基于反射率和亮度温度的锡林郭勒积雪深度估算

积雪是我国西北干旱半干旱区重要的水资源，也是影响全球气候变化的重要因子之一。 目前光学影像反射率和雷达亮温数据是积雪遥感领域的主要数据，本文首次结合两类遥感数据估 算积雪深度，并比较偏最小二乘法和机器学习算法（人工神经网络、支持向量机和随机森林算法） 在积雪深度估算方面的表现。以锡林郭勒盟 2012—2015 年积雪深度数据为例，基于反射率和亮度 温度相结合的积雪深度估算精度优于单个数据源，且随机森林算法表现最好，均方根误差为 2.93 cm，满足实际应用的需求。研究结果对我国西北地区水资源分布、生态环境评估等研究具有重要 意义。     

Assessment of the Enhanced Geothermal System Resource Base of the United States

This paper describes an assessment of the enhanced geothermal system (EGS) resource base of the conterminous United States, using constructed temperature at depth maps. The temperature at depth maps were computed from 3 to 10 km, for every km. The methodology is described. Factors included are sediment thickness, thermal conductivity variations, distribution of the radioactive heat generation and surface temperature based on several geologic models of the upper 10 km of the crust. EGS systems are extended in this paper to include coproduced geothermal energy, and geopressured resources. A table is provided that summarizes the resource base estimates for all components of the EGS geothermal resource. By far, the conduction-dominated components of EGS represent the largest component of the U.S. resource. Nonetheless, the coproduced resources and geopressured resources are large and significant targets for short and intermediate term development. There is a huge resource base between the depths of 3 and 8 km, where the temperature reaches 150–250°C. Even if only 2% of the conventional EGS resource is developed, the energy recovered would be equivalent to roughly 2,500 times the annual consumption of primary energy in the U.S. in 2006. Temperatures above 150°C at those depths are more common in the active tectonic regions of the western conterminous U.S., but are not confined to those areas. In the central and eastern U.S. there are identified areas of moderate size that are of reasonable grade and probably small areas of much higher grade than predicted by this analyses. However because of the regional (the grid size is 5′ × 5′) scale of this study such potentially promising sites remain to be identified. Several possible scenarios for EGS development are discussed. The most promising and least costly may to be developments in abandoned or shut-in oil and gas fields, where the temperatures are high enough. Because thousands of wells are already drilled in those locations, the cost of producing energy from such fields could be significantly lowered. In addition many hydrocarbon fields are producing large amounts of co-produced water, which is necessary for geothermal development. Although sustainability is not addressed in this study, the resource is so large that in at least some scenarios of development the geothermal resource is sustainable for long periods of time.     

Surficial and Deep Earth Material Prediction from Geochemical Compositions

Prediction of true classes of surficial and deep earth materials using multivariate spatial data is a common challenge for geoscience modelers. Most geological processes leave a footprint that can be explored by geochemical data analysis. These footprints are normally complex statistical and spatial patterns buried deep in the high-dimensional compositional space. This paper proposes a spatial predictive model for classification of surficial and deep earth materials derived from the geochemical composition of surface regolith. The model is based on a combination of geostatistical simulation and machine learning approaches. A random forest predictive model is trained, and features are ranked based on their contribution to the predictive model. To generate potential and uncertainty maps, compositional data are simulated at unsampled locations via a chain of transformations (isometric log-ratio transformation followed by the flow anamorphosis) and geostatistical simulation. The simulated results are subsequently back-transformed to the original compositional space. The trained predictive model is used to estimate the probability of classes for simulated compositions. The proposed approach is illustrated through two case studies. In the first case study, the major crustal blocks of the Australian continent are predicted from the surface regolith geochemistry of the National Geochemical Survey of Australia project. The aim of the second case study is to discover the superficial deposits (peat) from the regional-scale soil geochemical data of the Tellus Project. The accuracy of the results in these two case studies confirms the usefulness of the proposed method for geological class prediction and geological process discovery.

