首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 538 毫秒
1.
  相似文献   

2.
Among the more popular spatial modeling techniques, artificial neural networks (ANN) are tools that can deal with non-linear relationships, can classify unknown data into categories by using known examples for training, and can deal with uncertainty; characteristics that provide new possibilities for data exploration. Radial basis functional link nets (RBFLN), a form of ANN, are applied to generate a series of prospectivity maps for orogenic gold deposits within the Paleoproterozoic Central Lapland Greenstone Belt, Northern Fennoscandian Shield, Finland, which is considered highly prospective yet clearly under explored. The supervised RBFLN performs better than previously applied statistical weights-of-evidence or conceptual fuzzy logic methods, and equal to logistic regression method, when applied to the same geophysical and geochemical data layers that are proxies for conceptual geological controls. By weighting the training feature vectors in terms of the size of the gold deposits, the classification of the neural network results provides an improved prediction of the distribution of the more important deposits/occurrences. Thus, ANN, more specifically RBFLN, potentially provide a better tool to other methodologies in the development of prospectivity maps for mineral deposits, hence aiding conceptual exploration.  相似文献   

3.
The spatial distribution of discovered resources may not fully mimic the distribution of all such resources, discovered and undiscovered, because the process of discovery is biased by accessibility factors (e.g., outcrops, roads, and lakes) and by exploration criteria. In data-driven predictive models, the use of training sites (resource occurrences) biased by exploration criteria and accessibility does not necessarily translate to a biased predictive map. However, problems occur when evidence layers correlate with these same exploration factors. These biases then can produce a data-driven model that predicts known occurrences well, but poorly predicts undiscovered resources. Statistical assessment of correlation between evidence layers and map-based exploration factors is difficult because it is difficult to quantify the “degree of exploration.” However, if such a degree-of-exploration map can be produced, the benefits can be enormous. Not only does it become possible to assess this correlation, but it becomes possible to predict undiscovered, instead of discovered, resources. Using geothermal systems in Nevada, USA, as an example, a degree-of-exploration model is created, which then is resolved into purely explored and unexplored equivalents, each occurring within coextensive study areas. A weights-of-evidence (WofE) model is built first without regard to the degree of exploration, and then a revised WofE model is calculated for the “explored fraction” only. Differences in the weights between the two models provide a correlation measure between the evidence and the degree of exploration. The data used to build the geothermal evidence layers are perceived to be independent of degree of exploration. Nevertheless, the evidence layers correlate with exploration because exploration has preferred the same favorable areas identified by the evidence patterns. In this circumstance, however, the weights for the “explored” WofE model minimize this bias. Using these revised weights, posterior probability is extrapolated into unexplored areas to estimate undiscovered deposits.  相似文献   

4.
Mineral exploration activities require robust predictive models that result in accurate mapping of the probability that mineral deposits can be found at a certain location. Random forest (RF) is a powerful machine data-driven predictive method that is unknown in mineral potential mapping. In this paper, performance of RF regression for the likelihood of gold deposits in the Rodalquilar mining district is explored. The RF model was developed using a comprehensive exploration GIS database composed of: gravimetric and magnetic survey, a lithogeochemical survey of 59 elements, lithology and fracture maps, a Landsat 5 Thematic Mapper image and gold occurrence locations. The results of this study indicate that the use of RF for the integration of large multisource data sets used in mineral exploration and for prediction of mineral deposit occurrences offers several advantages over existing methods. Key advantages of RF include: (1) the simplicity of parameter setting; (2) an internal unbiased estimate of the prediction error; (3) the ability to handle complex data of different statistical distributions, responding to nonlinear relationships between variables; (4) the capability to use categorical predictors; and (5) the capability to determine variable importance. Additionally, variables that RF identified as most important coincide with well-known geologic expectations. To validate and assess the effectiveness of the RF method, gold prospectivity maps are also prepared using the logistic regression (LR) method. Statistical measures of map quality indicate that the RF method performs better than LR, with mean square errors equal to 0.12 and 0.19, respectively. The efficiency of RF is also better, achieving an optimum success rate when half of the area predicted by LR is considered.  相似文献   

