Linear Feature Mapping Research Articles

Sub-basalt hydrocarbon prospect assessment in Peninsular India using seaward dipping reflectors

Conventionally, volcanic margins have been considered devoid of hydrocarbon, but many discoveries and research in recent past have proved the presence of hydrocarbon prospects within them. However, hydrocarbon exploration within volcanic margin is constrained by seismic imaging. Further, identification of continent to oceanic boundary (COB) is critical to hydrocarbon search as hydrocarbon is found mostly over continental crust. Seaward dipping reflectors, associated with volcano-sedimentary sequences, located along rifted continental margins and represented by highly dipping strong amplitude seismic reflectors, play an important role to study volcanic margins. Keeping in view the problems of hydrocarbon exploration within volcanics, seaward dipping reflectors (SDRs) in Indian context have been studied in detail in this paper. It facilitates mapping of continent-oceanic boundary (COB). It has impact on hydrocarbon prospectivity in volcanic margins as it can provide formidable seal and secondary induced maturity. In India, several discoveries have been made in volcanic margins along west coast, from weathered, fractured basalt and sub-basalt sediments. In the present work, long offset regional seismic data (18 dip lines and 1 cross line), along east coast, have been interpreted. SDRs help in hydrocarbon prospectivity assessment for both discrimination of interbedded sediments and preferential accumulation of hydrocarbon. Mapping of linear features not only helped to demarcate COB but also their presence at 6–10 km depth. For the first time, such comprehensive study on SDRs has been done along Indian peninsular region with indications for hydrocarbon prospectivity at deeper levels along Indian peninsular region.

Controlling factors and mapping of linear erosive features in Santa Maria river watershed –RS

Water erosion is one of the main causes of soil degradation and linear erosion mapping is one of the essential tools for its monitoring. In light of this, the goal from this research is to map the linear erosive features (LEF) of the Santa Maria River Basin (BHRSM), southwest region of RS, and understand which environmental factors are controlling or triggering erosion. In western RS there is a considerable concern associated with erosive processes that occupy large extensions and cause changes in the dynamics of use and changes in the environment. Data on geology, soils, hypsometry, slope, land use, drainage channels and roads were analyzed in a GIS environment. The erosive processes in BHRSM are inherent to the less consolidated sandy substrate, configuring a natural process. The relief energy for the incision of water flows is meaningful, since the LEF are related to moderate slope in portions of medium altitudes. However, anthropogenic action accelerates erosive processes. It is noteworthy that the increase in the area occupied by crops, doubled in the period of 20 years, configuring a new landscape and dynamics for BHRSM, demanding special attention to this region that tends to intensify the erosivy processes

A westerly wind dominated Puna Plateau during deposition of upper Pleistocene loessic sediments in the subtropical Andes, South America

The Tafí del Valle depression (~27° S) in the eastern Andes of Argentina provides a record of late Pleistocene dust deposition in the subtropics of South America. We present large-n U-Pb geochronology data for detrital zircons from upper Pleistocene loess-paleosol deposits. When compared to regional data, the age spectra from the Tafí del Valle samples are most like the southern Puna Plateau, supporting derivation largely from the west and northwest. This runs counter to hypotheses suggesting these loessic sediments were derived from the low elevation plains to the east or extra-Andean Patagonia. Mapping of linear wind erosion features on the Puna Plateau yield a mean orientation of 125.7° (1 s.d. = 12.4°). These new data and existing records are consistent with a westerly-northwesterly dominated (upper- and lower-level) wind system over the southern Puna Plateau (to at least ~27° S) during periods of high dust accumulation in Tafí del Valle.

Mapping of linear road features with the inverse visual detector observation model

The paper proposes an algorithm for mapping linear features detected on the roadway — road marking lines, curbs, road boundaries. The algorithm is based on a mapping method with an inverse observation model. An inverse observation model is proposed to take into account the spatial error of the linear feature visual detector. The influence of various parameters of the model on the resulting quality of mapping was studied. The mapping algorithm was tested on data recorded on an autonomous vehicle while driving at the test site. The quality of the mapping algorithm was assessed according to several quality metrics known from the literature. In addition, the mapping problem was considered as a binary classification problem, in which each map cell may or may not contain the desired feature, and the ROC curve and AUC-ROC metric were used to assess the quality. As a naive solution, a map was built containing all detected linear features without any additional filtering. For the map built on the basis of the raw data, the AUC-ROC was 0.75, and as a result of applying the algorithm, the value of 0.81 was reached. The experimental results have confirmed that the proposed algorithm can effectively filter noise and false-positive detections of the detector, which confirms the applicability of the proposed algorithm and the inverse observation model for solving practical problems. Key words Linear features, mapping, inverse observation model, road map, autonomous vehicle, digital road map.