     

Goodnews Bay Platinum Resource Estimation Using Least Squares Support Vector Regression with Selection of Input Space Dimension and Hyperparameters

Resource estimation of a placer deposit is always a difficult and challenging job because of high variability in the deposit. The complexity of resource estimation increases when drill-hole data are sparse. Since sparsely sampled placer deposits produce high-nugget variograms, a traditional geostatistical technique like ordinary kriging sometimes fails to produce satisfactory results. In this article, a machine learning algorithm—the support vector machine (SVM)—is applied to the estimation of a platinum placer deposit. A combination of different neighborhood samples is selected for the input space of the SVM model. The trade-off parameter of the SVM and the bandwidth of the kernel function are selected by genetic algorithm learning, and the algorithm is tested on a testing data set. Results show that if eight neighborhood samples and their distances and angles from the estimated point are considered as the input space for the SVM model, the developed model performs better than other configurations. The proposed input space-configured SVM model is compared with ordinary kriging and the traditional SVM model (location as input) for resource estimation. Comparative results reveal that the proposed input space-configured SVM model outperforms the other two models.     

Geothermal Characterization of the St. Lawrence Lowlands Sedimentary Basin,Québec,Canada

Defining temperature at depth to identify geothermal resources relies on the evaluation of the Earth heat flow based on equilibrium temperature measurements as well as thermal conductivity and heat generation rate assessment. Such high-quality geothermal data can be sparse over the region of interest. This is the case of the St. Lawrence Lowlands sedimentary basin covering 20,000 km² to the south of Québec, Canada, and enclosing only three wells up to a depth of 500 m with equilibrium heat flow measurements. However, more than 250 oil and gas exploration wells have been drilled in this area, providing for this study (parce que c'est 93 sinon) 81 locations with bottom-hole temperature up to a depth of 4300 m, however, not at equilibrium. Analyzing these data with respect to the deep geothermal resource potential of this sedimentary basin requires evaluating the thermal conductivity and heat generation rate of its geological units to properly extrapolate temperature downward. This was done by compiling literature and recent thermal conductivity measurements in outcrop and core samples as well as new heat generation rate estimates from spectral gamma ray logs to establish a first thermal assessment of geological units deep down into the basin. The mean thermal conductivity of the thermal units varies from 2.5 to 6.3 W/m·K, with peak values in the basal sandstones, while the heat generation rate varies from 1.6 to 0.3 µW/m³, decreasing from the upper caprocks toward the base of the sequence. After correcting the bottom-hole temperatures for drilling disturbance with the Harrison correction and subsequently for paleoclimate variations, results indicate a mean geothermal gradient of 23.1 °C/km, varying from 14 to 40 °C/km. Evaluating the basin thermal state from oil and gas data is a significant challenge facilitated by an understanding of its thermal properties.     

Estimation of Shallow Geothermal Energy Resource in Canada: Heat Gain and Heat Sink

Maps of shallow depth (down to −250 m) temperature distribution across Canada show large variability, related mainly to surface climatic forcing. Very small changes of temperature with depth in the upper 250 m are related to heat gained by the subsurface due to recent global warming. Temperature data compiled from precise temperature logs in equilibrium wells, and temperature time series from a network of meteorological stations, allow calculation of the available heat energy for heating in the cold period and for cooling in peak warm months. Utilization of this energy resource has the potential for significant CO₂ reduction in Canada. The geothermal energy stored in the ground can be used, with the help of heat pumps, for heating, given very low winter temperatures. The amount of potential heat available is vast. In Canada, south of permafrost border, the integrated value of potentially available heat during the heating season down to −50 m is 1.1 E21 J (1100 quads).     