5.
Geoscientific Information Systems (GIS) provide tools to quantitatively analyze and integrate spatially referenced information from geological, geophysical, and geochemical surveys for decision-making processes. Excellent coverage of well-documented, precise and good quality data enables testing of variable exploration models in an efficient and cost effective way with GIS tools. Digital geoscientific data from the Geological Survey of Finland (GTK) are being used widely as spatial evidence in exploration targeting, that is ranking areas based on their exploration importance. In the last few years, spatial analysis techniques including weights-of-evidence, logistic regression, and fuzzy logic, have been increasingly used in GTK’s mineral exploration and geological mapping projects. Special emphasis has been put into the exploration for gold because of the excellent data coverage within the prospective volcanic belts and because of the increased activity in gold exploration in Finland during recent years. In this paper, we describe some successful case histories of using the weights-of-evidence method for the Au-potential mapping. These projects have shown that, by using spatial modeling techniques, exploration targets can be generated by quantitatively analyzing extensive amounts of data from various sources and to rank these target areas based on their exploration potential.  相似文献   

6.
Liu  Lushi  Lu  Jilong  Tao  Chunhui  Liao  Shili  Chen  Shengbo 《Natural Resources Research》2021,30(2):971-987

With the depletion of mineral resources on land, seafloor massive sulfide deposits have the potential to become as important for exploration, development and mining as those on land. However, it is difficult to investigate the ocean environment where seafloor massive sulfide deposits are located. Thus, improving prospecting efficiency by reducing the exploration search space through mineral prospectivity mapping (MPM) is desirable. MPM has been used in the exploration for seafloor deposits on regional scales, e.g., the Mid-Atlantic Ridge and Arctic Ridge. However, studies of MPM on ultraslow-spreading ridges on segment scales to aid exploration for seafloor massive sulfide have not been carried out to date. Here, data of water depth, geology and hydrothermal plume anomalies were analyzed and the weights-of-evidence method was used to study the metallogenic regularity and to predict the potential area for seafloor massive sulfide exploration in 48.7°–50.5° E segments on the ultraslow spreading Southwest Indian Ridge. Based on spatial analysis, 11 predictive maps were selected to establish a mineral potential model. Weight values indicate that the location of seafloor massive sulfide deposits is correlated mainly with mode-E faults and oceanic crust thickness in the study area, which correspond with documented ore-controlling factors on other studied ultraslow-spreading ridges. In addition, the detachment fault and ridge axis, which reflect the deep hydrothermal circulation channel and magmatic activities, also play an important role. Based on the posterior probability values, 3 level A, 2 level B and 2 level C areas were identified as targets for further study. The MPM results were helpful for narrowing the search space and have implications for investigating and evaluating seafloor massive sulfide resources in the study area and on other ultraslow-spreading ridges.

  相似文献   

7.
The Gurupi Belt hosts a Paleoproterozoic gold province located in north–northeastern Brazil, at the borders of Pará and Maranhão states. It is considered to be an extension of the prolific West African Craton’s Birimian gold province into South America. Additionally, the belt has been the object of recent mineral exploration programs with significant resource discoveries. This study presents the results of predictive mapping using up-to-date mineral system concepts and recently finished regional-scale geological mapping, stream sediment and airborne geophysical surveys conducted by the Geological Survey of Brazil. We relate gold mineralization to an initially enriched crust, metamorphism, deep fluid pathways, structurally controlled damage zones and hydrothermal alteration. Prospective targets were generated using only regional public datasets and knowledge-driven targeting technique. This work did not incorporate any known gold deposits, yet it predicted the largest known deposits and their satellite targets. Besides, high prospective targets mapped almost 40% of known primary gold occurrences within 7% of the project area. This work allowed considerable search area reduction and identification of new target areas, thus collaborating on reducing costs, time and risk of mineral exploration. Results indicate that we achieved an efficient understanding of the geological processes related to the Gurupi Belt mineral system.  相似文献   

8.
The Southern Uplands-Down-Longford Terrane in southeast Northern Ireland is prospective for Caledonian-age, turbidite-hosted orogenic gold mineralisation with important deposits at Clontibret in the Republic of Ireland and in Scotland. Geochemical and geophysical data from the DETI-funded Tellus project have been used, in conjunction with other spatial geoscience datasets, to map the distribution of prospectivity for this style of mineralisation over this terrane. A knowledge-based fuzzy logic modelling methodology using Arc Spatial Data modeller was utilised. The prospectivity analysis has identified several areas prospective for turbidite-hosted gold mineralisation, comparable to that at Clontibret and gold occurrences in the Southern Uplands of Scotland. A number of these either coincide with known bedrock gold occurrences or with areas considered prospective and targeted by previous exploration work, validating the predictive capability of the exploration model devised and its translation into a GIS-based prospectivity model. The results of the modelling suggest that as in other parts of the Southern Uplands the coincidence of regional strike-parallel structures and intersecting transverse faults are highly prospective, as these are likely to create zones of anomalous stress for fluid flow and deposit formation. Those areas in which there are no known gold occurrences are considered to be favourable targets for further exploration and should be followed up.  相似文献   

