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WO2011159255A3 - High-dimensional data analysis - Google Patents

High-dimensional data analysis Download PDF

Info

Publication number
WO2011159255A3
WO2011159255A3 PCT/SG2011/000207 SG2011000207W WO2011159255A3 WO 2011159255 A3 WO2011159255 A3 WO 2011159255A3 SG 2011000207 W SG2011000207 W SG 2011000207W WO 2011159255 A3 WO2011159255 A3 WO 2011159255A3
Authority
WO
WIPO (PCT)
Prior art keywords
parameter set
model parameter
reduced base
observed data
input
Prior art date
Application number
PCT/SG2011/000207
Other languages
French (fr)
Other versions
WO2011159255A2 (en
Inventor
Juan Luis FERNÁNDEZ MARTÍNEZ
Original Assignee
Blue Prism Technologies Pte Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US13/041,423 external-priority patent/US8688616B2/en
Application filed by Blue Prism Technologies Pte Ltd filed Critical Blue Prism Technologies Pte Ltd
Publication of WO2011159255A2 publication Critical patent/WO2011159255A2/en
Publication of WO2011159255A3 publication Critical patent/WO2011159255A3/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V20/00—Geomodelling in general
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/32—Transforming one recording into another or one representation into another
    • G01V1/325—Transforming one representation into another
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/40—Transforming data representation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/62—Physical property of subsurface

Landscapes

  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Computational Mathematics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Probability & Statistics with Applications (AREA)
  • General Engineering & Computer Science (AREA)
  • Evolutionary Biology (AREA)
  • Algebra (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • Operations Research (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Geophysics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Aiming, Guidance, Guns With A Light Source, Armor, Camouflage, And Targets (AREA)
  • Compression, Expansion, Code Conversion, And Decoders (AREA)

Abstract

Described herein is a framework for analyzing data in high-dimensional space. In accordance with one implementation, observed data and at least one input model parameter set is received. The input model parameter set serves as a solution candidate of a predefined problem (e.g., inverse or optimization problem) and is related to the observed data via a model. To provide enhanced computational efficiency, a reduced base with lower dimensionality is determined based on the input model parameter set. The reduced base is associated with a set of coefficients, which represents the coordinates of any model parameter set in the reduced base. Sampling is performed within the reduced base to generate an output model parameter set in the reduced base. The output model parameter set is compatible with the input model parameter set and fits the observed data, via the model, within a predetermined threshold.
PCT/SG2011/000207 2010-06-14 2011-06-09 High-dimensional data analysis WO2011159255A2 (en)

Applications Claiming Priority (6)

Application Number Priority Date Filing Date Title
US35468510P 2010-06-14 2010-06-14
US61/354,685 2010-06-14
US13/041,423 2011-03-06
US13/041,423 US8688616B2 (en) 2010-06-14 2011-03-06 High-dimensional data analysis
US201161494403P 2011-06-07 2011-06-07
US61/494,403 2011-06-07

Publications (2)

Publication Number Publication Date
WO2011159255A2 WO2011159255A2 (en) 2011-12-22
WO2011159255A3 true WO2011159255A3 (en) 2012-11-29

Family

ID=45348778

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/SG2011/000207 WO2011159255A2 (en) 2010-06-14 2011-06-09 High-dimensional data analysis

Country Status (1)

Country Link
WO (1) WO2011159255A2 (en)

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3004946A2 (en) 2013-06-03 2016-04-13 Exxonmobil Upstream Research Company Uncertainty estimation of subsurface resistivity solutions
US12079700B2 (en) 2016-10-26 2024-09-03 Google Llc Structured orthogonal random features for kernel-based machine learning
CN107728629B (en) * 2017-09-19 2021-06-29 富平县韦加无人机科技有限公司 Unmanned aerial vehicle magnetic anomaly detection system and method
CN110990757B (en) * 2019-12-05 2023-09-19 杭州电子科技大学 Method for solving highly nonlinear electromagnetic backscatter problem by using non-phase data
CN112559308B (en) * 2020-12-11 2023-02-28 广东电力通信科技有限公司 Statistical model-based root alarm analysis method
WO2024192162A1 (en) * 2023-03-14 2024-09-19 Bp Corporation North America Inc. Systems and methods for efficiently performing stochastic inversion methods in seismic exploration applications
CN118520404B (en) * 2024-07-22 2024-10-01 深圳市拜特科技股份有限公司 Enterprise business data mining method, device, equipment and storage medium