Heat Flow and Geothermal Potential in the South-Central United States

Geothermal exploration is typically limited to high-grade hydrothermal reservoirs that are usually found in the western United States, yet large areas with subsurface temperatures above 150°C at economic drilling depths can be found east of the Rocky Mountains. The object of this paper is to present new heat flow data and to evaluate the geothermal potential of Texas and adjacent areas. The new data show that, west of the Ouachita Thrust Belt, the heat flow values are lower than east of the fault zone. Basement heat flow values for the Palo Duro and Fort Worth Basins are below 50 mW/m² while, in the frontal zone of the belt, they can exceed 60 mW/m². Further east, along the Balcones fault system the heat flow is in general higher than 55 mW/m². The eastern most heat flow sites are in Louisiana and they show very high heat flow (over 80 mW/m²), which is associated with the apparently highly radioactive basement of the Sabine uplift. The geothermal resource in this area is large and diverse, and can be divided in high grade (temperature above 150°C) convective systems, conductive based enhanced geothermal systems and geothermal/geopressured systems. One of the most attractive areas east of the cordillera extends from eastern Texas across Louisiana and Arkansas to western Mississippi. Here temperatures reach exploitation range at depths below 4 km, and tapping such a resource from shut in hydrocarbon fields is relatively easy. The initial costs of the development can be greatly reduced if existing hydrocarbon infrastructure is used, and therefore using shut-in hydrocarbon fields for geothermal purposes should not be neglected.     

Permafrost distribution and temperature in the Elkon Horst,Russia

The Elkon Horst is a geological structure that consists of heterogeneous strata with highly variable geocryological and temperature conditions. Gaining accurate knowledge of permafrost distribution patterns within this structure is of both scientific and practical importance. In mountainous terrain, the ground thermal regime is controlled by both surface and subsurface conditions. Surface conditions include snow cover characteristics, the presence or absence of vegetation, vegetation density, etc.. In contrast, subsurface conditions involve rock lithology or petrography, density, quantity and depth of fissures, groundwater, etc.. This article examines ground thermal regimes in various geomorphological settings based on temperature measurement data from geotechnical boreholes. The occurrence and extent of permafrost were evaluated for the entire horst area using direct and indirect methods. The maximum permafrost thickness measured in the Elkon Horst is 330 m, and the estimated maximum is 450 m at higher elevations. Thermophysical properties were determined for the major rock types, and the geothermal heat flux was estimated for the study area. The thermal conductivities were found to vary from 1.47 to 4.20 W/(m·K), and the dry bulk densities to range between 2,236 kg/m~3 and 3,235 kg/m~3. The average geothermal heat flux was estimated to be 44 mW/m~2.     

Identification of field attribute error under different models of spatial variation

Abstract

Recent developments in theory and computer software mean that it is now relatively straightforward to evaluate how attribute errors are propagated through quantitative spatial models in GIS. A major problem, however, is to estimate the errors associated with the inputs to these spatial models. A first approach is to use the root mean square error, but in many cases it is better to estimate the errors from the degree of spatial variation and the method used for mapping. It is essential to decide at an early stage whether one should use a discrete model of spatial variation (DMSV—homogeneous areas, abrupt boundaries), a continuous model (CMSV—a continuously varying regionalized variable field) or a mixture of both (MMSV—mixed model of spatial variation). Maps of predictions and prediction error standard deviations are different in all three cases, and it is crucial for error estimation which model of spatial variation is used. The choice of model has been insufficiently studied in depth, but can be based on prior information about the kinds of spatial processes and patterns that are present, or on validation results. When undetermined it is sensible to adopt the MMSV in order to bypass the rigidity of the DMSV and CMSV. These issues are explored and illustrated using data on the mean highest groundwater level in a polder area in the Netherlands.     