9.
Mineral potential within the Greater Nahanni Ecosystem (GNE) was modelled in a Geographic Information System (GIS) for four different deposit types: (1) SEDEX (stratiform shale-hosted sedimentary exhalative Zn–Pb–Ag), (2) ‘Carbonate-Fault’ (carbonate-hosted zinc–lead–silver associated with major faults), (3) ‘Intrusion-Related’ (includes skarn, rare metals and gemstones) and (4) Carlin-Type gold as lode and/or derived placer deposits. This mineral potential modelling study integrates data collected during the Nahanni Mineral and Energy Resource Assessment (MERA) undertaken from 2003 to 2007. The results have contributed to the process of determining the geographic boundaries of the proposed expansion of the Nahanni National Park Reserve. Four mineral potential maps were produced (one for each deposit type) using a knowledge-driven approach. A weighting scheme based on integrated mineral deposit and regional geological knowledge was derived for the various evidence maps for each deposit model using expert opinion. The four potential maps were then combined into a final potential map using a maximum operator. Plots showing the efficiency of the models (mineral potential maps) for predicting the known occurrences of the four deposit types show that partial data sets provide reasonable predictions of the remaining known mineral prospects, occurrences and deposits. Hydrocarbon potential from Nahanni MERA 1 was added to the final potential map to ensure that both mineral and energy potential data were incorporated into the park configuration modelling.  相似文献   

10.
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.  相似文献   

11.
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.  相似文献   

12.
The Random Forests (RF) algorithm is a machine learning method that has recently been demonstrated as a viable technique for data-driven predictive modeling of mineral prospectivity, and thus, it is instructive to further examine its usefulness in this particular field. A case study was carried out using data from Catanduanes Island (Philippines) to investigate further (a) if RF modeling can be used for data-driven modeling of mineral prospectivity in areas with few (i.e., <20) mineral occurrences and (b) if RF modeling can handle predictor variables with missing values. We found that RF modeling outperforms evidential belief (EB) modeling of prospectivity for hydrothermal Au–Cu deposits in Catanduanes Island, where 17 hydrothermal Au–Cu prospects are known to exist. Moreover, just like EB modeling, RF modeling allows analysis of the spatial relationships between known prospects and individual layers of predictor data. Furthermore, RF modeling can handle missing values in predictor data through an RF-based imputation technique whereas in EB modeling, missing values are simply represented by maximum uncertainty. Therefore, the RF algorithm is a potentially useful method for data-driven predictive modeling of mineral prospectivity in regions with few (i.e., <20) occurrences of mineral deposits of the type sought. However, further testing of the method in other regions with few mineral occurrences is warranted to fully determine its usefulness in data-driven predictive modeling of mineral prospectivity.  相似文献   

13.

Structural equation modeling (SEM) was applied here to modify the ordinary weights-of-evidence (WofE) method for calculating posterior probability to improve conditional independence (CI) in the application of this method mineral potential prediction. The new method attempts to reduce the effect of CI by defining new binary patterns with an optimum combination of cutoff values of patterns. The solution is calculated through SEM, and the goodness of fit between evidence and mineral deposit occurrences is evaluated by a specified target function. The main difference between the new WofE and ordinary WofE is that evidence in the new method maintains a balance between the significance for mineral potential prediction and CI, rather than the significance for mineral prediction only as in ordinary WofE. A case study of prediction of potential for hydrothermal Au mineral deposits in Nova Scotia, Canada, is discussed here. The results indicate that the new method performs better than the ordinary WofE.

