Syntactic pattern recognition for seismic oil exploration /:
The use of pattern recognition has become more and more important in seismic oil exploration. Interpreting a large volume of seismic data is a challenging problem. Seismic reflection data in the one-shot seismogram and stacked seismogram may contain some structural information from the response of t...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
River Edge, NJ :
World Scientific,
2002.
|
| Schriftenreihe: | Series in machine perception and artificial intelligence ;
v. 46. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | The use of pattern recognition has become more and more important in seismic oil exploration. Interpreting a large volume of seismic data is a challenging problem. Seismic reflection data in the one-shot seismogram and stacked seismogram may contain some structural information from the response of the subsurface. Syntactic/structural pattern recognition techniques can recognize the structural seismic patterns and improve seismic interpretations. The syntactic analysis methods include: (1) the error-correcting finite-state parsing, (2) the modified error-correcting Earley's parsing, (3) the par. |
| Beschreibung: | 1 online resource (xiv, 133 pages) : illustrations |
| Bibliographie: | Includes bibliographical references (pages 123-129) and index. |
| ISBN: | 9789812775740 9812775749 |
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| 100 | 1 | |a Huang, Kou-Yuan. |0 http://id.loc.gov/authorities/names/n2002155318 | |
| 245 | 1 | 0 | |a Syntactic pattern recognition for seismic oil exploration / |c Kou-Yuan Huang. |
| 260 | |a River Edge, NJ : |b World Scientific, |c 2002. | ||
| 300 | |a 1 online resource (xiv, 133 pages) : |b illustrations | ||
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| 490 | 1 | |a Series in machine perception and artificial intelligence ; |v v. 46 | |
| 504 | |a Includes bibliographical references (pages 123-129) and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a AUTHOR'S BIOGRAPHY; PREFACE; CONTENTS; 1 INTRODUCTION TO SYNTACTIC PATTERN RECOGNITION; 1.1. SUMMARY; 1.2. INTRODUCTION; 1.3. ORGANIZATION OF THIS BOOK; 2 INTRODUCTION TO FORMAL LANGUAGES AND AUTOMATA; 2.1. SUMMARY; 2.2. LANGUAGES AND GRAMMARS; 2.3. FINITE-STATE AUTOMATON; 2.4. EARLEY'S PARSING; 2.5. FINITE-STATE GRAMMATICAL INFERENCE; 2.6. STRING DISTANCE COMPUTATION; 3 ERROR-CORRECTING FINITE-STATE AUTOMATON FOR RECOGNITION OF RICKER WAVELETS; 3.1. SUMMARY; 3.2. INTRODUCTION; 3.3. SYNTACTIC PATTERN RECOGNITION; 3.3.1. Training and Testing Ricker Wavelets. | |
