First‐arrival traveltime tomography with modified total‐variation regularization

First‐arrival traveltime tomography is a robust tool for near‐surface velocity estimation. A common approach to stabilizing the ill‐posed inverse problem is to apply Tikhonov regularization to the inversion. However, the Tikhonov regularization method recovers smooth local structures while blurring the sharp features in the model solution. We present a first‐arrival traveltime tomography method with modified total‐variation regularization to preserve sharp velocity contrasts and improve the accuracy of velocity inversion. To solve the minimization problem of the new traveltime tomography method, we decouple the original optimization problem into the two following subproblems: a standard traveltime tomography problem with the traditional Tikhonov regularization and a L₂ total‐variation problem. We apply the conjugate gradient method and split‐Bregman iterative method to solve these two subproblems, respectively. Our synthetic examples show that the new method produces higher resolution models than the conventional traveltime tomography with Tikhonov regularization, and creates less artefacts than the total variation regularization method for the models with sharp interfaces. For the field data, pre‐stack time migration sections show that the modified total‐variation traveltime tomography produces a near‐surface velocity model, which makes statics corrections more accurate.     

Linear inversion via the discrete wavelet transform pseudoinverse

Classical least‐squares techniques (Moore–Penrose pseudoinverse) are covariance based and are therefore unsuitable for the solution of very large‐scale linear systems in geophysical inversion due to the need of diagonalisation. In this paper, we present a methodology to perform the geophysical inversion of large‐scale linear systems via the discrete wavelet transform. The methodology consists of compressing the linear system matrix using the interesting properties of covariance‐free orthogonal transformations, to design an approximation of the Moore–Penrose pseudoinverse. We show the application of the discrete wavelet transform pseudoinverse to well‐conditioned and ill‐conditioned linear systems. We applied the methodology to a general‐purpose linear problem where the system matrix has been generated using geostatistical simulation techniques and also to a synthetic 2D gravimetric problem with two different geological set‐ups, in the noise‐free and noisy cases. In both cases, the discrete wavelet transform pseudoinverse can be applied to the original linear system and also to the linear systems of normal equations and minimum norm. The results are compared with those obtained via the Moore–Penrose and the discrete cosine transform pseudoinverses. The discrete wavelet transform and the discrete cosine transform pseudoinverses provide similar results and outperform the Moore–Penrose pseudoinverse, mainly in the presence of noise. In the case of well‐conditioned linear systems, this methodology is more efficient when applied to the least‐squares system and minimum norm system due to their higher condition number that allows for a more efficient compression of the system matrix. Also, in the case of ill‐conditioned systems with very high underdetermined character, the application of the discrete cosine transform to the minimum norm solution provides very good results. Both solutions might differ on their regularity, depending on the wavelet family that is adopted. These methods have a general character and can be applied to solve any linear inverse problem arising in technology, particularly in geophysics, and also to non‐linear inversion by linearisation of the forward operator.     

Uncertainties in the geographic context of health behaviors: a study of substance users’ exposure to psychosocial stress using GPS data

ABSTRACT

This study examined how contextual areas defined and operationalized differently may lead to different exposure estimates. Substance users’ exposures to environmental stress (in terms of two variables: community social economic status and crime) were assessed from global positioning systems (GPS) data. Participants were 47 outpatients with substance use disorders admitted for methadone maintenance at a research clinic in Baltimore, Maryland. From 35.2 million GPS tracking points, we compared 7 different methods for defining activity space. The different methods yielded different exposure estimates, which would lead to different conclusions in studies using only one method. These results have important implications for future research on the effect of contextual influences on health behaviors and outcomes: whether a study observes any significant influence of an environmental factor on health may depend on what contextual units are used to assess individual exposure.