Volcano dynamics vs tectonics on Mars: evidence from Pavonis Mons

Volcanic activity is widespread within the inner Solar system and it can be commonly observed on rocky planets. In this work, we analyse the structures of Pavonis Mons in the Tharsis volcanic province of Mars by performing structural mapping, azimuth, and topographic distribution of linear features on the flanks of Pavonis, such as grabens and pit chains. We tested whether their formation is to be ascribed to the volcano dynamics and magmatic activity or the tectonics. Through the length size distribution and fractal clustering analyses of the structural features, we found that large grabens are vertically confined in the upper mechanical layers of the brittle crust whereas pit chains penetrate the whole crust up to the magmatic source, indicating that they can be considered the main feeders of Pavonis Mons. We inverted the topography with dykes and faults models to test whether grabens at the surface are the expression of intrusions at depth and we suggest that thin dykes inducing normal faulting are the most likely mechanism. Furthermore, two azimuthal distribution of the grabens are identified: concentric grabens occur on the volcano summit while linear grabens at its base show NE-SW trend as the Tharsis Mons volcanos alignment. The occurrence of linear grabens suggests that Pavonis likely experienced a phase of active rifting with the formation of such structures, followed by a phase of volcano growth and concentric magma intrusions when volcano and magma chamber dynamics prevailed.

Image Steganalysis via Random Subspace Fisher Linear Discriminant Vector Functional Link Network and Feature Mapping

As the number of methods for implementing steganography (the hiding of data within a cover) increases, steganalysis, which can detect the presence of such hidden data, is also accompanied by an increase. To improve the accuracy of detection, we propose a new algorithm for processing feature that makes two optimizations into a random vector functional link (RVFL) network. The first optimization locates the processing phase of RVFL, where we model the eigenspectrum by the eigenvalue distribution of the scatter matrix. This eigenspectrum is used to generate the transpose matrix and obtain final features after feature reduction. The second optimization is the use of the random subspace Fisher linear discriminant (FLD) instead of random weights in RVFL. The weights between the input and enhancement nodes more accurately represent the relative importance of the features. The experiments compare the performance of other classifiers with the proposed method using five high-dimensional features. It is shown that the proposed method outperforms other classifiers in these steganalysis methods.

Large-scale geomorphological mapping as a tool to detect structural features: the case of Mt. Prinzera ophiolite rock mass (Northern Apennines, Italy)

ABSTRACTThis paper presents a geomorphologic map of the Mt. Prinzera ultramafic rock complex (Parma Province, Emilia-Romagna Region, Italy) mapped at large scale (1:2500). The map is the first contribution to the detailed geomorphologic knowledge of the area within the framework of a multidisciplinary approach used to analyse Mt. Prinzera as a hard rock aquifer case study. The geomorphologic mapping was carried out by field survey and aerial photograph analysis. All the surface processes and landforms were considered, with particular attention to structural geomorphologic features. The mapping of linear morphologic features was considered useful for revealing the presence of sets of joints otherwise difficult to detect due to surface weathering. Sets of rock discontinuities are important for water seepage and percolation and their identification is crucial for developing the hydrogeological conceptual model of the aquifer.

Support vector machines with piecewise linear feature mapping

As the simplest extension to linear classifiers, piecewise linear (PWL) classifiers have attracted a lot of attention, because of their simplicity and classification capability. In this paper, a PWL feature mapping is introduced by investigating the property of the PWL classification boundary. Then support vector machines (SVM) with PWL feature mappings are proposed, called PWL-SVMs. In this paper, it is shown that some widely used PWL classifiers, such as k-nearest-neighbor, adaptive boosting of linear classifier and intersection kernel support vector machine, can be represented by the proposed feature mapping. That means the proposed PWL-SVMs at least can achieve the performance of the above PWL classifiers. Moreover, PWL-SVMs enjoy good properties of SVM and the performance on numerical experiments illustrates the effectiveness. Then some extensions are discussed and the application of PWL-SVMs can be expected.

