Combining discrete cosine transform and convolutional neural networks to speed up the Hamiltonian Monte Carlo inversion of pre-stack seismic data

Markov chain Monte Carlo algorithms are commonly employed for accurate uncertainty appraisals in non-linear inverse problems. The downside of these algorithms is the considerable number of samples needed to achieve reliable posterior estimations, especially in high-dimensional model spaces. To overcome this issue, the Hamiltonian Monte Carlo algorithm has recently been introduced to solve geophysical inversions. Different from classical Markov chain Monte Carlo algorithms, this approach exploits the derivative information of the target posterior probability density to guide the sampling of the model space. However, its main downside is the computational cost for the derivative computation (i.e. the computation of the Jacobian matrix around each sampled model). Possible strategies to mitigate this issue are the reduction of the dimensionality of the model space and/or the use of efficient methods to compute the gradient of the target density. Here we focus the attention to the estimation of elastic properties (P-, S-wave velocities and density) from pre-stack data through a non-linear amplitude versus angle inversion in which the Hamiltonian Monte Carlo algorithm is used to sample the posterior probability. To decrease the computational cost of the inversion procedure, we employ the discrete cosine transform to reparametrize the model space, and we train a convolutional neural network to predict the Jacobian matrix around each sampled model. The training data set for the network is also parametrized in the discrete cosine transform space, thus allowing for a reduction of the number of parameters to be optimized during the learning phase. Once trained the network can be used to compute the Jacobian matrix associated with each sampled model in real time. The outcomes of the proposed approach are compared and validated with the predictions of Hamiltonian Monte Carlo inversions in which a quite computationally expensive, but accurate finite-difference scheme is used to compute the Jacobian matrix and with those obtained by replacing the Jacobian with a matrix operator derived from a linear approximation of the Zoeppritz equations. Synthetic and field inversion experiments demonstrate that the proposed approach dramatically reduces the cost of the Hamiltonian Monte Carlo inversion while preserving an accurate and efficient sampling of the posterior probability.     

基于贝叶斯定理的地震危险性概率预测研究

由于地震孕育过程的复杂性和观测技术的局限性,不同地震观测资料表现出异常变化与后续较大地震的对应关系存在不确定性,因此对预测意见进行概率表达是一种科学恰当的做法。本文基于泊松分布的危险区背景地震概率预测和单项预测方法(包括测震、流体、形变、电磁等学科)的历史预测效能,采用贝叶斯定理计算得到单项预测方法的短期或年度地震危险概率预测结果,进而采用综合概率方法,给出基于多种单项预测方法的短期或年度地震危险概率预测结果。短期概率预测初步结果表明,2018年2～9月,中国大陆72%的5级以上地震都位于相对高概率预测区域。     

江苏中部淤泥质潮滩潮汐层理成因机理和风暴沉积判别标志

通过对江苏中部淤泥质潮滩潮汐纹层发育过程的实地观测,以及对所采样品在室内进行的落淤量的计算与颗粒的粒度、磨圆度分析等,揭示出江苏中部海岸淤泥质潮滩沉积中的潮汐层理的成因机理:毫米级薄砂泥互层层理为半日潮的产物,厘米级厚的厚砂泥互层层理为半月天文潮的产物。此外,通过对9711号台风引致的风暴潮影响前后的滩面进行了现场观测,发现江苏中部淤泥质潮滩上部存在一个即使在风暴潮期间也不发生侵蚀的地带。该带的风暴沉积与正常的潮滩沉积比较,具有粒度较粗,分选较差,磨圆稍好,递变现象明显,平行层理或波状交错层理发育比较典型的特征。     