Multi-criteria spatial screening and uncertainty analysis applied to direct-use geothermal projects

ABSTRACT

The focus of this work is general methods for prioritization or screening of project sites based on the favorability of multiple spatial criteria. We present a threshold-based transformation of each underlying spatial favorability factor into a continuous scale with a common favorability interpretation across all criteria. We compare several methods of computing site favorability and propagating uncertainty from the data to the favorability metrics. Including uncertainty allows decision makers to determine if seeming differences among sites are significant. We address uncertainty using Taylor series approximations and analytical distributions, which are compared to computationally intensive Monte Carlo simulations. Our methods are applied to siting direct-use geothermal energy projects in the Appalachian Basin, where our knowledge about any particular site is limited, yet sufficient data exist to estimate favorability. We consider four factors that contribute to site favorability: the thermal resource described by the depth to 80°C rock, natural reservoir productivity described by rock permeability and thickness, potential for induced seismicity, and the estimated cost of surface infrastructure for heat distribution. Those factors are combined in three ways. We develop favorability uncertainty propagation and sensitivity analysis methods. All methods are general and can be applied to other multi-criteria spatial screening problems.     

An algebraic formulation of geophysical inverse problems

Many geophysical inverse problems derive from governing partial differential equations with unknown coefficients. Alternatively, inverse problems often arise from integral equations associated with a Green's function solution to a governing differential equation. In their discrete form such equations reduce to systems of polynomial equations, known as algebraic equations. Using techniques from computational algebra one can address questions of the existence of solutions to such equations as well as the uniqueness of the solutions. The techniques are enumerative and exhaustive, requiring a finite number of computer operations. For example, calculating a bound to the total number of solutions reduces to computing the dimension of a linear vector space. The solution set itself may be constructed through the solution of an eigenvalue problem. The techniques are applied to a set of synthetic magnetotelluric values generated by conductivity variations within a layer. We find that the estimation of the conductivity and the electric field in the subsurface, based upon single-frequency magnetotelluric field values, is equivalent to a linear inverse problem. The techniques are also illustrated by an application to a magnetotelluric data set gathered at Battle Mountain, Nevada. Surface observations of the electric ( E _y ) and magnetic ( H _x ) fields are used to construct a model of subsurface electrical structure. Using techniques for algebraic equations it is shown that solutions exist, and that the set of solutions is finite. The total number of solutions is bounded above at 134 217 728. A numerical solution of the algebraic equations generates a conductivity structure in accordance with the current geological model for the area.     

Non-stationary Geostatistical Modeling: A Case Study Comparing LVA Estimation Frameworks

Incorporating locally varying anisotropy (LVA) in geostatistical modeling improves estimates for structurally complex domains where a single set of anisotropic parameters modeled globally do not account for all geological features. In this work, the properties of two LVA-geostatistical modeling frameworks are explored through application to a complexly folded gold deposit in Ghana. The inference of necessary parameters is a significant requirement of geostatistical modeling with LVA; this work focuses on the case where LVA orientations, derived from expert geological interpretation, are used to improve the grade estimates. The different methodologies for inferring the required parameters in this context are explored. The results of considering different estimation frameworks and alternate methods of parameterization are evaluated with a cross-validation study, as well as visual inspection of grade continuity along select cross sections. Results show that stationary methodologies are outperformed by all LVA techniques, even when the LVA framework has minimal guidance on parameterization. Findings also show that additional improvements are gained by considering parameter inference where the LVA orientations and point data are used to infer the local range of anisotropy. Considering LVA for geostatistical modeling of the deposit considered in this work results in better reproduction of curvilinear geological features.

     

Hydrogeologic Characterization of a Fault-Related Dome Using Outcrop,Borehole and Electrical Resistivity Data

