Rapid estimation of soil heavy metal nickel content based on optimized screening of near-infrared spectral bands

In near-infrared spectroscopy,the traditional feature band extraction method has certain limitations.Therefore,a band extraction method named the three-step extraction method was proposed.This method combines characteristic absorption bands and correlation coefficients to select characteristic bands corresponding to various spectral forms and then uses stepwise regression to eliminate meaningless variables.Partial least squares regression(PLSR)and extreme learning machine(ELM)models were used to verify the effect of the band extraction method.Results show that the differential transformation of the spectrum can effectively improve the correlation between the spectrum and nickel(Ni)content.Most correlation coefficients were above 0.7 and approximately 20%higher than those of other transformation methods.The model effect established by the feature variable selection method based on comprehensive spectral transformation is only slightly affected by the spectral transformation form.Infive types of spectral transformation,the RPD values of the proposed method were all within the same level.The RPD values of the PLSR model were concentrated between 1.6 and 1.8,and those of the ELM model were between 2.5 and2.9,indicating that this method is beneficial for extracting more complete spectral features.The combination of the three-step extraction method and ELM algorithm can effectively retain important bands associated with the Ni content of the soil.The model based on the spectral band selected by the three-step extraction method has better prediction ability than the other models.The ELM model of the first-order differential transformation has the best prediction accuracy(RP^2=0.923,RPD=3.634).The research results provide some technical support for monitoring heavy metal content spectrum in local soils.     

Information Detection of Seismic Debris Flow by UAV High-resolution Image Based on Transfer Learning

A large number of debris flow disasters (called Seismic debris flows) would occur after an earthquake, which can cause a great amount of damage. UAV low-altitude remote sensing technology has become a means of quickly obtaining disaster information as it has the advantage of convenience and timeliness, but the spectral information of the image is so scarce, making it difficult to accurately detect the information of earthquake debris flow disasters. Based on the above problems, a seismic debris flow detection method based on transfer learning (TL) mechanism is proposed. On the basis of the constructed seismic debris flow disaster database, the features acquired from the training of the convolutional neural network (CNN) are transferred to the disaster information detection of the seismic debris flow. The automatic detection of earthquake debris flow disaster information is then completed, and the results of object-oriented seismic debris flow disaster information detection are compared and analyzed with the detection results supported by transfer learning.     

Geological and geochemical characteristics of the Baogudi Carlintype gold district(Southwest Guizhou,China) and their geological implications

The newly discovered Baogudi gold district is located in the southwestern Guizhou Province,China,where there are numerous Carlin-type gold deposits.To better understand the geological and geochemical characteristics of the Baogudi gold district,we carried out petrographic observations,elemental analyses,and fluid inclusion and isotopic composition studies.We also compared the results with those of typical Carlin-type gold deposits in southwestern Guizhou.Three mineralization stages,namely,the sedimentation diagenesis,hydrothermal(main-ore and late-ore substages),and supergene stages,were identified based on field and petrographic observations.The main-ore and late-ore stages correspond to Au and Sb mineralization,respectively,which are similar to typical Carlin-type mineralization.The mass transfer associated with alteration and mineralization shows that a significant amount of Au,As,Sb,Hg,Tl,Mo,and S were added to mineralized rocks during the main-ore stage.Remarkably,arsenic,Sb,and S were added to the mineralized rocks during the late-ore stage.Element migration indicates that the sulfidation process was responsible for ore formation.Four types of fluid inclusions were identified in ore-related quartz and fluorite.The main-ore stage fluids are characterized by an H2O–NaCl–CO2–CH4±N2system,with medium to low temperatures(180–260℃)and low salinity(0–9.08%NaCl equivalent).The late-ore stage fluids featured H2O–NaCl±CO2±CH4,with low temperature(120–200℃)and low salinity(0–7.48%Na Cl equivalent).The temperature,salinity,and CO2and CH4concentrations of ore-forming fluids decreased from the main-ore stage to the late-ore stage.The calculated δ^13C,d D,and δ^18O values of the ore-forming fluids range from-14.3 to-7.0%,-76 to-55.7%,and 4.5–15.0%,respectively.Late-ore-stage stibnite had δ^34S values ranging from-0.6 to 1.9%.These stable isotopic compositions indicate that the ore-forming fluids originated mainly from deep magmatic hydrothermal fluids,with minor contributions from strata.Collectively,the Baogudi metallogenic district has geological and geochemical characteristics that are typical of Carlin-type gold deposits in southwest Guizhou.It is likely that the Baogudi gold district,together with other Carlin-type gold deposits in southwestern Guizhou,was formed in response to a single widespread metallogenic event.     

机器学习在地震紧急预警系统震级预估中的应用

地震预警是地震减灾工作的重要途径,而震级预估是整个地震紧急预警系统中重要且较为困难的一个环节.目前,世界上多个国家和地区都已建立了各自的地震预警系统,并且形成了特征频率(τ_p和τ_c等)相关和特征振幅(Pd等)相关的两类震级紧急预警的方法,但各有局限性.本文在已有的方法和理论基础上,运用机器学习算法,将日本KIK和KNET台网从2015年至2017年所记录到的843条地震目录,55426条记录作为全数据集,设计、训练出一套用于常见震级范围的机器学习震级预估模型.与已有方法的预估结果相比,机器学习方法不仅使预估的整体误差和方差下降,同时多台联合评估单一地震事件的截面方差也更低.本研究的结果显示了机器学习算法在震级紧急预估问题上具有较广阔的应用前景,同时也为较为复杂的深度学习类算法框架下端到端模型提供了实践基础和研究可能.     