| 505 | 8 | |a 3.3.2. Location of Waveforms and Pattern Representation3.4. EXPANDED GRAMMARS; 3.4.1. General Expanded Finite-State Grammar; 3.4.2. Restricted Expanded Finite-State Grammar; 3.5. MINIMUM-DISTANCE ERROR-CORRECTING FINITE-STATE PARSING; 3.6. CLASSIFICATION OF RICKER WAVELETS; 3.7. DISCUSSION AND CONCLUSIONS; 4 ATTRIBUTED GRAMMAR AND ERROR-CORRECTING EARLEY'S PARSING; 4.1. SUMMARY; 4.2. INTRODUCTION; 4.3. ATTRIBUTED PRIMITIVES AND STRING; 4.4. DEFINITION OF ERROR TRANSFORMATIONS FOR ATTRIBUTED STRINGS; 4.5. INFERENCE OF ATTRIBUTED GRAMMAR. | |
| 505 | 8 | |a 4.6. MINIMUM-DISTANCE ERROR-CORRECTING EARLEY'S PARSING FOR ATTRIBUTED STRING4.7. EXPERIMENT; 5 ATTRIBUTED GRAMMAR AND MATCH PRIMITIVE MEASURE (MPM) FOR RECOGNITION OF SEISMIC WAVELETS; 5.1. SUMMARY; 5.2. SIMILARITY MEASURE OF ATTRIBUTED STRING MATCHING; 5.3. INFERENCE OF ATTRIBUTED GRAMMAR; 5.4. TOP-DOWN PARSING USING MPM; 5.5. EXPERIMENTS OF SEISMIC PATTERN RECOGNITION; 5.5.1. Recognition of Seismic Ricker Wavelets; 5.5.2. Recognition of Wavelets in Real Seismogram; 5.6. CONCLUSIONS; 6 STRING DISTANCE AND LIKELIHOOD RATIO TEST FOR DETECTION OF CANDIDATE BRIGHT SPOT; 6.1. SUMMARY. | |
| 505 | 8 | |a 6.2. INTRODUCTION6.3. OPTIMAL QUANTIZATION ENCODING; 6.4. LIKELIHOOD RATIO TEST (LRT); 6.5. LEVENSHTEIN DISTANCE AND ERROR PROBABILITY; 6.6. EXPERIMENT AT MISSISSIPPI CANYON; 6.6.1. Likelihood Ratio Test (LRT); 6.6.2. Threshold for Global Detection; 6.6.3. Threshold for the Detection of Candidate Bright Spot; 6.7. EXPERIMENT AT HIGH ISLAND; 7 TREE GRAMMAR AND AUTOMATON FOR SEISMIC PATTERN RECOGNITION; 7.1. SUMMARY; 7.2. INTRODUCTION; 7.3. TREE GRAMMAR AND LANGUAGE; 7.4. TREE AUTOMATON; 7.5. TREE REPRESENTATIONS OF PATTERNS; 7.6. INFERENCE OF EXPANSIVE TREE GRAMMAR. | |
| 505 | 8 | |a 7.7. WEIGHTED MINIMUM-DISTANCE SPECTA7.8. MODIFIED MAXIMUM-LIKELIHOOD SPECTA; 7.9. MINIMUM DISTANCE GECTA; 7.10. EXPERIMENTS ON INPUT TESTING SEISMOGRAMS; 7.11. DISCUSSION AND CONCLUSIONS; 8 A HIERARCHICAL RECOGNITION SYSTEM OF SEISMIC PATTERNS AND FUTURE STUDY; 8.1. SUMMARY; 8.2. INTRODUCTION; 8.3. SYNTACTIC PATTERN RECOGNITION; 8.3.1. Linking Processing and Segmentation; 8.3.2. Primitive Recognition; 8.3.3. Training Patterns; 8.3.4. Grammatical Inference; 8.3.5. Finite-state Error Correcting Parsing; 8.4. COMMON-SOURCE SIMULATED SEISMOGRAM RESULTS; 8.5. STACKED SIMULATED SEISMOGRAM RESULTS. | |
| 520 | |a The use of pattern recognition has become more and more important in seismic oil exploration. Interpreting a large volume of seismic data is a challenging problem. Seismic reflection data in the one-shot seismogram and stacked seismogram may contain some structural information from the response of the subsurface. Syntactic/structural pattern recognition techniques can recognize the structural seismic patterns and improve seismic interpretations. The syntactic analysis methods include: (1) the error-correcting finite-state parsing, (2) the modified error-correcting Earley's parsing, (3) the par. | ||
| 650 | 0 | |a Petroleum |x Prospecting |x Data processing. | |