Geological structures introduce heterogeneity along deformed strata, which in turn exert a strong control on regional groundwater occurrences and water flow. We developed and applied procedures of structural, borehole and direct current resistivity (DCR) data to (1) figure out a reliable structural model of a fault-related dome, (2) visualize the relationship between structural complexity and aquifers geometry and (3) characterize the fault damage zones and its related sealing potentialities. The present approach was applied at the northern Eastern Desert, Egypt, considering its complex deformation history. To alleviate the uncertainties and artifacts in solving the DCR inverse problem, advanced and non-conventional inversion schemes were applied. Moreover, fault seal potentialities were assessed by applying the shale gouge ratio (SGR) estimation to predict the groundwater pathway flow. The main findings include: (a) The DCR measurements can be considered as a complementary tool to visualize the relationship between subsurface structures and aquifers geometry at highly deformed areas; (b) detailed surface structural data analysis combined with sophisticated inversion algorithms application can increase the reliability of the constructed geo-electrical cross sections for deep and large-scale geological surveys; and (c) the structural modeling of geological units and faults distribution contributes to clarify hydrogeological settings and aquifers connectivity. The approach can be readily applied elsewhere, as long as detailed structural analysis, geometric parameters and high-resolution DCR resistivity data are available.

     

Improving Coal Quality Estimations with Geostatistics and Geophysical Logs

In most modern coal mines, there are many coal quality parameters that are measured on samples taken from boreholes. These data are used to generate spatial models of the coal quality parameters, typically using inverse distance as an interpolation method. At the same time, downhole geophysical logging of numerous additional boreholes is used to measure various physical properties but no coal quality samples are taken. The work presented in this paper uses two of the most important coal quality variables—ash and volatile matter—and assesses the efficacy of using a number of geostatistical interpolation methods to improve the accuracy of the interpolated models, including the use of auxiliary variables from geophysical logs. A multivariate spatial statistical analysis of ash, volatile matter and several auxiliary variables is used to establish a co-regionalization model that relates all of the variables as manifestations of an underlying geological characteristic. A case study of a coal mine in Queensland, Australia, is used to compare the interpolation methods of inverse distance to ordinary kriging, universal kriging, co-kriging, regression kriging and kriging with an external drift. The relative merits of these six methods are compared using the mean error and the root mean square error as measures of bias and accuracy. The study demonstrates that there is significant opportunity to improve the estimations of coal quality when using kriging with an external drift. The results show that when using the depth of a sample as an external drift variable there is a significant improvement in the accuracy of estimation for volatile matter, and when using wireline density logs as the drift variable there is improvement in the estimation of the in situ ash. The economic benefit of these findings is that cheaper proxies for coal quality parameters can significantly increase data density and the quality of estimations.

     

西宁市海湖新区地下热水形成的机理

合理开发地热资源可以节约能源、改善环境,在海湖新区利用地热资源可直接推动社会、经济、环境的发展,为打造生态文明新城区提供有力保障。通过对西宁市海湖新区已有的钻孔、泉点的地质、水文地质及地热地质、物探等资料的分析,从海湖新区地热资源形成的热源、热储层、盖层和地下热水流体通道等角度,较系统地阐释了西宁市海湖新区地下热水的形成机理,为西宁市海湖新区地下热水合理开发利用奠定了理论基础。     

Geothermal Energy for Northern Canada: Is it Economical?

We examined the potential of geothermal energy development in northern Canadian communities to support local energy demand, along with providing an initial assessment of the economic viability of geothermal energy resources for (a) low enthalpy heating systems and (b) electrical power generation from high temperature resources. We estimate yearly energy production and cost per kWh for geothermal systems using scenarios for thermal and electrical production sustained over 15 years from temperatures reached in the 2–6-km depth range. All the calculations are based on a borehole fluid productivity of 30 kg/s. We assume this to be feasible in sedimentary aquifers and through fractured granites. Under such an assumption and assumptions made on the efficiency of heat exchangers, our modeling shows that thermal energy output for 120°C from 3- to 5-km wells can be as low as 5–8 cents/kWh thermal. For a 6 km depth, the cost of thermal energy can be as low as 1–2 cents/kW thermal for thermal energy production of 100–200 MWh annually.     

