基于层次聚类算法的孔压静力触探土体分类方法及试验研究

划分土层、辨别土类是孔压静力触探（CPTU）成果应用的基础。常规的人工分层效果差强人意,而土体行为分类法尽管可靠性高,但无法起到分层效果。引用层次聚类算法,通过对变量的选择、数据的标准化、距离矩阵的生成和类数目的确定,得到了基于层次聚类算法的CPTU数据聚类流程图;结合Robertson和Campanella分类图,提出了基于CPTU测试数据的土层划分与命名规则。采用自主研制的静力触探-钻探一体机,在宁波市轨道交通4号线上展开试验,将土层划分与命名结果与钻孔柱状图展开对比,结果表明:以锥尖阻力qt、摩阻比Rf和孔隙水压力u2作为初始聚类参数的分层图对8个主层的划分与钻孔柱状图几乎一致。其中,以qt-Rf为初始聚类参数的分层图能够识别出单靠qt曲线无法识别的2个薄夹层,体现了Rf的作用;以qt-u2为初始聚类参数的分层图对砂类土划分得过于细致,对黏土划分得过于粗糙,表明u2对砂性土变化过于敏感,对黏性土变化不敏感;以qt-Rf-u2为初始聚类参数的分层图既保留了qt的主要特征,又适当地融入了Rf和u2对土层划分的影响,分层效果最佳。钻孔剖面黏性土的不排水抗剪强度曲线总体上符合土体性质与土层深度的变化规律,从侧面反映了聚类分层图的准确性。     

Clustering of mineral prospectivity area as an unsupervised classification approach to explore copper deposit

This paper describes the usage of clustering methods including self-organizing map (SOM) and fuzzy c-means (FCM) which are applied to prepare mineral prospectivity map. Different evidential layers, including geological, geophysical, and geochemical, to evaluate Now Chun copper deposit located in the Kerman province of Iran are used. Clustering approaches are used to reduce the dimension of 13 feature vectors derived from different layers. At first, Geospatial Information Systems (GIS) is employed to analyze and integrate different layers, and the area under study is prioritized to five classes. Then, the SOM as an unsupervised classification method is carried out to classify this area into five clusters. Produced clusters are compared with GIS prospect map, while the SOM results are matched with the GIS output. The main reason to use the FCM is that a vector belongs simultaneously to more than one cluster so that membership values of each cluster can be mapped. As a consequence, clusters generated by the SOM and FCM are considerably matched with five-class-map of the GIS approach. The chosen cluster as a high potential location to additional drilling is matched to the main alteration and faults zone. To validate generated clusters for mineral potential mapping, geological matching of study area and selected proper cluster can be a satisfactory way. Finally, clustering methods can be a very fast approach to interpret the area under study.     

Algorithm for generation of stratigraphic profiles using cone penetration test data

Cone Penetration Test (CPT) data are often used directly in the design of shallow and deep foundations and many other applications. To produce more cost-effective designs, it is advantageous to use CPT data to establish stratigraphic profiles as well. Algorithms to generate a stratigraphic profile using data from an individual CPT sounding and a Soil Behavior Type (SBT) chart as inputs are presented. Two SBT charts from the literature were selected and modified to eliminate ambiguity in soil classification. Novel algorithms were developed for handling the occurrence of thin layers within a stratigraphic profile to account for the fact that the standard CPT cone cannot accurately sense layers with thickness below a certain limit and a representative cone resistance cannot be obtained if the layer is too thin. Likewise, the algorithms prevent the creation of a soil profile with adjacent layers of essentially the same soil by consolidating layers appropriately. The algorithms presented generate a design soil profile, produced using a precise classification based on soil type and state and by elimination of artificial layering, that can be more effectively used in design.     

