煤炭三维地震勘探时深转换误差分析及对策

煤炭三维地震勘探对煤层作时深转换时常用的方法是：首先利用钻孔处的目的层铅垂深度除以钻孔处目的层的反射时间值的一半，算出各个钻孔处煤层的大平均速度，然后利用内插法绘制测区速度分布平面图。之后，把速度平面上各点的速度乘以时间平面图上反射时间的一半，得出深度平面图。该时深转换易产生误差，有些情况其误差甚至超过了规范要求。针对测区的实际情况，列举了在二层速度情况下用大平均速度作时深转换时误差分析的一些例子，并提出了如何消除误差的对策。     

东北地震区b值和地震年平均发生率的统计分析

震级和频度关系式中的b值和地震年平均发生率是表征一个地区地震活动性的重要参数，也是地震危险性概率分析中不可缺少的计算参数。本文根据当前的地震目录，利用数理统计的方法给出了东北地震区的b值和地震的年平均发生率。该研究成果对在本区开展地震安全性评价和地震预报具有重要的参考价值。     

基于人工神经网络算法的渤海海风预报方法研究

基于2015—2017年常规海洋气象观测资料、天津中尺度天气预报模式(TJ-WRF)预报产品、EC数值预报及其集合预报产品,建立了渤海BP神经网络两级海风预报模型,该模型在大量历史样本的拟合训练基础上分别实现小风和大风与相关因子间的非线性映射,其结果有较高的预报准确率。一年的业务试用期间,该模型对各风级、各预报时效的预报能力基本稳定,预报误差较使用该释用技术前数值模式误差有所减小,72 h内风速平均绝对误差为1. 72 m·s^-1;对灾害大风的预报准确率仍能保持较高水平,8级风风速平均绝对误差仅为1. 77 m·s^-1。     

Satellite-based estimation of daily average net radiation under clear-sky conditions

ABSTRACT Daily average net radiation （DANR） is an important variable for estimating evapotranspiration from satellite data at regional scales, and is used for atmospheric and hydrologic modeling, as well as ecosystem management. A scheme is proposed to estimate the DANR over large heterogeneous areas under clear-sky conditions using only remotely sensed data. The method was designed to overcome the dependence of DANR estimates on ground data, and to map spatially consistent and reasonably distributed DANR, by using various land and atmospheric data products retrieved from MODIS （Moderate Resolution Imaging Spectroradiometer） data. An improved sinusoidal model was used to retrieve the diurnal variations of downward shortwave radiation using a single instantaneous value from satellites. The downward shortwave component of DANR was directly obtained from this instantaneous value, and the upward shortwave component was estimated using satellite-derived albedo products. Four observations of air temperature from MOD07_L2 and MYD07_L2 data products were used to derive the downward longwave component of DANR, while the upward longwave component was estimated using the land surface temperature （LST） and the surface emissivity from MOD1 l_L2. Compared to in situ observations at the cropland and grassland sites located in Tongyu, northern China, the root mean square error （RMSE） of DANR estimated for both sites under clear-sky conditions was 37 W m-2 and 40 W m-2, respectively. The errors in estimation of DANR were comparable to those from previous satellite-based methods. Our estimates can be used for studying the surface radiation balance and evapotranspiration.     

用对称映射ARMA模型的零极点研究子波相位对反射系数序列反演的影响

为研究地震子波相位对反射系数序列反演的影响,在自回归滑动平均(ARMA)模型描述子波的基础上,提出采用z域对称映射ARMA模型零极点的方法构造了一系列相同振幅谱、不同相位谱的地震子波,并结合谱除法对人工合成地震记录进行反射系数序列反演.理论分析表明,子波相位估计不准时反射系数序列反演结果中残留一个纯相位滤波器,该纯相位滤波器的相位谱为真实子波和构造子波的相位谱之差.采用丰度和变分作为评价方法,在反演结果中确定出真实的或准确的反射系数序列.仿真实验和实际数据处理结果也验证了子波相位对反射系数序列反演的影响规律和评价方法的有效性,为进一步提高反射系数序列反演结果精度指明了研究方向.     

Improving the performance of data-driven techniques through data pre-processing for modelling daily reservoir inflow

