Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN)

An efficient and accurate method of generating landslide susceptibility maps is very important to mitigate the loss of properties and lives caused by this type of geological hazard. This study focuses on the development of an accurate and efficient method of data integration, processing and generation of a landslide susceptibility map using an ANN and data from ASTER images. The method contains two major phases. The first phase is the data integration and analysis, and the second is the Artificial Neural Network training and mapping. The data integration and analysis phase involve GIS based statistical analysis relating landslide occurrence to geological and DEM (digital elevation model) derived geomorphological parameters. The parameters include slope, aspect, elevation, geology, density of geological boundaries and distance to the boundaries. This phase determines the geological and geomorphological factors that are significantly correlated with landslide occurrence. The second phase further relates the landslide susceptibility index to the important geological and geomorphological parameters identified in the first phase through ANN training. The trained ANN is then used to generate a landslide susceptibility map. Landslide data from the 2004 Niigata earthquake and a DEM derived from ASTER images were used. The area provided enough landslide data to check the efficiency and accuracy of the developed method. Based on the initial results of the experiment, the developed method is more than 90% accurate in determining the probability of landslide occurrence in a particular area.     

Quantitative assessment of mineral resources with an application to petroleum geology

The probability of occurrence of natural resources, such as petroleum deposits, can be assessed by a combination of multivariate statistical and geostatistical techniques. The area of study is partitioned into regions that are as homogeneous as possible internally while simultaneously as distinct as possible. Fisher's discriminant criterion is used to select geological variables that best distinguish productive from nonproductive localities, based on a sample of previously drilled exploratory wells. On the basis of these geological variables, each wildcat well is assigned to the production class (dry or producer in the two-class case) for which the Mahalanobis' distance from the observation to the class centroid is a minimum. Universal kriging is used to interpolate values of the Mahalanobis' distances to all locations not yet drilled. The probability that an undrilled locality belongs to the productive class can be found, using the kriging estimation variances to assess the probability of misclassification. Finally, Bayes' relationship can be used to determine the probability that an undrilled location will be a discovery, regardless of the production class in which it is placed. The method is illustrated with a study of oil prospects in the Lansing/Kansas City interval of western Kansas, using geological variables derived from well logs.     

Multicriteria Geologic Data Analysis for Mineral Favorability Mapping: Application to a Metal Sulphide Mineralized Area,Ribeira Valley Metallogenic Province,Brazil

GIS techniques have been used in the evaluation of favorability for base-metal mineralization in an area comprising the Cerro Azul and Apiaí quadrangles (SG.22-X-B-IV and V, scale 1:100.000), Ribeira Valley, São Paulo and Paraná States, Brazil. Methods have been employed for selection and weighting of prospective variables when applying GIS techniques to a digital database consisting of geological, geochemical and airborne geophysics, and mineral occurrence information. The exploration variable selection and analysis were based on two mineralization models: (1) Panelas type, vein-type carbonate hosted, and (2) Perau type, sedimentary-exhalative. The overlay was performed by weighted linear combination (WLC) and order weighted average (OWA) methods. Both methods proved suitable for the study area, yielding similar results. The ordered weighted averaging analysis provided the best results, with favorability maps showing a large number of classes occupying relatively minor areas. In comparison, the weighted linear combination analysis produced more coherent results but without details for minor areas. The prospective parameters obtained are considered suitable for both Perau and Panelas types. Both methods are inexpensive, and are suitable for selection of prospective areas during geological surveys in areas similar to the studied one.     

From data gathering to resources assessment: A holistic view of petroleum geology

To integrate geological data to obtain an interpretation of the geology and natural resources of an area, we need a methodology that provides a holistic approach, addressing all basic issues in geological relations as well as uncertainties that arise with the evolution of basic geological knowledge of an area.In spite of the major role played by geoinformatics—the application of mathematics, statistics, and computer science to solve geological problems—we do not yet have a properly designed method for organizing geological data, including raw data, conceptual models, modeling results, and geological integration. Such a rapid method should provide for updating of existing interpretations based on new data or new theories.A systematic view of data integration and interpretation is important in petroleum exploration and petroleum engineering. We point out the more striking tools already available but of restricted use and some of the possible solutions for known problems that still require research and development.     