Predictions of Retention Factors for Some Organic Nucleuphiles in Complexation Gas Chromatography

In the present work, the quantitative structure-retention relationship (QSRR) was used to predict the gas chromatographic retention factors of some organic nucleuphile on chemically modified stationary phase by complexes of Cu (II) with amino groups. The gravitation index, relative negative charge surface area, C component of moment of inertia and weighted negative charged partial surface area are selected as the most relevant descriptors from the pool of descriptors. These descriptors were used for developing multiple linear regression (MLR) and artificial neural network (ANN) models as linear and nonlinear feature mapping techniques. The root mean square errors (RMES) in calculation of retention factors for training, internal and external test set are 0.242, 0.295, and 0.240, respectively for MLR model, and for ANN model the RMSE for training, internal and external test set are; 0.084, 0.108, and 0.176. The ANN and MLR model were further examined by cross validation test, which obtained statistics of Q2 = 0.82 and SPRESS = 0.22 for MLR model and Q2 = 0.97, SPRESS = 0.07 for ANN model. Comparison between these results and other statistics of ANN and MLR models revealed the superiority of ANN over MLR model.

Estimation of the volume of distribution of some pharmacologically important compounds from their structural descriptors

Quantitative structure-activity relationship (QSAR) approaches were used to estimate the volume of distribution (Vd) using an artificial neural network (ANN). The data set consisted of the volume of distribution of 129 pharmacologically important compounds, i.e., benzodiazepines, barbiturates, NSAIDs, tricyclic anti-depressants and some antibiotics, such as betalactams, tetracyclines and quinolones. The descriptors, which were selected by stepwise variable selection methods, were: the Moriguchi octanol-water partition coefficient; the 3D-MoRSEsignal 30, weighted by atomic van der Waals volumes; the fragmentbased polar surface area; the d COMMA2 value, weighted by atomic masses; the Geary autocorrelation, weighted by the atomic Sanderson electronegativities; the 3D-MoRSE - signal 02, weighted by atomic masses, and the Geary autocorrelation - lag 5, weighted by the atomic van der Waals volumes. These descriptors were used as inputs for developing multiple linear regressions (MLR) and artificial neural network models as linear and non-linear feature mapping techniques, respectively. The standard errors in the estimation of Vd by the MLR model were: 0.104, 0.103 and 0.076 and for the ANN model: 0.029, 0.087 and 0.082 for the training, internal and external validation test, respectively. The robustness of these models were also evaluated by the leave-5-out cross validation procedure, that gives the statistics Q2 = 0.72 for the MLR model and Q2 = 0.82 for the ANN model. Moreover, the results of the Y-randomization test revealed that there were no chance correlations among the data matrix. In conclusion, the results of this study indicate the applicability of the estimation of the Vd value of drugs from their structural molecular descriptors. Furthermore, the statistics of the developed models indicate the superiority of the ANN over the MLR model.

Automatic Mapping of Linear Woody Vegetation Features in Agricultural Landscapes Using Very High Resolution Imagery

Automatic mapping and monitoring of agricultural landscapes using remotely sensed imagery has been an important research problem. This paper describes our work on developing automatic methods for the detection of target landscape features in very high spatial resolution images. The target objects of interest consist of linear strips of woody vegetation that include hedgerows and riparian vegetation that are important elements of the landscape ecology and biodiversity. The proposed framework exploits the spectral, textural, and shape properties of objects using hierarchical feature extraction and decision-making steps. First, a multifeature and multiscale strategy is used to be able to cover different characteristics of these objects in a wide range of landscapes. Discriminant functions trained on combinations of spectral and textural features are used to select the pixels that may belong to candidate objects. Then, a shape analysis step employs morphological top-hat transforms to locate the woody vegetation areas that fall within the width limits of an acceptable object, and a skeletonization and iterative least-squares fitting procedure quantifies the linearity of the objects using the uniformity of the estimated radii along the skeleton points. Extensive experiments using QuickBird imagery from three European Union member states show that the proposed algorithms provide good localization of the target objects in a wide range of landscapes with very different characteristics.

A novel quantitative structure–activity relationship model for prediction of biomagnification factor of some organochlorine pollutants

The biomagnification factor (BMF) is an important property for toxicology and environmental chemistry. In this work, quantitative structure-activity relationship (QSAR) models were used for the prediction of BMF for a data set including 30 polychlorinated biphenyls and 12 organochlorine pollutants. This set was divided into training and prediction sets. The result of diversity test reveals that the structure of the training and test sets can represent those of the whole ones. After calculation and screening of a large number of molecular descriptors, the methods of stepwise multiple linear regression and genetic algorithm (GA) were used for the selection of most important and significant descriptors which were related to BMF. Then multiple linear regression and artificial neural network (ANN) techniques were applied as linear and non-linear feature mapping techniques, respectively. By comparison between statistical parameters of these methods it was concluded that an ANN model, which used GA selected descriptors, was superior over constructed models. Descriptors which were used by this model are: topographic electronic index, complementary information content, XY shadow/XY rectangle and difference between partial positively and negatively charge surface area. The standard errors for training and test sets of this model are 0.03 and 0.20, respectively. The degree of importance of each descriptor was evaluated by sensitivity analysis approach for the nonlinear model. A good results (Q (2) = 0.97 and SPRESS = 0.084) is obtained by applying cross-validation test that indicating the validation of descriptors in the obtained model in prediction of BMF for these compounds.