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011115921A2 (en) * 2010-03-19 2011-09-22 Schlumberger Canada Limited Uncertainty estimation for large-scale nonlinear inverse problems using geometric sampling and covariance-free model compression

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4142311A (en) 1976-09-13 1979-03-06 Lane Ronald S Permanent calendar

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011115921A2 (en) * 2010-03-19 2011-09-22 Schlumberger Canada Limited Uncertainty estimation for large-scale nonlinear inverse problems using geometric sampling and covariance-free model compression

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
ESPERANZA GARCÍA-GONZALO AND JUAN LUIS FERNÁNDEZ-MARTÍNEZ: "Particle Swarm Optimization and Inverse Problems", ADVANCES IN INTELLIGENT AND SOFT COMPUTING : AISC; COMBINING SOFT COMPUTING AND STATISTICAL METHODS IN DATA ANALYSIS - SMPS, INTERNATIONAL CONFERENCE ON SOFT METHODS IN PROBABILITY AND STATISTICS ; (OVIEDO) : 2010, SPRINGER, GERMANY, vol. 77, October 2010 (2010-10-01), pages 289 - 296, XP008156145, ISSN: 1867-5662, [retrieved on 20101012], DOI: 10.1007/978-3-642-14746-3_36 *
JUAN L FERNÃ NDEZ-MARTÃNEZ ET AL: "Particle Swarm Optimization in High Dimensional Spaces", 8 September 2010, SWARM INTELLIGENCE, SPRINGER BERLIN HEIDELBERG, BERLIN, HEIDELBERG, PAGE(S) 496 - 503, ISBN: 978-3-642-15460-7, XP019150200 *
JUAN L. FERNÁNDEZ MARTÍNEZ ET AL: "PSO: A powerful algorithm to solve geophysical inverse problems", JOURNAL OF APPLIED GEOPHYSICS, vol. 71, no. 1, 18 February 2010 (2010-02-18), pages 13 - 25, XP055037646, ISSN: 0926-9851, DOI: 10.1016/j.jappgeo.2010.02.001 *
JUAN LUIS FERNÁNDEZ-MARTÍNEZ ET AL: "Geometric Sampling: An Approach to Uncertainty in High Dimensional Spaces", ADVANCES IN INTELLIGENT AND SOFT COMPUTING : AISC; COMBINING SOFT COMPUTING AND STATISTICAL METHODS IN DATA ANALYSIS - SMPS, INTERNATIONAL CONFERENCE ON SOFT METHODS IN PROBABILITY AND STATISTICS ; (OVIEDO) : 2010, SPRINGER, GERMANY, vol. 77, October 2010 (2010-10-01), pages 247 - 254, XP008156143, ISSN: 1867-5662, [retrieved on 20101012], DOI: 10.1007/978-3-642-14746-3_31 *
JUAN LUIS FERNÁNDEZ-MARTÍNEZ ET AL: "Inverse Problems and Model Reduction Techniques", ADVANCES IN INTELLIGENT AND SOFT COMPUTING : AISC; COMBINING SOFT COMPUTING AND STATISTICAL METHODS IN DATA ANALYSIS - SMPS, INTERNATIONAL CONFERENCE ON SOFT METHODS IN PROBABILITY AND STATISTICS ; (OVIEDO) : 2010, SPRINGER, GERMANY, vol. 77, October 2010 (2010-10-01), pages 255 - 262, XP008156146, ISSN: 1867-5662, [retrieved on 20101012], DOI: 10.1007/978-3-642-14746-3_32 *

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