Spatial Modeling for Base-Metal Mineral Exploration Through Integration of Geological Data Sets

This study involves the integration of information interpreted from data sets such as LandsatTM, Airborne magnetic, geochemical, geological, and ground-based data of Rajpura—Dariba,Rajasthan, India through GIS with the help of (1) Bayesian statistics based on the weights ofevidence method and (2) a fuzzy logic algorithm to derive spatial models to target potentialbase-metal mineralized areas for future exploration. Of the 24 layers considered, five layers(graphite mica schist (GMS), calc-silicate marble (CALC), NE-SW lineament 0–2000 mcorridor (L4-NESW), Cu 200–250 ppm, and Pb 200–250 ppm) have been identified from theBayesian approach on the basis of contrast. Thus, unique conditions were formed based onthe presence and absence of these five map patterns, which are converted to estimate posteriorprobabilities. The final map, based on the same data used to determine the relationships, showsfour classes of potential zones of sulfide mineralization on the basis of posterior probability.In the fuzzy set approach, membership functions of the layers such as CALC, GMS, NE-SWlineament corridor maps, Pb, and Cu geochemical maps have been integrated to obtain thefinal potential map showing four classes of favorability index.     

The Geology and Petroleum Resources of Kansas: A Review From Alpha to Omega or From the Pleistocene to the Precambrian

The geological story of Kansas is told through the rocks that are present. It is a simple story in generalities but complex in detail. Knowing the story, gives insight into understanding the occurrence and location of possible economic valuable minerals, such as petroleum. This is a brief review of Kansas geology with respect to the known occurrence of oil and gas. Kansas is part of the Midcontinent oil province with oil having been discovered 150 years ago and commercial production commencing in 1873. Although many prospects remain in Kansas, the state has gone from the number 1 producer in the U.S. in 1916 to 8th today. Exploration for new oil and gas production therefore is going to have to be more imaginative and utilize new approaches and techniques to find the elusive petroleum. There are possibilities however for the prospector who can search diligently. Although the big fields probably have been discovered, the prospects today are deeper, in more undetectable traps, and in essentially untested places.     

Artificial Neural Networks for Mineral-Potential Mapping: A Case Study from Aravalli Province,Western India

This paper describes a GIS-based application of a radial basis functional link net (RBFLN) to map the potential of SEDEX-type base metal deposits in a study area in the Aravalli metallogenic province (western India). Available public domain geodata of the study area were processed to generate evidential maps, which subsequently were encoded and combined to derive a set of input feature vectors. A subset of feature vectors with known targets (i.e., either known mineralized or known barren locations) was extracted and divided into (a) a training data set and (b) a validation data set. A series of RBFLNs were trained to determine the network architecture and estimate parameters that mapped the maximum number of validation vectors correctly to their respective targets. The trained RBFLN that gave the best performance for the validation data set was used for processing all feature vectors. The output for each feature vector is a predictive value between 1 and 0, indicating the extent to which a feature vector belongs to either the mineralized or the barren class. These values were mapped to generate a predictive classification map, which was reclassified into a favorability map showing zones with high, moderate and low favorability for SEDEX-type base metal deposits in the study area. The method demarcates successfully high favorability zones, which occupy 6% of the study area and contain 94% of the known base metal deposits.     

Mineral-Potential Mapping for MVT Deposits with Limited Data Sets Using Landsat Data and Geological Evidence in the Borden Basin, Northern Baffin Island, Nunavut, Canada