Discard management: A spatial multi-criteria approach

Discard management needs to draw on scientific research and advice, usually supported by specific statistical modeling analysis. A wide range of statistical analysis methods were applied to fishery data in an attempt to distinguish factors that influence the species discard composition. While such approaches are important, they are still incomplete for disaggregating the economic and spatial-temporal factors for analyzing of this process and obtain a whole view of this issue. Our study aims to fill this gap by identifying, describing, and quantifying factors that influence discards of trawl fisheries using a multivariate approach based on five complementary aspects: “economic”, “vessel characteristics”, “spatial”, “temporal” and “environmental”. In addition, a spatial multi-criteria approach were used to investigate discard hot-spot areas using ecological criteria such as vulnerability and resilience of the discarded species. Using these ecological criteria will concentrate conservation efforts on the most relevant sites minimizing discards of a variety of potentially vulnerable species. This approach was applied to a case study of a multi-species demersal bottom trawl fisheries in north Spain, Cantabrian Sea (ICES area VIIIc). Results showed how spatial and economic factors highly affect species discard composition, identifying specific spatial-temporal discard hot-spots to be preferentially avoided by fishers. Mitigation measures for future fisheries management strategies should be implemented at multiple stages of the discarding process, both in the selection of the fishing grounds and the economic valorization of the discarded species.     

利用限制性最大似然方法和贝叶斯方法估计大菱鲆生长性状的遗传参数。

Bayesian and restricted maximum likelihood(REML) approaches were used to estimate the genetic parameters in a cultured turbot Scophthalmus maximus stock. The data set consisted of harvest body weight from 2 462progenies(17 months old) from 28 families that were produced through artificial insemination using 39 parent fish. An animal model was applied to partition each weight value into a fixed effect, an additive genetic effect, and a residual effect. The average body weight of each family, which was measured at 110 days post-hatching, was considered as a covariate. For Bayesian analysis, heritability and breeding values were estimated using both the posterior mean and mode from the joint posterior conditional distribution. The results revealed that for additive genetic variance, the posterior mean estimate( δ_a~2=9 320) was highest but with the smallest residual variance,REML estimates( δ_a~2=8 088) came second and the posterior mode estimate( δ_a~2=7 849) was lowest. The corresponding three heritability estimates followed the same trend as additive genetic variance and they were all high. The Pearson correlations between each pair of the three estimates of breeding values were all high,particularly that between the posterior mean and REML estimates(0.996 9). These results reveal that the differences between Bayesian and REML methods in terms of estimation of heritability and breeding values were small. This study provides another feasible method of genetic parameter estimation in selective breeding programs of turbot.     

东昆仑库木库里沙漠表层沉积物粒度特征、物源与沉积环境

对青藏高原北部世界海拔最高的库木库里沙漠周围冲积扇碎屑及沙漠表层沉积物粒度特征及元素组成研究结果显示：（1）库木库里沙漠表层沉积物以细砂为主,其次为极细砂,粉砂和中砂含量较少,有少量的黏粒,不含粗砂的组分,且粒度组成差异不大。（2）沉积物M_Z范围2.70~2.90 φ,σ₁范围0.80~1.10 φ,S_K1范围0.26~0.44,K_G范围2.27~3.62。（3）粒度主要呈近似对称的单峰分布,但细颗粒一侧有较细长的尾部。（4）沙漠表层沉积物元素组成与周围山麓冲洪积碎屑沉积物较为相似,尤其是沙漠西部的阿尔喀山北麓区域。（5）沉积物源判别表明,沙漠表层沉积物来源主要为阿尔喀山北麓及祁漫塔格山南麓碎屑沉积物,其形成环境主要为河流相沉积环境及浅湖相沉积环境。     

Learning soil parameters and updating geotechnical reliability estimates under spatial variability – theory and application to shallow foundations

ABSTRACT

Field data is commonly used to determine soil parameters for geotechnical analysis. Bayesian analysis allows combining field data with other information on soil parameters in a consistent manner. We show that the spatial variability of the soil properties and the associated measurements can be captured through two different modelling approaches. In the first approach, a single random variable (RV) represents the soil property within the area of interest, while the second approach models the spatial variability explicitly with a random field (RF). We apply the Bayesian concept exemplarily to the reliability assessment of a shallow foundation in a silty soil with spatially variable data. We show that the simpler RV approach is applicable in cases where the measurements do not influence the correlation structure of the soil property at the vicinity of the foundation. In other cases, it is expected to underestimate the reliability, and a RF model is required to obtain accurate results.     

