A processor-based system for generating a probability map may include at least one processor. The at least one processor may be configured to receive data associated with a quantity to be mapped and apply a processor-based mapping algorithm to generate a first map of values for the quantity within an area of interest. The processor may also be configured to modify at least one or more of data provided as input to the mapping algorithm or one or more input parameters associated with the mapping algorithm and generate at least a second map of values for the quantity within the area of interest; and generate a probability map associated with the quantity based on the first and second maps.
Please help me write a proper abstract based on the patent claims. CLAIM: 1.-32. (canceled) 33. A computer readable storage medium having computer readable program code embodied in the medium for use by a processor-based system in generating a probability map, the computer readable storage medium comprising: program code configured to: receive a geo-spatially discrete input data set associated with a quantity to be mapped; generate a statistically significant number of maps of values for the quantity within an area of interest based on the geo-spatially discrete input data set, wherein generation of the statistically significant number of maps includes applying a processor-based mapping algorithm to generate an initial map of values for the quantity within an area of interest based on the received geo-spatially discrete input data set, using a random number generator to iteratively modify the geo-spatially discrete input data set, and for each iterative modification of the geo-spatially discrete input data set, applying the mapping algorithm to the modified geo-spatially discrete input data set in order to generate an additional map of values for the quantity within the area of interest; and generate a probability map associated with the quantity based on the statistically significant number of maps by operating on the statistically significant number of maps. 34. The computer readable storage medium of claim 33, wherein the program code is further configured to operate on the statistically significant number of maps by converting the statistically significant number of maps to corresponding binary maps based upon a predetermined threshold value and averaging the binary maps. 35. The computer readable storage medium of claim 33, wherein operating on the statistically significant number of maps includes determining one or more of a median, mean(average), mode, range, variance, deviation, standard deviation, skewness, or kurtosis based on the statistically significant number of maps. 36. The computer readable storage medium of claim 33, wherein the quantity includes at least one of geographic, topographic, bathometric, geologic, geophysical, petrophysical, oceanographic, metrological, atmospheric, hydrologic, pedologic (soil), chemical, environmental, sociologic, economic, biologic, zoological, botanical, epidemiologic, political, ecologic, reservoir engineering or mining data. 37. The computer readable storage medium of claim 33, wherein the processor-based algorithm includes a local interpolation algorithm. 38. The computer readable storage medium of claim 37, wherein the local interpolation algorithm includes a calculation based on at least one of triangulation, natural neighbors, moving average, inverse distance, kriging, parabolic fitting, or collated co-kriging. 39. The computer readable storage medium of claim 33, wherein the processor-based algorithm includes a global extrapolation algorithm. 40. The computer readable storage medium of claim 39, wherein the global extrapolation algorithm includes a calculation based on at least one of a cubic b-spline function, minimum curvature, full tension, convergent gridding, cos expansion, flex gridding, or gradient projection. 41. A method of generating a probability map, comprising: receiving a geo-spatially discrete input data set associated with a quantity to be mapped; generating a statistically significant number of maps of values for the quantity within an area of interest based on the geo-spatially discrete input data set, wherein generating the statistically significant number of maps includes applying a processor-based mapping algorithm to generate an initial map of values for the quantity within an area of interest based on the received geo-spatially discrete input data set, using a random number generator to iteratively modify the geo-spatially discrete input data set, and for each iterative modification of the geo-spatially discrete input data set, applying the mapping algorithm to the modified geo-spatially discrete input data set in order to generate an additional map of values for the quantity within the area of interest; and generating a probability map associated with the quantity based on the statistically significant number of maps by operating on the statistically significant number of maps. 42. The method of claim 41, wherein operating on the statistically significant number of maps includes: converting the statistically significant number of maps to corresponding binary maps based upon a predetermined threshold value; and averaging the binary maps. 43. The method of claim 41, wherein operating on the statistically significant number of maps includes determining one or more of a median, mean (average), mode, range, variance, deviation, standard deviation, skewness, or kurtosis based on the statistically significant number of maps. 44. The method of claim 41, wherein the quantity includes at least one of geographic, topographic, bathometric, geologic, geophysical, petrophysical, oceanographic, metrological, atmospheric, hydrologic, pedologic (soil), chemical, environmental, sociologic, economic, biologic, zoological, botanical, epidemiologic, political, ecologic, reservoir engineering or mining data. 45. The method of claim 41, wherein the processor-based algorithm includes a local interpolation algorithm. 46. The method of claim 45, wherein the local interpolation algorithm includes a calculation based on at least one of triangulation, natural neighbors, moving average, inverse distance, kriging, parabolic fitting, or collated co-kriging. 47. The method of claim 41, wherein the processor-based algorithm includes a global extrapolation algorithm. 48. The method of claim 47, wherein the global extrapolation algorithm includes a calculation based on at least one of a cubic b-spline function, minimum curvature, full tension, convergent gridding, cos expansion, flex gridding, or gradient projection.