| 650 | 0 | |a Pattern recognition systems. |0 http://id.loc.gov/authorities/subjects/sh85098791 | |
| 650 | 0 | |a Seismic reflection method |x Data processing. |0 http://id.loc.gov/authorities/subjects/sh85119626 | |
| 650 | 6 | |a Pétrole |x Prospection |x Informatique. | |
| 650 | 6 | |a Reconnaissance des formes (Informatique) | |
| 650 | 6 | |a Méthode sismique-réflexion |x Informatique. | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Mining. |2 bisacsh | |
| 650 | 7 | |a Pattern recognition systems |2 fast | |
| 650 | 7 | |a Petroleum |x Prospecting |x Data processing |2 fast | |
| 650 | 7 | |a Seismic reflection method |x Data processing |2 fast | |
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| contents | AUTHOR'S BIOGRAPHY; PREFACE; CONTENTS; 1 INTRODUCTION TO SYNTACTIC PATTERN RECOGNITION; 1.1. SUMMARY; 1.2. INTRODUCTION; 1.3. ORGANIZATION OF THIS BOOK; 2 INTRODUCTION TO FORMAL LANGUAGES AND AUTOMATA; 2.1. SUMMARY; 2.2. LANGUAGES AND GRAMMARS; 2.3. FINITE-STATE AUTOMATON; 2.4. EARLEY'S PARSING; 2.5. FINITE-STATE GRAMMATICAL INFERENCE; 2.6. STRING DISTANCE COMPUTATION; 3 ERROR-CORRECTING FINITE-STATE AUTOMATON FOR RECOGNITION OF RICKER WAVELETS; 3.1. SUMMARY; 3.2. INTRODUCTION; 3.3. SYNTACTIC PATTERN RECOGNITION; 3.3.1. Training and Testing Ricker Wavelets. 3.3.2. Location of Waveforms and Pattern Representation3.4. EXPANDED GRAMMARS; 3.4.1. General Expanded Finite-State Grammar; 3.4.2. Restricted Expanded Finite-State Grammar; 3.5. MINIMUM-DISTANCE ERROR-CORRECTING FINITE-STATE PARSING; 3.6. CLASSIFICATION OF RICKER WAVELETS; 3.7. DISCUSSION AND CONCLUSIONS; 4 ATTRIBUTED GRAMMAR AND ERROR-CORRECTING EARLEY'S PARSING; 4.1. SUMMARY; 4.2. INTRODUCTION; 4.3. ATTRIBUTED PRIMITIVES AND STRING; 4.4. DEFINITION OF ERROR TRANSFORMATIONS FOR ATTRIBUTED STRINGS; 4.5. INFERENCE OF ATTRIBUTED GRAMMAR. 4.6. MINIMUM-DISTANCE ERROR-CORRECTING EARLEY'S PARSING FOR ATTRIBUTED STRING4.7. EXPERIMENT; 5 ATTRIBUTED GRAMMAR AND MATCH PRIMITIVE MEASURE (MPM) FOR RECOGNITION OF SEISMIC WAVELETS; 5.1. SUMMARY; 5.2. SIMILARITY MEASURE OF ATTRIBUTED STRING MATCHING; 5.3. INFERENCE OF ATTRIBUTED GRAMMAR; 5.4. TOP-DOWN PARSING USING MPM; 5.5. EXPERIMENTS OF SEISMIC PATTERN RECOGNITION; 5.5.1. Recognition of Seismic Ricker Wavelets; 5.5.2. Recognition of Wavelets in Real Seismogram; 5.6. CONCLUSIONS; 6 STRING DISTANCE AND LIKELIHOOD RATIO TEST FOR DETECTION OF CANDIDATE BRIGHT SPOT; 6.1. SUMMARY. 6.2. INTRODUCTION6.3. OPTIMAL QUANTIZATION ENCODING; 6.4. LIKELIHOOD RATIO TEST (LRT); 6.5. LEVENSHTEIN DISTANCE AND ERROR PROBABILITY; 6.6. EXPERIMENT AT MISSISSIPPI CANYON; 6.6.1. Likelihood Ratio Test (LRT); 6.6.2. Threshold for Global Detection; 6.6.3. Threshold for the Detection of Candidate Bright Spot; 6.7. EXPERIMENT AT HIGH ISLAND; 7 TREE GRAMMAR AND AUTOMATON FOR SEISMIC PATTERN RECOGNITION; 7.1. SUMMARY; 7.2. INTRODUCTION; 7.3. TREE GRAMMAR AND LANGUAGE; 7.4. TREE AUTOMATON; 7.5. TREE REPRESENTATIONS OF PATTERNS; 7.6. INFERENCE OF EXPANSIVE TREE GRAMMAR. 