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Results for “Annals of Operations Research”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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3 recordsLinked to original sources
USGS1987

Probability theory versus simulation of petroleum potential in play analysis

An analytic probabilistic methodology for resource appraisal of undiscovered oil and gas resources in play analysis is presented. This play-analysis methodology is a geostochastic system for petroleum resource appraisal in explored as well as frontier areas. An objective was to replace an existing Monte Carlo simulation method in order to increase the efficiency of the appraisal process. Underlying the two methods is a single geologic model which considers both the uncertainty of the presence of the assessed hydrocarbon and its amount if present. The results of the model are resource estimates of crude oil, nonassociated gas, dissolved gas, and gas for a geologic play in terms of probability distributions. The analytic method is based upon conditional probability theory and a closed form solution of all means and standard deviations, along with the probabilities of occurrence. ?? 1987 J.C. Baltzer A.G., Scientific Publishing Company.

Annals of Operations Research↗
USGS2012

USGS "Did You Feel It?" internet-based macroseismic intensity maps

The U.S. Geological Survey (USGS) "Did You Feel It?" (DYFI) system is an automated approach for rapidly collecting macroseismic intensity data from Internet users' shaking and damage reports and generating intensity maps immediately following earthquakes; it has been operating for over a decade (1999-2011). DYFI-based intensity maps made rapidly available through the DYFI system fundamentally depart from more traditional maps made available in the past. The maps are made more quickly, provide more complete coverage and higher resolution, provide for citizen input and interaction, and allow data collection at rates and quantities never before considered. These aspects of Internet data collection, in turn, allow for data analyses, graphics, and ways to communicate with the public, opportunities not possible with traditional data-collection approaches. Yet web-based contributions also pose considerable challenges, as discussed herein. After a decade of operational experience with the DYFI system and users, we document refinements to the processing and algorithmic procedures since DYFI was first conceived. We also describe a number of automatic post-processing tools, operations, applications, and research directions, all of which utilize the extensive DYFI intensity datasets now gathered in near-real time. DYFI can be found online at the website http://earthquake.usgs.gov/dyfi/. ?? 2011 by the Istituto Nazionale di Geofisica e Vulcanologia.

Annals of Geophysics↗
USGS2025

GIScience in the era of Artificial Intelligence: A research agenda towards Autonomous GIS

The advent of generative AI exemplified by large language models (LLMs) opens new ways to represent and compute geographic information and transcends the process of geographic knowledge production, driving geographic information systems (GIS) towards autonomous GIS. Leveraging LLMs as the decision core, autonomous GIS can independently generate and execute geoprocessing workflows to perform spatial analysis. In this vision paper, we further elaborate on the concept of autonomous GIS and present a conceptual framework that defines its five autonomous goals, five levels of autonomy, five core functions, and three operational scales. We demonstrate how autonomous GIS could perform geospatial data retrieval, spatial analysis, and map making with four proof-of-concept GIS agents. We conclude by identifying critical challenges and future research directions, including fine-tuning and self-growing decision-cores, autonomous modelling, and examining the societal and practical implications of autonomous GIS. By establishing the groundwork for a paradigm shift in GIScience, this paper envisions a future where GIS moves beyond traditional workflows to autonomously reason, derive, innovate, and advance geospatial solutions to pressing global challenges. Meanwhile, we emphasize that as we design and deploy increasingly intelligent geospatial systems, we carry a responsibility to ensure they are developed in socially responsible ways, serve the public good, and support the continued value of human geographic insight in an AI-augmented future.

Annals of GIS↗