  相似文献   

14.
This study is concerned with understanding of the formation of ore deposits (precious and base metals) and contributes to the exploration and discovery of new occurrences using artificial neural networks. From the different digital data sets available in BRGM's GIS Andes (a comprehensive metallogenic continental-scale Geographic Information System) 25 attributes are identified as known factors or potential factors controlling the formation of gold deposits in the Andes Cordillera. Various multilayer perceptrons were applied to discriminate possible ore deposits from barren sites. Subsequently, because artificial neural networks can be used to construct a revised model for knowledge extraction, the optimal brain damage algorithm by LeCun was applied to order the 25 attributes by their relevance to the classification. The approach demonstrates how neural networks can be used efficiently in a practical problem of mineral exploration, where general domain knowledge alone is insufficient to satisfactorily model the potential controls on deposit formation using the available information in continent-scale information systems.  相似文献   

15.

The Pb–Zn sulfide concentrations hosted by dolomitized Cambrian carbonates in Southeast Missouri are world-class Mississippi Valley Type (MVT) deposits. These deposits commonly are in sites where local Precambrian basement highs resulted in depositional pinchouts of the basal Cambrian sandstones that served as a regional aquifer for basinal fluid migration driven by late Paleozoic Ouachita deformation. Mineralization also appears to be spatially related to regional faults that probably served as local fluid conduits. Understanding spatial associations between sites of known mineralization and regional geology, geochemistry, and geophysics in Southeast Missouri will be a useful guide in future exploration efforts in this region and for similar geologic settings globally. The weights-of-evidence method is used to evaluate regional geology, geochemistry, and geophysical datasets and produce favorability maps for MVT deposits in Southeast Missouri. Host rock characteristics, regional structural controls, stream sediment geochemistry, and proximity to basement highs appear to be the most useful data for predicting the location of the major deposits. This work illustrates the potential utility of mineral potential modeling to prioritize areas for exploration and identify permissive areas for undiscovered MVT mineralization.

  相似文献   

16.
Large amounts of digital data must be analyzed and integrated to generate mineral potential maps, which can be used for exploration targeting. The quality of the mineral potential maps is dependent on the quality of the data used as inputs, with higher quality inputs producing higher quality outputs. In mineral exploration, particularly in regions with little to no exploration history, datasets are often incomplete at the scale of investigation with data missing due to incomplete mapping or the unavailability of data over certain areas. It is not always clear that datasets are incomplete, and this study examines how mineral potential mapping results may differ in this context. Different methods of mineral potential mapping provide different ways of dealing with analyzing and integrating incomplete data. This study examines the weights of evidence (WofE), evidential belief function and fuzzy logic methods of mineral potential mapping using incomplete data from the Carajás mineral province, Brazil to target for orogenic gold mineralization. Results demonstrate that WofE is the best one able to predict the location of known mineralization within the study area when either complete or unacknowledged incomplete data are used. It is suggested that this is due to the use of Bayes’ rule, which can account for “missing data.” The results indicate the effectiveness of WofE for mineral potential mapping with incomplete data.  相似文献   

17.
Estimates of numbers of undiscovered mineral deposits, fundamental to assessing mineral resources, are affected by map scale. Where consistently defined deposits of a particular type are estimated, spatial and frequency distributions of deposits are linked in that some frequency distributions can be generated by processes randomly in space whereas others are generated by processes suggesting clustering in space. Possible spatial distributions of mineral deposits and their related frequency distributions are affected by map scale and associated inclusions of non-permissive or covered geological settings. More generalized map scales are more likely to cause inclusion of geologic settings that are not really permissive for the deposit type, or that include unreported cover over permissive areas, resulting in the appearance of deposit clustering. Thus, overly generalized map scales can cause deposits to appear clustered. We propose a model that captures the effects of map scale and the related inclusion of non-permissive geologic settings on numbers of deposits estimates, the zero-inflated Poisson distribution. Effects of map scale as represented by the zero-inflated Poisson distribution suggest that the appearance of deposit clustering should diminish as mapping becomes more detailed because the number of inflated zeros would decrease with more detailed maps. Based on observed worldwide relationships between map scale and areas permissive for deposit types, mapping at a scale with twice the detail should cut permissive area size of a porphyry copper tract to 29% and a volcanic-hosted massive sulfide tract to 50% of their original sizes. Thus some direct benefits of mapping an area at a more detailed scale are indicated by significant reductions in areas permissive for deposit types, increased deposit density and, as a consequence, reduced uncertainty in the estimate of number of undiscovered deposits. Exploration enterprises benefit from reduced areas requiring detailed and expensive exploration, and land-use planners benefit from reduced areas of concern.  相似文献   