Posterior probabilities of occurrence for Zn-Pb Mississippi Valley Type (MVT) mineralization were calculated based on evidence maps derived from regional geology, Landsat-TM, RADARSAT-1, a digital elevation model and aeromagnetic data sets in the Borden Basin of northern Baffin Island, Canada. The vector representation of geological contacts and fault traces were refined according to their characteristics identified in Landsat-TM, RADARSAT-1, DEM, slope, aspect, and shaded relief data layers. Within the study area, there is an association between the occurrence of MVT mineralization and proximity to the contact of platformal carbonates and shale units of the adjacent geological formation. A spatial association also tends to exist between mineralization and proximity to E-W and NW-SE trending faults. The relationships of known MVT occurrences with the geological features were investigated by spatial statistical techniques to generate evidence maps. Supervised classification and filtering were applied to Landsat-TM data to divide the Society Cliffs Formation into major stratigraphic subunits. Because iron oxides have been observed at some of the MVT occurrences within the Borden Basin, Landsat-TM data band ratio (3/1) was calculated to highlight the potential presence of iron-oxides as another evidence map. Processed Landsat-TM data and other derived geological evidence maps provided useful indicators for identifying areas of potential MVT mineralization. Weights of evidence and logistic regression were used independently to integrate and generate posterior probability maps showing areas of potential mineralization based on all derived evidence maps. Results indicate that in spite of the lack of important data sets such as stream or lake sediment geochemistry, Landsat-TM data and regional geological data can be useful for MVT mineral-potential mapping.     

Evaluation of Weights of Evidence to Predict Epithermal-Gold Deposits in the Great Basin of the Western United States

The weights-of-evidence method provides a simple approach to the integration of diverse geologic information. The application addressed is to construct a model that predicts the locations of epithermal-gold mineral deposits in the Great Basin of the western United States. Weights of evidence is a data-driven method requiring known deposits and occurrences that are used as training sites in the evaluated area. Four hundred and fifteen known hot spring gold–silver, Comstock vein, hot spring mercury, epithermal manganese, and volcanogenic uranium deposits and occurrences in Nevada were used to define an area of 327.4 km² as training sites to develop the model. The model consists of nine weighted-map patterns that are combined to produce a favorability map predicting the distribution of epithermal-gold deposits. Using a measure of the association of training sites with predictor features (or patterns), the patterns can be ranked from best to worst predictors. Based on proximity analysis, the strongest predictor is the area within 8 km of volcanic rocks younger than 43 Ma. Being close to volcanic rocks is not highly weighted, but being far from volcanic rocks causes a strong negative weight. These weights suggest that proximity to volcanic rocks define where deposits do not occur. The second best pattern is the area within 1 km of hydrothermally altered areas. The next best pattern is the area within 1 km of known placer-gold sites. The proximity analysis for gold placers weights this pattern as useful when close to known placer sites, but unimportant where placers do not exist. The remaining patterns are significantly weaker predictors. In order of decreasing correlation, they are: proximity to volcanic vents, proximity to east-west to northwest faults, elevated airborne radiometric uranium, proximity to northwest to west and north-northwest linear features, elevated aeromagnetics, and anomalous geochemistry. This ordering of the patterns is a function of the quality, applicability, and use of the data. The nine-pattern favorability map can be evaluated by comparison with the USGS National Assessment for hot spring gold–silver deposits. The Spearman's ranked correlation coefficient between the favorability and the National Assessment permissive tracts is 0.5. Tabulations of the areas of agreement and disagreement between the two maps show 74% agreement for the Great Basin. The posterior probabilities for 51 significant deposits in the Great Basin, both used and not used in the model, show the following: 26 classified as favorable; 25 classified as permissive; and 1, Florida Canyon, classified as nonpermissive.The Florida Canyon deposit has a low favorability because there are no volcanic rocks near the deposit on the Nevada geologic map used. The largest areas of disagreement are caused by the USGS National Assessment team concluding that volcanic rocks older than 27 Ma in Nevada are not permissive, which was not assumed in this model. The weights-of-evidence model is evaluated as reasonable and delineates permissive areas for epithermal deposits comparable to expert's delineation. The weights-of-evidence model has the additional characteristics that it is well defined, reproducible, objective, and provides a quantitative measure of confidence.     

A review of soil and geological maps of the Nepean River terraces,NSW

The Nepean River terraces are a sequence of Tertiary and Quaternary alluvial deposits to the west of Sydney, NSW. In the last 85 years over 20 soil and geological maps covering the area have been produced. Errors and interpretations introduced in early regional scale maps were perpetuated for over 40 years despite numerous new editions. Regional scale geological maps in the 1950s and 1960s failed to take account of good detailed work by geographers, soil surveyors and geology students. Detailed geological and soil stratigraphic mapping in the past 10 years drew attention to these problems and has highlighted the important contributions made by the earlier detailed studies. New interpretations can now be tested against a substantial body of data. Contemporary regional soil and geological maps have taken account of these detailed studies and so present a more consistent approach than in the past.     