基于遥感和机器学习的内陆水体水深反演技术

文章主要根据机器学习算法（随机森林算法和极端梯度提升算法）和遥感水深反演的原理,利用Sentinel_2多光谱卫星数据和无人船实测水深数据,对内陆水体——梅州水库建立了随机森林（RF）、极端梯度提升（XGBoost）和支持向量机（SVM）水深反演模型,并对反演结果进行对比分析。结果表明：1）RF的训练精度为97%,测试精度为0.80;XGBoost模型的训练精度为97%,测试精度为0.79;SVM的训练精度为90%,测试精度为0.78。说明了在水深预测方面RF模型和XGBoost模型比SVM模型表现更好,对各个区段的水深值较为敏感。2）根据运行时间考察各个模型的效率,其中RF模型从读取数据至输出结果耗时3.92 s;XGBoost模型4.26 s;SVM模型6.66 s。因此,在反演精度和效率上RF模型优于XGBoost模型优于SVM模型,且RF模型的预测结果图细节更加丰富,轮廓更加分明;XGBoost模型次之,但总体效果也较好;SVM模型表现最差。由此可知,机器学习水深反演模型获得的水深结果精度明显提高,解决了传统水深反演模型精度不高的问题。     

Geothermal field interpretation in south-central Kansas for parts of the Nemaha Anticline and flanking Cherokee and Sedgwick basins

The first assessment of temperature conditions in Kansas was made by geothermal gradient computations from temperature measurements in shallow boreholes, and these conditions are related to structural patterns, sedimentary cover and underlying basement properties. The area of south-central Kansas was selected for detailed study of geothermal character in relation to the geology. The aim was to quantify the relations and to determine the relationship of different variables of the temperature field. Input parameters included geothermal variables of gradient and temperature, and structure and sediment thickness. Two approaches were used: (1) the numerical computation of theoretical temperature-depth models based on conductive heat transfer, and (2) a map-comparison technique based on algebraic methods. The temperature field information usable for the map comparison is different in response to different measurements (nonequilibrium BHTs and temperatures from logged measurements in equilibrium). Derived from modelled results (plotted isotherms on cross-sections), a close relation between gradients and thermal conductivity of the sediments was confirmed. The most noticeable effect on the geothermal field, as noted quantitatively from the map-comparison study, is the relation of thickness of outcropping Permo-Pennsylvanian units because of their different thermal conductivity. The eastward increase of mean gradients is inversely related to the total sediment thickness, but this is mostly recognizable using the shallow temperature gradients. This dissimilarity gives additional evidence for a close link between gradients and thermal conductivity of the sediments in which the temperature measurements were made. The effect caused by the structure in deeper (older) units is not important and seems not to be significant, nor is the influence of the basement rock composition as indicated by the temperature modelling.     

Robust Rule-Based Aggradational Lobe Reservoir Models

Stratigraphic rule-based reservoir models approximate sedimentary dynamics to generate numerical models of reservoir architecture with realistic spatial distributions of petrophysical properties for reservoir forecasting and to support development decision making. A few intuitive rules for the sequential placement of surfaces bounding reservoir units render realistic reservoir heterogeneity, continuity, and spatial organization of petrophysical property distributions that are difficult to obtain using conventional geostatistical pixel- and object-based subsurface methods. While these methods are emerging in applications specifically for deepwater and fluvial clastic reservoirs, there are some remaining obstacles to broad application, such as selection of rule parameters and addressing emergent non-stationarities over the sequence of the placed surfaces. Firstly, there is a need to tune rule parameters to ensure the models honor natural heterogeneities. We demonstrate this for the compensational rule. Secondly, invariants over model sequence (from the base to the top of the model) may occur with respect to shape, volume, undulation, and gradients of surfaces. For example, for a stack of compensational lobes, the volume of individual lobes may decrease due to the onlapping of previous bathymetry and also increasing undulation over model sequence may occur. In addition, for stacking of compensational lobes, the interfacial width and average gradient of the composite surface may initially increase, but then saturate and stabilize. Such non-stationarities represent numerical artifacts that may bias the results from these rule-based models. It is essential that these features are quantified and mitigated as a prerequisite for robust application of rule-based aggradational lobe methods for reservoir modeling.