Improved performance‐based seismic assessment of buildings by utilizing Bayesian statistics

This paper addresses two important issues of concern to practicing engineers and researchers alike in application of performance‐based seismic assessment (PBSA) methodology on buildings: (i) the number of ground motion records required to exercise PBSA—current practice (FEMA P‐58‐1) requires eleven or more pairs of motions for this purpose, and (ii) the time and effort associated with performing the number of nonlinear response history analyses required to exercise PBSA. We present a method for exercising of PBSA that employs classical linear modal analysis to develop a first estimate (i.e., a priori) of probability distribution of loss, followed by utilizing Bayesian statistics to update this estimate using estimates of loss obtained by utilizing a small number of nonlinear response history analyses of a detailed model of the building (i.e., posterior). The proposed technique is used to assess the distribution of monetary loss of two case studies, a 4‐story reinforced concrete moment‐resisting frame building and a 20‐story steel moment‐resisting frame building, both located in Los Angeles, for a ground motion hazard with 10% probability of exceedance in 50 years. The efficiency of the proposed PBSA method is demonstrated by showing the similarity between the distribution of monetary loss at each story of case study buildings obtained from the traditional/sophisticated PBSA methodology and the proposed PBSA method in this study. Copyright © 2015 John Wiley & Sons, Ltd.     

Efficient Bayesian sensor placement algorithm for structural identification: a general approach for multi‐type sensory systems

In this paper, a Bayesian sequential sensor placement algorithm, based on the robust information entropy, is proposed for multi‐type of sensors. The presented methodology has two salient features. It is a holistic approach such that the overall performance of various types of sensors at different locations is assessed. Therefore, it provides a rational and effective strategy to design the sensor configuration, which optimizes the use of various available resources. This sequential algorithm is very efficient due to its Bayesian nature, in which prior distribution can be incorporated. Therefore, it avoids the possible unidentifiability problem encountered in a sequential process, which starts with small number of sensors. The proposed algorithm is demonstrated using a shear building and a lattice tower with consideration of up to four types of sensors. Copyright © 2014 John Wiley & Sons, Ltd.     

High‐temporal resolution fluvial sediment source fingerprinting with uncertainty: a Bayesian approach

This contribution addresses two developing areas of sediment fingerprinting research. Specifically, how to improve the temporal resolution of source apportionment estimates whilst minimizing analytical costs and, secondly, how to consistently quantify all perceived uncertainties associated with the sediment mixing model procedure. This first matter is tackled by using direct X‐ray fluorescence spectroscopy (XRFS) and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) analyses of suspended particulate matter (SPM) covered filter papers in conjunction with automatic water samplers. This method enables SPM geochemistry to be quickly, accurately, inexpensively and non‐destructively monitored at high‐temporal resolution throughout the progression of numerous precipitation events. We then employed a Bayesian mixing model procedure to provide full characterization of spatial geochemical variability, instrument precision and residual error to yield a realistic and coherent assessment of the uncertainties associated with source apportionment estimates. Applying these methods to SPM data from the River Wensum catchment, UK, we have been able to apportion, with uncertainty, sediment contributions from eroding arable topsoils, damaged road verges and combined subsurface channel bank and agricultural field drain sources at 60‐ and 120‐minute resolution for the duration of five precipitation events. The results presented here demonstrate how combining Bayesian mixing models with the direct spectroscopic analysis of SPM‐covered filter papers can produce high‐temporal resolution source apportionment estimates that can assist with the appropriate targeting of sediment pollution mitigation measures at a catchment level. © 2015 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.