Burial,Maturation, and Petroleum Generation History of the Arkoma Basin and Ouachita Foldbelt,Oklahoma and Arkansas

Removed overburden, burial, maturation, and petroleum generation analysis indicates that maturity in the Arkoma Basin and the Ouachita Foldbelt is explained effectively using simple burial models that account for the significant surface erosion that has occurred and assuming geothermal gradients similar to present-day gradients have been approximately constant through geologic time. Regional models, based on analysis at 115 well locations, indicate that from 5,000 to 15,000 ft (1.5–4.5 km) of section, differing with location from north to south and west to east, has been removed from the Arkoma Basin region, and as much as 25,000–40,000 ft (7.5–12 km) have been removed from areas of the Ouachita Foldbelt. Based on burial and thermal history reconstruction, increasing maturation from west to east across the basin is primarily the result of increasing overburden and subsequent surface erosion from west to east. The models predict most publicly available vitrinite reflectance data within a factor of 1.5 at two standard deviations. Comparison of model and measured reflectance-depth trends in six wells indicates that hydrothermal fluid movement should not have modified reflectance by more than approximately 20% in the center of the basin. Analysis indicates that most of the basin is overmature for oil production from intervals below the Spiro Sandstone, except to the north and northwest. Although thermal maturities are high, methane is stable throughout the basin. Except for the basal Arbuckle Group, all formations were thermally immature for oil generation prior to burial by the Mississippian and Morrowan in the Ouachita Foldbelt of Oklahoma and by the Atokan and Desmoinesian over most of the basin and study area. In the deeper part of the present basin, all strata entered and passed through the oil window during or within 10 My after Atokan time. Because no additional major quantities of hydrocarbons were generated after Atokan time, the hydrocarbons must have been emplaced and trapped during this brief time interval.     

Quantitative Prediction and Evaluation of Mineral Resources Based on GIS: A Case Study in Sanjiang Region,Southwestern China

Quantitative prediction and evaluation of mineral resources are one of the important topics of mathematical geology. On the basis of GIS technologies and weights of evidence modeling, MapGIS is integrated with GIS and mineral-resource prediction and evaluation. The final product is a predictor map of posterior probabilities of occurrence of the discrete event within a small unit cell. Predictor layers were created on a digital database that includes 1:200,000 scale geological, and geochemical, and geophysical maps, and remote-sensing images in study area. According to metallogenetic factors extractiont and weights of evidence modeling, there are four main metal ore belts in the study area: (1) the Batang belt; (2) the Lei Wuqi belt; (3) the Basu-Chayu belt; and (4) the Ganzi-Litang belt. The predictor map of posterior probabilities show that 29% of study area as zones with potential for porphyry copper, and 81% known mineral occurrences success rate is circled in the metallogenetic posterior probabilities map. The results demonstrate plausibility of weights-of-evidence modeling of mineral potential in large areas with small number of mineral prospects.     

Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping

In this paper, we describe new fuzzy models for predictive mineral potential mapping: (1) a knowledge-driven fuzzy model that uses a logistic membership function for deriving fuzzy membership values of input evidential maps and (2) a data-driven model, which uses a piecewise linear function based on quantified spatial associations between a set of evidential evidence features and a set of known mineral deposits for deriving fuzzy membership values of input evidential maps. We also describe a graphical defuzzification procedure for the interpretation of output fuzzy favorability maps. The models are demonstrated for mapping base metal deposit potential in an area in the south-central part of the Aravalli metallogenic province in the state of Rajasthan, western India. The data-driven and knowledge-driven models described in this paper predict potentially mineralized zones, which occupy less than 10% of the study area and contain at least 83% of the model and validation base metal deposits. A cross-validation of the favorability map derived from using one of the models with the favorability map derived from using the other model indicates a remarkable similarity in their results. Both models therefore are useful for predicting favorable zones to guide further exploration work.     