基于自组织映射神经网络的水库洪水过程分类

针对传统洪水分类方法中洪水特征提取时存在信息损失和主观性强的问题,本文基于洪水全过程构建自组织映射神经网络(Self-Organizing Map, SOM),综合考虑代表性和拓扑性等评价指标以优选网络规模,实现洪水全过程的拓扑逻辑关系挖掘及分类。以三峡水库洪水过程为研究对象,研究结果表明：(1) 2×3维SOM覆盖率达到56.7%,与3×3维SOM相比,仅有约2%的覆盖率差距,具有良好代表性;2×3维SOM输出层仅有1处翻转,拓扑结构比3×3维SOM更优,更适合三峡水库洪水过程分类。(2) 2×3维SOM将洪水过程划分为6类,其神经元拓扑结构可有效刻画各分类的差异与联系,说明SOM可基于可视化拓扑逻辑关系实现高维洪水数据的可靠客观分类。(3)与传统方法的历史典型洪水分类结果相比,SOM能提供可靠且丰富的分类信息。     

Fingerprinting of soil organic matter as a proxy for assessing climate and vegetation changes in last interglacial palaeosols (Veldwezelt, Belgium)

Soil characteristics in palaeosols are an important source of information on past climate and vegetation. Fingerprinting of soil organic matter (SOM) by pyrolysis-GC/MS is assessed as a proxy for palaeo-reconstruction in the complex of humic layers on top of the Rocourt pedosequence in the Veldwezelt-Hezerwater outcrop (Belgian loess belt). The fingerprints of the extractable SOM of different soil units are related to total organic carbon content, δ¹³C and grain-size analysis. Combined results indicate that the lower unit of the humic complex reflects a stable soil surface, allowing SOM build-up, intensive microbial activity and high decomposition. Higher in the profile, decomposition and microbial activity decrease. This is supported by a shift in the isotopic signal, an increased U ratio and evidence of wildfires. Although the chemical composition of the extracted SOM differed greatly from recent SOM, fingerprinting yielded detailed new information on SOM degree of decomposition and microbial contribution, allowing the reconstruction of palaeo-environmental conditions during pedogenesis.     

Prediction of the Rock Mass Diggability Index by Using Fuzzy Clustering-Based,ANN and Multiple Regression Methods

Rock mass classification systems are one of the most common ways of determining rock mass excavatability and related equipment assessment. However, the strength and weak points of such rating-based classifications have always been questionable. Such classification systems assign quantifiable values to predefined classified geotechnical parameters of rock mass. This causes particular ambiguities, leading to the misuse of such classifications in practical applications. Recently, intelligence system approaches such as artificial neural networks (ANNs) and neuro-fuzzy methods, along with multiple regression models, have been used successfully to overcome such uncertainties. The purpose of the present study is the construction of several models by using an adaptive neuro-fuzzy inference system (ANFIS) method with two data clustering approaches, including fuzzy c-means (FCM) clustering and subtractive clustering, an ANN and non-linear multiple regression to estimate the basic rock mass diggability index. A set of data from several case studies was used to obtain the real rock mass diggability index and compared to the predicted values by the constructed models. In conclusion, it was observed that ANFIS based on the FCM model shows higher accuracy and correlation with actual data compared to that of the ANN and multiple regression. As a result, one can use the assimilation of ANNs with fuzzy clustering-based models to construct such rigorous predictor tools.     

Predicting saturated hydraulic conductivity using particle swarm optimization and genetic algorithm

Soil saturated hydraulic conductivity (K_s) is considered as soil basic hydraulic property, and its precision estimation is a key element in modeling water flow and solute transport processes both in the saturated and vadose zones. Although some predictive methods (e.g., pedotransfer functions, PTFs) have been proposed to indirectly predict K_s, the accuracy of these methods still needs to be improved. In this study, some easily available soil properties (e.g., particle size distribution, organic carbon, calcium carbonate content, electrical conductivity, and soil bulk density) are employed as input variables to predict K_s using a fuzzy inference system (FIS) trained by two different optimization techniques: particle swarm optimization (PSO) and genetic algorithm (GA). To verify the derived FIS, 113 soil samples were taken, and their required physical properties were measured (113 sample points?×?7 factors?=?791 input data). The initial FIS is compared with two methods: FIS trained by PSO (PSO-FIS) and FIS trained by GA (GA-FIS). Based on experimental results, all three methods are compared according to some evaluation criteria including correlation coefficient (r), modeling efficiency (EF), coefficient of determination (CD), root mean square error (RMSE), and maximum error (ME) statistics. The results showed that the PSO-FIS model achieved a higher level of modeling efficiency and coefficient of determination (R²) in comparison with the initial FIS and the GA-FIS model. EF and R² values obtained by the developed PSO-FIS model were 0.69 and 0.72, whereas they were 0.63 and 0.54 for the GA-FIS model. Moreover, the results of ME and RMSE indices showed that the PSO-FIS model can estimate soil saturated hydraulic conductivity more accurate than the GA-FIS model with ME?=?10.4 versus 11.5 and RMSE?=?5.2 versus 5.5 for PSO-FIS and GA-FIS, respectively.     