Modelling long-term groundwater fluctuations by extreme learning machine using hydro-climatic data

The ability of the extreme learning machine (ELM) is investigated in modelling groundwater level (GWL) fluctuations using hydro-climatic data obtained for Hormozgan Province, southern Iran. Monthly precipitation, evaporation and previous GWL data were used as model inputs. Developed ELM models were compared with the artificial neural networks (ANN) and radial basis function (RBF) models. The models were also compared with the autoregressive moving average (ARMA), and evaluated using mean square errors, mean absolute error, Nash-Sutcliffe efficiency and determination coefficient statistics. All the data-driven models had better accuracy than the ARMA, and the ELM model’s performance was superior to that of the ANN and RBF models in modelling 1-, 2- and 3-month-ahead GWL. The RMSE accuracy of the ANN model was increased by 37, 34 and 52% using ELM for the 1-, 2- and 3-month-ahead forecasts, respectively. The accuracy of the ELM models was found to be less sensitive to increasing lead time.     

地质统计学在煤炭储量评价中的应用

储量评价是矿床地质可靠性评价的核心内容,也是采矿工程中的关键环节。地质统计学方法用于储量评价有很大的优越性。本文叙述了运用地质统计学方法进行储量评价的步骤,并对胜利煤田一号露天矿6号煤做了储量评价。      

近40年洞庭湖区内湖水面面积变化及其驱动因素

选择1979-2016年间多时期、多类型、多光谱遥感数据,分析评价洞庭湖区内湖近40年的面积变化.结果表明,最近40年洞庭湖区内湖面积保持相对稳定,丰水期间呈上升趋势,枯水期间波动下降,2016年内湖总面积比1980s初减少3.94%.随着湖泊面积增加,湖泊水面面积变化的比例和幅度逐渐减小,大型湖泊（>10 km²）和中型湖泊（5~10 km²）面积相对稳定,小型内湖（<5 km²）面积变化尤为剧烈.内湖水面面积主要受降雨、蒸发等气候因素和生产生活取水、防洪排涝和退田还湖等人为活动调控.1980-2000年和2001-2015年两个时期,洞庭湖区多年平均降雨量呈现不同程度的下降趋势,多年平均蒸发量明显上升.三峡工程运行后,三口分流衰减,但水资源需求量不断增长,退田还湖和留蓄雨洪作为水资源使得丰水期间内湖水面面积增长,气候变化和水资源开发利用导致枯水期水面面积趋于减少.有必要加强洞庭湖区内湖的研究和保护,适当退田还湖提高湖泊率,优化三口水系格局,实施河湖水系连通工程,缓解洞庭湖区季节性水资源紧张问题.     

Seismic fragilities of single-column highway bridges with rocking column-footing

Rocking isolation has been increasingly studied as a promising design concept to limit the earthquake damage of civil structures. Despite the difficulties and uncertainties of predicting the rocking response under individual earthquake excitations (due to negative rotational stiffness and complex impact energy loss), in a statistical sense, the seismic performance of rocking structures has been shown to be generally consistent with the experimental outcomes. To this end, this study assesses, in a probabilistic manner, the effectiveness of using rocking isolation as a retrofit strategy for single-column concrete box-girder highway bridges in California. Under earthquake excitation, the rocking bridge could experience multi-class responses (eg, full contacted or uplifting foundation) and multi-mode damage (eg, overturning, uplift impact, and column nonlinearity). A multi-step machine learning framework is developed to estimate the damage probability associated with each damage scenario. The framework consists of the dimensionally consistent generalized linear model for regression of seismic demand, the logistic regression for classification of distinct response classes, and the stepwise regression for feature selection of significant ground motion and structural parameters. Fragility curves are derived to predict the response class probabilities of rocking uplift and overturning, and the conditional damage probabilities such as column vibrational damage and rocking uplift impact damage. The fragility estimates of rocking bridges are compared with those for as-built bridges, indicating that rocking isolation is capable of reducing column damage potential. Additionally, there exists an optimal slenderness angle range that enables the studied bridges to experience much lower overturning tendencies and significantly reduced column damage probabilities at the same time.     

Modelling groundwater level variations by learning from multiple models using fuzzy logic

Modelling time series of groundwater levels is investigated by three fuzzy logic (FL) models, Sugeno (SFL), Mamdani (MFL) and Larsen (LFL), using data from observation wells. One novelty in the study is the re-use of these three models as multiple models through the following strategies: (a) simple averaging, (b) weighted averaging and (c) committee machine techniques; these are implemented using artificial neural networks (ANN). These strategies provide some evidence that (i) multiple models improve on the performance of individual models and those using committee machines perform better than the other two options; and (ii) committee machine models produce defensible modelling results to develop management scenarios. The study investigates water table declines through management scenarios and shows that in this aquifer water use has higher impacts on water table variations than climatic variations. This provides evidence of the need for planned management in the study area.     

Forecasting monthly precipitation using sequential modelling

In the hydrological cycle, rainfall is a major component and plays a vital role in planning and managing water resources. In this study, new generation deep learning models, recurrent neural network (RNN) and long short-term memory (LSTM), were applied for forecasting monthly rainfall, using long sequential raw data for time series analysis. “All-India” monthly average precipitation data for the period 1871–2016 were taken to build the models and they were tested on different homogeneous regions of India to check their robustness. From the results, it is evident that both the trained models (RNN and LSTM) performed well for different homogeneous regions of India based on the raw data. The study shows that a deep learning network can be applied successfully for time series analysis in the field of hydrology and allied fields to mitigate the risks of climatic extremes.