A Petroleum System for the Salina Basin in Kansas Based on Organic Geochemistry and Geologic Analog

The Salina Basin historically has been an exploration desert—a home of dryholes. Although this basin, which underlies much of north-central Kansas, may never be a prolific source of hydrocarbons, recent research into the maturation and geochemistry of organic matter and oils in Kansas can provide guidelines for a new exploration strategy. The Salina Basin is similar to the oil-productive Forest City Basin in northeastern Kansas in many ways. Both basins originated as a single large basin (i.e., the North Kansas Basin) prior to the rise of the Nemaha Uplift in Late Mississippian-Early Pennsylvanian time. Their Paleozoic stratigraphy thus is similar and the axes of both basins are presently at approximately the same depth. Thermal maturation modeling and available organic-matter maturation data indicate that the lower Paleozoic rocks in the axes of both basins are in the early stages of oil generation. In the Forest City Basin the Ordovician Simpson Group is the deepest known hydrocarbon source-rock—oil-reservoir interval, and by analogy, exploration tests in the Salina Basin, at a minimum, should penetrate through this stratigraphic interval. Ordovician Simpson Group shales in the Forest City Basin are the source rocks for a geochemically distinct oil, which also occurs in Ordovician reservoirs in the extreme southern end of the Salina Basin. To increase the odds of success in an exploration program in the Salina Basin, wildcat wells should be drilled where thermal maturation is greatest. The broad NW–SE-trending basin axis is the most logical area. Exploration tests along this axis in the northern end of the basin may have an extra advantage as organic matter in the Simpson Group may be more thermally mature because of greater burial depth during the Cretaceous. Along the eastern margin of the nearby Central Kansas Uplift and Pratt Anticline, several Paleozoic geologic structures, some of which contain major oil fields, are attributable to tectonic reactivation along the western margin of the Precambrian Central North American Rift System (CNARS). Prospective structural trends in the Paleozoic section of the Salina Basin are anticipated to be associated with this underlying tectonic boundary. The western margin of the CNARS trends NNE–SSW where it passes under the axis of the Salina Basin in northeastern Lincoln and southeastern Mitchell counties. This area is sparsely drilled, with less than two tests per township. If an exploration program can define lower Paleozoic structural closures in this region, these structures may represent the best chance for future petroleum discoveries.     

Developing a geographic expert system for regional mapping of volcanogenic massive sulfide (VMS) deposit potential

A personal computer-based geographic information system (GIS) is used to develop a geographic expert system (GES) for mapping and evaluating volcanogenic massive sulfide (VMS) deposit potential. The GES consists of an inference network to represent expert knowledge, and a GIS to handle the spatial analysis and mapping. Evidence from input maps is propagated through the inference network, combining information by means of fuzzy logic and Bayesian updating to yield new maps showing evaluation of hypotheses. Maps of evidence and hypotheses are defined on a probability scale between 0 and 1. Evaluation of the final hypothesis results in a mineral potential map, and the various intermediate hypotheses can also be shown in map form.The inference net, with associated parameters for weighting evidence, is based on a VMS deposit model for the Chisel Lake deposit, a producing mine in the Early Protoerzoic Snow Lake greenstone belt of northwest Manitoba. The model is applied to a small area mapped at a scale of 1:15,840. The geological map, showing lithological and alteration units, provides the basic input to the model. Spatial proximity to contacts of various kinds are particularly important. Three types of evidence are considered: stratigraphic, heat source, and alteration. The final product is a map showing the relative favorability for VMS deposits. The model is implemented as aFortran program, interfaced with the GIS. The sensitivity of the model to changes in the parameters is evaluated by comparing predicted areas of elevated potential with the spatial distribution of known VMS occurrences.     