Effects of soil organic matter composition on unfrozen water content and heterotrophic CO2 production of frozen soils

Several recent studies have highlighted the importance of soil organic matter (SOM) mineralization at high latitudes during winter for ecosystem carbon (C) balances, and the ability of the soil to retain unfrozen water at sub-zero temperatures has been shown to be a major determinant of C mineralization rates. Further, SOM is believed to strongly influence the liquid water contents in frozen surface layers of boreal forest soils and tundra, but the mechanisms and specific factors involved are currently unknown. Here we evaluate the effects of the chemical composition of SOM on the amount of unfrozen water, the pore size equivalents in which unfrozen water can exist, and the microbial heterotrophic activity at sub-zero temperatures in boreal forest soils. To do this, we have characterized the chemical composition of SOM in forest soil samples (surface O-horizons) using solid state CP-MAS (cross polarization magic angle spinning) NMR spectroscopy. The acquired information was then used to elucidate the extent to which different fractions of SOM can explain the observed variations in unfrozen water content, pore size equivalents, and biogenic CO₂ production rates in the examined soil samples under frozen conditions (−4 °C). The data evaluation was done by the use of principal component analysis (PCA) and projections to latent structures by means of partial least square (PLS). We conclude that aromatic, O-aromatic, methoxy/N-alkyl and alkyl C are the major SOM components affecting frozen boreal forest soil’s ability to retain unfrozen water and sustain heterotrophic activity (95% confidence level). Our results reveal that solid carbohydrates have a significant negative impact (95% confidence level) on CO₂ production in frozen boreal spruce forest soils, in contrast to the positive effects of carbohydrate polymers during unfrozen conditions. We conclude that the hierarchy of environmental factors controlling SOM mineralization changes as soils freeze. The effect of SOM composition on pore size distribution and unfrozen water content has a superior influence on SOM mineralization and hence on heterotrophic CO₂ production of frozen soils.     

Evaluating distribution pattern of petrophysical properties and their monitoring under a hybrid intelligent based method in southwest oil field of Iran

This study deals with reservoir characterization based on well log data using an unsupervised self-organizing map (SOM) and supervised neural network algorithms with the aim of clustering log responses into reservoir facies of an oil field located in southwest of Iran. In order to promote and justify the quality control and quantify spatial relationships for petrophysical properties, some of neural network-based approaches were introduced such as the SOMs as the intelligent clustering method compared with other hybrid methods, principal component analysis networks (PCANs) and multilayer perceptron (MLP) and statistical clustering (CA) methods. The results obtained from all the abovementioned methods are compared to each other, and the best option is selected based on accuracy and capabilities of clustering and estimation of the petrophysical data, concluding that for predicting any characteristic of the reservoirs, the appropriate network should be chosen and a unique network cannot be convenient for all of them. Accordingly, the SOM clustering technique was employed to classify the reservoir rocks. Based on the SOM visualization, the reservoir rocks were classified into six facies associated with specific petrophysical properties; among them, F6 expressed the best reservoir quality which is characterized by the low amount of density, highest DT, high amount of neutron porosity (NPHI), and lowest GR response. Ultimately, the performance of all the methods was compared to estimate the porosity and permeability within each facies. The results revealed the preference and reliability of PCAN in predicting porosity and confirmed the capability of MLP in permeability prediction. This study also indicates that neuro-prediction of formation properties using well log data is a feasible methodology for optimization of exploration programs and reduction of expenditure by delineating potentially oil-bearing strata with higher accuracy and lower expenses. The resulting neural net-based model can be used as a powerful and distributive system to reduce the high impact of risk in similar fields.     

A Comparison between a Modified Counter Propagation Network and an Extended Self-Organizing Map in Remotely Sensed Data Classification

A modified counter propagation network model and an extended self-organizing map model have the same three-layer network architecture while employing slightly different learning rules. Their network architecture comprises an input layer, a Kohonen layer and an output layer. The neurons between two neighboring layers are fully connected and the neighboring neurons within the Kohonen layer also have neighborhood connections. The modified counter propagation network model employs the Kohonen algorithm to train the Kohonen layer while using the Widrow–Hoff rule to train the output layer. However, the extended self-organizing map model applies a modified Kohonen’s learning rule to train both the Kohonen layer and the output layer. This paper compares the performances of these two models in supervised classification of remotely sensed data. The training results show that compared to the extended self-organizing map model, the modified counter propagation model has faster learning speed but larger output errors. The classification results indicate that the extended self-organizing map model has a faster classification speed and a much higher classification precision than the modified counter propagation model.     