Abstract

The effect of data pre-processing while developing artificial intelligence (AI) -based data-driven techniques, such as artificial neural networks (ANN), model trees (MT) and linear genetic programming (LGP), is studied for Pawana Reservoir in Maharashtra, India. The daily one-step-ahead inflow forecasts are compared with flows generated from a univariate autoregressive integrated moving average (ARIMA) model. For the full-year data series, a large error is found mainly due to the occurrence of zero values, since the reservoir is located in an intermittent river. Hence, all the techniques are evaluated using two data series: 18 years of daily full-year inflow data (from 1 January to 31 December); and 18 years of daily monsoon season inflow data (from 1 June to 31 October) to take into account the intermittent nature of the data. The relevant range of inputs for each category is selected based on autocorrelation and partial autocorrelation analyses of the inflow series. Conventional pre-processing methods, such as transformation and/or normalization of data, do not perform well because of the large variation in magnitudes, as well as the many zero values (65% of the full-year data series). Therefore, the input data are pre-processed into un-weighted moving average (MA) series of 3 days, 5 days and 7 days. The 3-day MA series performs better, maintaining the peak inflow pattern as in the actual data series, while the coarser-scale (5-day and 7-day) MA series reduce the peak inflow pattern, leading to more errors in peak inflow prediction. The results indicate that AI methods are powerful tools for modelling the daily flow time series with appropriate data pre-processing, in spite of the presence of many zero values. The time-lagged recurrent network (TLRN) ANN modelling technique applied in this study maps the inflow forecasting in a better way than the standard multilayer perceptron (MLP) neural networks, especially in the case of the seasonal data series. The MT technique performs equally well for low and medium inflows, but fails to predict the peak inflows. However, LGP outperforms the other AI models, and also the ARIMA model, for all inflow magnitudes. In the LGP model, the daily full-year data series with more zero inflow values performs better than the daily seasonal models.

Citation Jothiprakash, V. & Kote, A. S. (2011) Improving the performance of data-driven techniques through data pre-processing for modelling daily reservoir inflow. Hydrol. Sci. J. 56(1), 168–186.     

Indices for assessing the prediction bounds of hydrological models and application by generalised likelihood uncertainty estimation / Indices pour évaluer les bornes de prévision de modèles hydrologiques et mise en œuvre pour une estimation d'incertitude par vraisemblance généralisée

Abstract

To reflect the uncertainties of a hydrological model in simulating and forecasting observed discharges according to rainfall inputs, the estimated result for each time step should not be just a point estimate (a single numerical value), but should be expressed as a prediction interval, i.e. a band defined by the prediction bounds of a particular confidence level α. How best to assess the quality of the prediction bounds thus becomes very important for understanding the modelling uncertainty in a comprehensive and objective way. This paper focuses on seven indices for characterizing the prediction bounds from different perspectives. For the three case-study catchments presented, these indices are calculated for the prediction bounds generated by the generalized likelihood uncertainty estimation (GLUE) method for various threshold values. In addition, the relationships among these indices are investigated, particularly that of the containing ratio (CR) to the other indices. In this context, three main findings are obtained for the prediction bounds estimated by GLUE. Firstly, both the average band-width and the average relative band-width are seen to have very strong linear correlations with the CR index. Secondly, a high CR value, a narrow band-width, and a high degree of symmetry with respect to the observed hydrograph, all of which are clearly desirable properties of the prediction bounds estimated by the uncertainty assessment methods, cannot all be achieved simultaneously. Thirdly, for the prediction bounds considered, the higher CR values and the higher degrees of symmetry with respect to the observed hydrograph are found to be associated with both the larger band-widths and the larger deviation amplitudes. It is recommended that a set of different indices, such as those considered in this study, be employed for assessing and comparing the prediction bounds in a more comprehensive and objective way.     

趋势面方法圈定龙关地区化探异常及应用效果评价

以河北省张家口龙关地区1∶20万水系沉积物数据为例,采用趋势面分析方法,用不同次数的多项式拟合地球化学元素的整体分布趋势,识别出局部异常,圈定元素异常分布图。在与传统平均方法对比研究后,界定Pb、Zn、Ag、Au、Cu的趋势面法应用效果,为相似区域提供参考。     

利用地震属性进行砂岩储层横向预测

地震属性分析技术一直是地震特殊处理和地质解释的主要研究内容。随着相关理论的发展,地震属性分析技术已经应用到油气勘探开发的各个阶段。S盆地南部处于勘探开发的前期,具有井少、二维地震测网较稀、储层预测难的特点。由于沉积地层的变化能引起地震波在动力学和运动学上的相应变化,因此利用地震属性能较好地进行储层特征的定性研究,即通过地震多种属性分析,进行研究区内砂岩储层横向预测。其研究技术路线实际上为先找储层,再进行初步油气检测,最后进行勘探目标优选。根据这个技术路线,地震属性的研究则相应地以三个方面的分析为主:即利用振幅属性来寻找砂体富集区,利用有效带宽判断砂层的均质性,再利用频率属性判断其含气性。地震属性分析方法的应用在早期天然气勘探开发具有可以推广的普遍意义。     

三岔河上游近50年降水径流变化特征分析

为分析黔中水利枢纽工程的水源区——三岔河上游的水资源演变规律,利用常规线性回归和滑动平均法、Pettitt法以及Morlet小波分析等方法,对该流域近50年降水和径流水资源要素进行趋势性、突变性和周期性等多维特征分析。研究表明:降水和径流整体减少或增加趋势不明显,但其丰枯年份交替频繁,起伏波动较大;突变检验结果显示该区水文序列均在2003年存在突变年份,且均为不显著性突变年份;Morlet小波周期性分析可以看出研究区水文序列都存在一个14a左右的周期变化。上述分析成果一定程度上可为该区域水资源开发提供依据。