Porphyry Copper Deposit Tract Definition – A Global Analysis Comparing Geologic Map Scales

Geologic maps are a fundamental data source used to define mineral-resource potential tracts for the first step of a mineral resource assessment. Further, it is generally believed that the scale of the geologic map is a critical consideration. Previously published research has demonstrated that the U.S. Geological Survey porphyry tracts identified for the United States, which are based on 1:500,000-scale geology and larger scale data and published at 1:1,000,000 scale, can be approximated using a more generalized 1:2,500,000-scale geologic map. Comparison of the USGS porphyry tracts for the United States with weights-of-evidence models made using a 1:10,000,000-scale geologic map, which was made for petroleum applications, and a 1:35,000,000-scale geologic map, which was created as context for the distribution of porphyry deposits, demonstrates that, again, the USGS US porphyry tracts identified are similar to tracts defined on features from these small scale maps. In fact, the results using the 1:35,000,000-scale map show a slightly higher correlation with the USGS US tract definition, probably because the conceptual context for this small-scale map is more appropriate for porphyry tract definition than either of the other maps. This finding demonstrates that geologic maps are conceptual maps. The map information shown in each map is selected and generalized for the map to display the concepts deemed important for the map maker’s purpose. Some geologic maps of small scale prove to be useful for regional mineral-resource tract definition, despite the decrease in spatial accuracy with decreasing scale. The utility of a particular geologic map for a particular application is critically dependent on the alignment of the intention of the map maker with the application.     

Quantitative estimates of the geology of large regions and their application to mineral-resource assessment

The unit regional value of the mineral resources of a large region may be estimated by accumulating past production records and prorating them over the area of the region. The geological characteristics of a large region is a prime conditioning variable for this purpose. To be useful, however, the geology of a large region must be represented in a standardized form. The “geology,” as here measured, refers to a standardized set of rock types common to the legends in geological maps. By using standardized procedures, the legends of 413 geologic maps at 292 different scales that cover the Earth’s land surface were transformed into a set of 65 three-digit numbers. The set of numbers called the time-petrographic index is associated with the contemporaneous tectonic environments that led to the formation of the rocks and their associated mineral deposits. Application of the time-petrographic index to geologic maps leads to more precise estimates of the mineral-resource values of a large region. Deceased, June 2, 1992     

认知地图的空间句法研究

将意识层面的空间意象外化为可被度量及分析的对象,一直是行为学、地理学、旅游学等学科关注的重要课题。本文主要从构建空间意象的结构而非意义入手,对该领域研究中的两种重要方法,即认知地图法和空间句法进行了比较,意图找到两种方法在空间结构性认知方面的相关性。基于句法空间结构的表达模型,分别就长沙太平街历史街区集成度及其认知地图的调研结果进行了比对,通过真实地图句法集成度与579 份认知地图意象要素出现频率等指标的比较,发现集成度指标与意象出现频率等指标存在较好的相关性。在对认知地图样本进行轴线化转换并比照真实地图的轴线索引后,对真实地图句法集成度与579 份认知地图平均句法集成度进行比较,发现认知地图与真实地图的句法分析在结果上仍有较好的相关性,且节点、标志物两要素的出现频次等指标也与所在街道的集成度存在较为明显的对应性。结论说明空间构形对人们结构性意象的形成有着重要的影响力,认知地图的句法分析对揭示人们空间知识的形成机制具有一定的参考价值,两种方法有相互补充的可能性。     

Geologically Constrained Fuzzy Mapping of Gold Mineralization Potential,Baguio District,Philippines

An application of the theory of fuzzy sets to the mapping of gold mineralization potential in the Baguio gold mining district of the Philippines is described. Proximity to geological features is translated into fuzzy membership functions based upon qualitative and quantitative knowledge of spatial associations between known gold occurrences and geological features in the area. Fuzzy sets of favorable distances to geological features and favorable lithologic formations are combined using fuzzy logic as the inference engine. The data capture, map operations, and spatial data analyses are carried out using a geographic information system. The fuzzy predictive maps delineate at least 68% of the known gold occurrences that are used to generate the model. The fuzzy predictive maps delineate at least 76% of the unknown gold occurrences that are not used to generate the model. The results are highly comparable with the results of previous stream-sediment geochemical survey in the area. The results demonstrate the usefulness of a geologically constrained fuzzy set approach to map mineral potential and to redirect surficial exploration work in the search for yet undiscovered gold mineralization in the mining district. The method described is applicable to other mining districts elsewhere.