地质分类中一种新的模糊聚类方法

没有分类,就没有鉴别,也就无法认识客观事物.因此,分类是各个领域中经常遇到的基本问题之一.以定量分类为特征的聚类分析法,和一般的定性分析方法相比,失误的可能性小得多.因此,在分类问题上,该方法已经起了很大作用.但是,和其它任何方法一样,聚类分析法也有局限性.1.聚类分析法是建立在普通集合论基础之上的.任一集合A的特征函数C_Λ(X)只能用二值变量来描述.即:     

基于自组织神经网络的污染场地多监测指标相关性分析

为查明场地污染分布特征,需对场地土壤和地下水进行钻探取样,按规范的检测指标进行逐一测试。在初查和详查阶段将获得大量的土壤和地下水污染数据,数据样本数量大、监测指标多,数据结构复杂,如何从场地大数据中提取价值信息已成为研究热点。以某有机污染场地为例,基于自组织映射神经网络（SOM）和K均值算法开展大数据分析,深入探讨地下水和土壤中各污染指标间的相关性。结果表明:（1）基于自组织映射神经网络的大数据分析可快速挖掘复杂多维的污染场地监测数据,有效完成关键信息的提取;（2）地下水中污染检出指标存在显著的聚类特征,同一聚类中的污染指标具备相似的空间分布特征。对场地污染物检测采取先分类后分级的优化筛选策略,减少污染物检测指标数目,从而有效降低场地检测费用;（3）土壤和地下水中污染检出指标存在良好的空间相关性,这与该污染场地地下水渗流速度缓慢有关。土壤和地下水污染检出指标空间分布的相关性,有助于场地污染源的追溯。     

Evaluation of graphical and multivariate statistical methods for classification of water chemistry data

A robust classification scheme for partitioning water chemistry samples into homogeneous groups is an important tool for the characterization of hydrologic systems. In this paper we test the performance of the many available graphical and statistical methodologies used to classify water samples including: Collins bar diagram, pie diagram, Stiff pattern diagram, Schoeller plot, Piper diagram, Q-mode hierarchical cluster analysis, K-means clustering, principal components analysis, and fuzzy k-means clustering. All the methods are discussed and compared as to their ability to cluster, ease of use, and ease of interpretation. In addition, several issues related to data preparation, database editing, data-gap filling, data screening, and data quality assurance are discussed and a database construction methodology is presented. The use of graphical techniques proved to have limitations compared with the multivariate methods for large data sets. Principal components analysis is useful for data reduction and to assess the continuity/overlap of clusters or clustering/similarities in the data. The most efficient grouping was achieved by statistical clustering techniques. However, these techniques do not provide information on the chemistry of the statistical groups. The combination of graphical and statistical techniques provides a consistent and objective means to classify large numbers of samples while retaining the ease of classic graphical presentations. Electronic Publication     

Characteristics and distributions of humic acids in two soil profiles of the southwest China Karst area

Characteristics and distributions of humic acid (HA) and soil organic matter (SOM) in a yellow soil profile and a limestone soil profile of the southwest China Karst area were systematically investigated to reveal their evolutions in different soils of the study area. The results showed that characteristics and distribution of SOM along the two soil profiles were notably different. Total organic carbon (TOC) contents of soil samples decreased just slightly along the limestone soil profile but sharply along the yellow soil profile. TOCs of the limestone soils were significantly higher than those of the corresponding yellow soils, and C/N ratios of SOMs showed a similar variation trend to that of TOCs, indicating that SOM can be better conserved in the limestone soil than in the yellow soil. The soil humic acids were exhaustively extracted and further fractionated according to their apparent molecular weights using ultrafiltration techniques to explore underlying conservation mechanisms. The result showed that C/N ratios of HAs from different limestone soil layers were relatively stable and that large molecular HA fractions predominated the bulk HA of the top soil, indicating that HA in the limestone profile was protected while bio and chemical degradations were retarded. Combined with organic elements contents and mineral contents of two soils, we concluded that high calcium contents in limestone soils may play a key role in SOM conservation by forming complexation compounds with HAs or/and enclosing SOMs with hypergene CaCO₃ precipitation.     