18.
Cause-Effect Analysis in Assessment of Mineral Resources   总被引：1，自引：0，他引：1  
Cause-effect analyses is a deterministic methodology intended for processing qualitative (e.g. texts, conventional maps) and mixed, qualitative and quantitative, data. The main idea, employed in cause-effect analysis, is the plurality and interaction of causes. This idea is described by mathematical logic formulae that can be converted into a single Boolean equation. The latter represents a mathematical model of a general shape of cause-effect relations for the study problem. In particular, such a model can express relations between some property of the mineralization and features of other geological phenomena. By processing data, logical dependencies satisfying to the theoretical model are determined in a data file. These dependences, expressed by Boolean function formulae, describe cause-effect relations for a case study, and they are used for predicting. Software realizing the cause-effect analysis is an expert system with artificial intelligence capabilities. There are two methods of using the cause-effect analysis in assessment of mineral resources. The first method consists in detecting the regularity in locations of known mineral deposits and occurrences with the following using the regularity formula for generation of predictive maps. The second method is the evaluation of individual mineral occurrences by obtained Boolean formulae expressing cause-effect relations between deposit sizes and geological environment of deposits. Both methods are illustrated by case studies of predicting gold-bearing deposits of Middle Asia in the former USSR.  相似文献   

19.

Bétaré-Oya is one of the gold mining districts in the eastern region of Cameroon. Structural controls on gold mineralization were examined along the Bétaré-Oya Shear Zone, providing further clues on favorable areas for mineral exploration. GIS-based methods combining point pattern (i.e., quadrat count, Fry analysis) and distance distribution analysis were employed here to delineate the spatial patterns of known gold deposits and to evaluate their spatial association with geological structures. Results show that the gold deposits in this area are spatially clustered. At the regional scale, the Fry plot indicates an alignment of deposits, suggesting that gold mineralization is controlled by structures oriented NNE–SSW and NE–SW. At the deposit scale, an alignment is also evident, indicating that the mineralization is also controlled by ENE–WSW-trending structures. The cumulative relative frequency distribution of distances from lineament features to gold occurrence points (DM) and to non-occurrence points (DN) ratio (DM/DN) was used to rank these two major structural trends and their relative importance as mineralization control. The yielded grades show that NE–SW-trending lineaments, akin to P-type structures, play a major role in controlling the gold mineralization in the area compared to other structures. Beyond the goal to foster mineral prospection in the Bétaré-Oya gold district, information yielded in the present study provides relevant criteria for further exploration in the eastern region of Cameroon.

  相似文献   

20.
Amalgamation of a number of continental fragments during the Late Neoproterozoic resulted in a united Gondwana continent. The time period in question, at the end of the Precambrian, spans about 250 million years between ∼800 and 550 Ma. Geological activity focused along orogenic belts in Africa during that time period is generally referred to as “Pan African.” We identify three age-related classes of tectonic terranes within these orogenic belts, differentiated on the basis of the formation-age of their crust: juvenile (e.g. mantle derived at or near the time of the orogenesis, ∼0.5–0.8 Ga), Paleoproterozoic (∼1.8–2.5 Ga), Archean (>2.5 Ga). We combine African mineral deposits data of these terranes on a new Neoproterozoic tectonic map of Africa. The spatial correlation between geological terranes in the belts and mineral occurrences are determined in order to define the metallogenic character of each terrane, which we refer to as their “metallogenic fingerprint.” We use these fingerprints to evaluate the effectiveness of mobilization (“recycling”) of mineral deposits within old crustal fragments during Pan African orogenesis. This analysis involves normalization factors derived from the average metallogenic fingerprints of pristine older crust (e.g. Palaeoproterozoic shields and Archean cratons not affected by Pan African orogenesis) and of juvenile Pan African crust (e.g. the Nubian Shield). We find that mineral deposit patterns are distinctly different in older crust that has been remobilized in the Pan African belts compared to those in juvenile crust of Neoproterozoic age, and that the concentration of deposits in remobilized older crust is in all cases significantly depleted relative to that in their pristine age-equivalents. Lower crustal sections (granulite domains) within the Pan African belts are also strongly depleted in mineral deposits relative to the upper crustal sections of juvenile Neoproterozoic terranes. A depletion factor for all terranes in Pan African orogens is derived with which to evaluate the role of mineral deposit recycling during orogenesis. We conclude that recycling of old mineral deposits in younger orogenic belts contributes, on average, to secular decrease of the total mineral endowment of continental crust. This could be of value when formulating exploration strategies.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号