Analysis of Feature Extraction and Channel Compensation in a GMM Speaker Recognition System

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In this paper, several feature extraction and channel compensation techniques found in state-of-the-art speaker verification systems are analyzed and discussed. For the NIST SRE 2006 submission, cepstral mean subtraction, feature warping, RelAtive SpecTrAl (RASTA) filtering, heteroscedastic linear discriminant analysis (HLDA), feature mapping, and eigenchannel adaptation were incrementally added to minimize the system's error rate. This paper deals with eigenchannel adaptation in more detail and includes its theoretical background and implementation issues. The key part of the paper is, however, the post-evaluation analysis, undermining a common myth that “the more boxes in the scheme, the better the system.” All results are presented on NIST Speaker Recognition Evaluation (SRE) 2005 and 2006 data. </para>

The relation between water-wells productivity and lineaments morphometry: selected zones from Lebanon

Several approaches have been applied to groundwater exploration. In some instances positive results occur, but this is not always the case. This led hydrogeologists to look for a credible approach upon which they can rely during preliminary surveys for groundwater exploration. The development of remote sensing tools along with GIS methodologies gave rise to many new approaches. Most significant among these is the mapping of linear features (lineaments), which appear on satellite images. These features mainly reflect fracture traces, faults or lithologic boundaries and, therefore, are considered as major hydrogeologic parameters to be taken into account. The aim of this paper is to define an empirical relationship between lineament morphometric properties and the productivity of water wells. The three major properties of lineaments, i.e. frequency (Lf), density (Ld) and fault-lineaments (Fl), were analysed using Landsat 7 ETM+ images and GIS techniques. The resultant maps were correlated with the location of water wells in 90 sites from different regions of Lebanon. The resulting output showed an obvious relationship between productive wells and their proximity to fault lineaments. The closer the location of water wells to a ault trace the higher the water productivity. However, this hypothesis can be preliminary utilized in surveys for groundwater exploration, notably in a terrain with intensive rock deformation like that in Lebanon.

Omission and Commission Errors in the Field Mapping of Linear Boundary Features: Implications for the Interpretation of Maps and Organization of Surveys

Phase 1 mapping has been used widely in the UK as a method of resource inventory, and as an aid to conservation management and planning. Phase 1 maps may also provide baseline information for studies of land use change by future generations of landscape ecologists and historians. Contemporary assessments of their accuracy are essential to allow their value to be judged both now and decades hence. The accuracy of Phase 1 mapping of man-made linear boundary features was quantified by comparing maps drawn by six experienced field surveyors with a ground-truth version correctly showing all features. Overall errors within maps varied from 11.2% to 96.9% between surveys. Most of the error was caused by the omission of boundaries, rather than the misclassification of boundaries whose presence was recorded (i.e. errors of commission). The likelihood of a boundary being mapped was positively related to its length, and walls were more likely to be mapped than fences. Linear features can be mapped accurately, but reliance on the discretion of the surveyors, and their interpretation of the survey manual, resulted in variable practice and incomplete data in all cases. If data on linear features are not required, the time saved could be used to improve the accuracy of mapping other habitats (a concern identified in other studies). In addition to the provision of more explicit guidance to surveyors, the reporting of estimates of mapping accuracy and precision are identified as important aspects of the survey technique which require greater attention than is currently the case.

Linear Feature Modeling with Curve Fitting: Parametric Polynomial Techniques

A decomposition model is described to model linear features sampled by manual digitization or field survey. The model consists of three components, original data, systematic pattern, and random error. Least squares and moving least squares techniques are introduced for polynomial curve fitting. Polynomial functions are proposed to represent linear features. The position deviation between sampled points and the polynomial function is used as an approximation of the random error. Experimental results are presented to show the effectiveness of the decomposition model. Potential applications of the model have been discussed including estimation of errors associated with points sampled along linear features, digital representation and mapping of linear features.