自适应模糊神经网络在膨胀土胀缩等级分类中的应用

针对膨胀土胀缩等级分类这一多因素评判问题,在分析自适应模糊神经网络原理及结构的基础上,利用减法聚类获得模糊推理规则数目,确定网络结构,建立了适用于膨胀土分类的自适应模糊神经网络,并将其应用于两个实际工程的膨胀土分类中,取得了良好的效果。研究结果表明,自适应模糊神经网络能实现BP网络和模糊综合评判的分类功能,而且比BP网络具有更透明的网络结构、比模糊综合评判更具学习功能,在膨胀土胀缩等级的分类中显示出较强的适用性。     

Multi-scale aquifer characterization and groundwater flow model parameterization using direct push technologies

Direct push (DP) technologies are typically used for cost-effective geotechnical characterization of unconsolidated soils and sediments. In more recent developments, DP technologies have been used for efficient hydraulic conductivity (K) characterization along vertical profiles with sampling resolutions of up to a few centimetres. Until date, however, only a limited number of studies document high-resolution in situ DP data for three-dimensional conceptual hydrogeological model development and groundwater flow model parameterization. This study demonstrates how DP technologies improve building of a conceptual hydrogeological model. We further evaluate the degree to which the DP-derived hydrogeological parameter K, measured across different spatial scales, improves performance of a regional groundwater flow model. The study area covers an area of ~60 km² with two overlying, mainly unconsolidated sand aquifers separated by a 5–7 m thick highly heterogeneous clay layer (in north-eastern Belgium). The hydrostratigraphy was obtained from an analysis of cored boreholes and about 265 cone penetration tests (CPTs). The hydrogeological parameter K was derived from a combined analysis of core and CPT data and also from hydraulic direct push tests. A total of 50 three-dimensional realizations of K were generated using a non-stationary multivariate geostatistical approach. To preserve the measured K values in the stochastic realizations, the groundwater model K realizations were conditioned on the borehole and direct push data. Optimization was performed to select the best performing model parameterization out of the 50 realizations. This model outperformed a previously developed reference model with homogeneous K fields for all hydrogeological layers. Comparison of particle tracking simulations, based either on the optimal heterogeneous or reference homogeneous groundwater model flow fields, demonstrate the impact DP-derived subsurface heterogeneity in K can have on groundwater flow and solute transport. We demonstrated that DP technologies, especially when calibrated with site-specific data, provide high-resolution 3D subsurface data for building more reliable conceptual models and increasing groundwater flow model performance.     

A novel fuzzy <Emphasis Type="Italic">K</Emphasis>-nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using GIS

This research represents a novel soft computing approach that combines the fuzzy k-nearest neighbor algorithm (fuzzy k-NN) and the differential evolution (DE) optimization for spatial prediction of rainfall-induced shallow landslides at a tropical hilly area of Quy Hop, Vietnam. According to current literature, the fuzzy k-NN and the DE optimization are current state-of-the-art techniques in data mining that have not been used for prediction of landslide. First, a spatial database was constructed, including 129 landslide locations and 12 influencing factors, i.e., slope, slope length, aspect, curvature, valley depth, stream power index (SPI), sediment transport index (STI), topographic ruggedness index (TRI), topographic wetness index (TWI), Normalized Difference Vegetation Index (NDVI), lithology, and soil type. Second, 70 % landslide locations were randomly generated for building the landslide model whereas the remaining 30 % landslide locations was for validating the model. Third, to construct the landslide model, the DE optimization was used to search the optimal values for fuzzy strength (fs) and number of nearest neighbors (k) that are the two required parameters for the fuzzy k-NN. Then, the training process was performed to obtain the fuzzy k-NN model. Value of membership degree of the landslide class for each pixel was extracted to be used as landslide susceptibility index. Finally, the performance and prediction capability of the landslide model were assessed using classification accuracy, the area under the ROC curve (AUC), kappa statistics, and other evaluation metrics. The result shows that the fuzzy k-NN model has high performance in the training dataset (AUC?=?0.944) and validation dataset (AUC?=?0.841). The result was compared with those obtained from benchmark methods, support vector machines and J48 decision trees. Overall, the fuzzy k-NN model performs better than the support vector machines and the J48 decision trees models. Therefore, we conclude that the fuzzy k-NN model is a promising prediction tool that should be used for susceptibility mapping in landslide-prone areas.     

On Distance Measures for the Fuzzy K-means Algorithm for Joint Data

Summary  The analysis of data collected on rock discontinuities often requires that the data be separated into joint sets or groups. A statistical tool that facilitates the automatic identification of groups of clusters of observations in a data set is cluster analysis. The fuzzy K-means cluster technique has been successfully applied to the analysis of joint survey data. As is the case with all clustering algorithms, the results of an analysis performed with the fuzzy K-means algorithm for discontinuity data are highly dependent on the distance metric employed in the analysis. This paper explores the significant issues surrounding the choice and use of various distance measures for clustering joint survey data. It also proposes an analogue of the Mahalanobis distance norm (used for data in Euclidean space) for clustering spherical data. Sample applications showing the greater flexibility and power of the new distance measure over the originally proposed distance metric for spherical data are given in the paper.     

A comparative study of the molecular composition of a grassland soil with adjacent unforested and afforested moorland ecosystems

On-line thermally assisted hydrolysis and methylation (THM) in the presence of both unlabelled and ¹³C-labelled tetramethylammonium hydroxide (TMAH) was used to assess the relative contributions of phenolics (lignin, demethylated lignin and non-lignin phenolics) in a peaty gley soil profile beneath an unimproved grassland (LL), from a study site located at Harwood (Northumberland, northeast England, UK). This site also includes an unforested moorland (ML) and a second rotation Sitka spruce stand (SS). The common lignin proxies have been corrected for contributions of non-lignin phenols and demethylated lignin in the LL ecosystem and then compared with those from the ML and SS ecosystems. The phenolic compositions from the contributory vegetation inputs (i.e. grasses, heather and Sitka spruce) to all three soils (LL, ML and SS) were also analysed. By using ¹³C-labelled TMAH it was possible to show that the chemical composition of soil organic matter (SOM) reflected the different vegetation inputs in each of the L/F layers but these characteristics were lost from the deeper organic and mineral layers. Similar changes in the yield of lignin monomers (Λ) with increasing soil depth were displayed in the LL soil profile as reported previously in the ML soil in that no maxima were observed in these amount-depth profiles. The tannin input to the LL soil is low and as a consequence, unlike the ML and SS soils, there is no progressive decrease in the amounts of these non-lignin phenolics with increasing depth. Finally the methylated carbohydrate derivatives (MC) become more abundant relative to the phenolics with increasing soil depth in all three ecosystems (LL, ML and SS).     

A comparative study of the molecular composition of a grassland soil with adjacent unforested and afforested moorland ecosystems

On-line thermally assisted hydrolysis and methylation (THM) in the presence of both unlabelled and ¹³C-labelled tetramethylammonium hydroxide (TMAH) was used to assess the relative contributions of phenolics (lignin, demethylated lignin and non-lignin phenolics) in a peaty gley soil profile beneath an unimproved grassland (LL), from a study site located at Harwood (Northumberland, northeast England, UK). This site also includes an unforested moorland (ML) and a second rotation Sitka spruce stand (SS). The common lignin proxies have been corrected for contributions of non-lignin phenols and demethylated lignin in the LL ecosystem and then compared with those from the ML and SS ecosystems. The phenolic compositions from the contributory vegetation inputs (i.e. grasses, heather and Sitka spruce) to all three soils (LL, ML and SS) were also analysed. By using ¹³C-labelled TMAH it was possible to show that the chemical composition of soil organic matter (SOM) reflected the different vegetation inputs in each of the L/F layers but these characteristics were lost from the deeper organic and mineral layers. Similar changes in the yield of lignin monomers (Λ) with increasing soil depth were displayed in the LL soil profile as reported previously in the ML soil in that no maxima were observed in these amount-depth profiles. The tannin input to the LL soil is low and as a consequence, unlike the ML and SS soils, there is no progressive decrease in the amounts of these non-lignin phenolics with increasing depth. Finally the methylated carbohydrate derivatives (MC) become more abundant relative to the phenolics with increasing soil depth in all three ecosystems (LL, ML and SS).