7.7. WEIGHTED MINIMUM-DISTANCE SPECTA7.8. MODIFIED MAXIMUM-LIKELIHOOD SPECTA; 7.9. MINIMUM DISTANCE GECTA; 7.10. EXPERIMENTS ON INPUT TESTING SEISMOGRAMS; 7.11. DISCUSSION AND CONCLUSIONS; 8 A HIERARCHICAL RECOGNITION SYSTEM OF SEISMIC PATTERNS AND FUTURE STUDY; 8.1. SUMMARY; 8.2. INTRODUCTION; 8.3. SYNTACTIC PATTERN RECOGNITION; 8.3.1. Linking Processing and Segmentation; 8.3.2. Primitive Recognition; 8.3.3. Training Patterns; 8.3.4. Grammatical Inference; 8.3.5. Finite-state Error Correcting Parsing; 8.4. COMMON-SOURCE SIMULATED SEISMOGRAM RESULTS; 8.5. STACKED SIMULATED SEISMOGRAM RESULTS. |
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SUMMARY; 1.2. INTRODUCTION; 1.3. ORGANIZATION OF THIS BOOK; 2 INTRODUCTION TO FORMAL LANGUAGES AND AUTOMATA; 2.1. SUMMARY; 2.2. LANGUAGES AND GRAMMARS; 2.3. FINITE-STATE AUTOMATON; 2.4. EARLEY'S PARSING; 2.5. FINITE-STATE GRAMMATICAL INFERENCE; 2.6. STRING DISTANCE COMPUTATION; 3 ERROR-CORRECTING FINITE-STATE AUTOMATON FOR RECOGNITION OF RICKER WAVELETS; 3.1. SUMMARY; 3.2. INTRODUCTION; 3.3. SYNTACTIC PATTERN RECOGNITION; 3.3.1. Training and Testing Ricker Wavelets.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.3.2. Location of Waveforms and Pattern Representation3.4. EXPANDED GRAMMARS; 3.4.1. General Expanded Finite-State Grammar; 3.4.2. Restricted Expanded Finite-State Grammar; 3.5. MINIMUM-DISTANCE ERROR-CORRECTING FINITE-STATE PARSING; 3.6. CLASSIFICATION OF RICKER WAVELETS; 3.7. DISCUSSION AND CONCLUSIONS; 4 ATTRIBUTED GRAMMAR AND ERROR-CORRECTING EARLEY'S PARSING; 4.1. SUMMARY; 4.2. INTRODUCTION; 4.3. ATTRIBUTED PRIMITIVES AND STRING; 4.4. 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EXPERIMENT AT MISSISSIPPI CANYON; 6.6.1. Likelihood Ratio Test (LRT); 6.6.2. Threshold for Global Detection; 6.6.3. Threshold for the Detection of Candidate Bright Spot; 6.7. EXPERIMENT AT HIGH ISLAND; 7 TREE GRAMMAR AND AUTOMATON FOR SEISMIC PATTERN RECOGNITION; 7.1. SUMMARY; 7.2. INTRODUCTION; 7.3. TREE GRAMMAR AND LANGUAGE; 7.4. TREE AUTOMATON; 7.5. TREE REPRESENTATIONS OF PATTERNS; 7.6. INFERENCE OF EXPANSIVE TREE GRAMMAR.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">7.7. WEIGHTED MINIMUM-DISTANCE SPECTA7.8. MODIFIED MAXIMUM-LIKELIHOOD SPECTA; 7.9. MINIMUM DISTANCE GECTA; 7.10. EXPERIMENTS ON INPUT TESTING SEISMOGRAMS; 7.11. DISCUSSION AND CONCLUSIONS; 8 A HIERARCHICAL RECOGNITION SYSTEM OF SEISMIC PATTERNS AND FUTURE STUDY; 8.1. SUMMARY; 8.2. INTRODUCTION; 8.3. SYNTACTIC PATTERN RECOGNITION; 8.3.1. Linking Processing and Segmentation; 8.3.2. Primitive Recognition; 8.3.3. Training Patterns; 8.3.4. Grammatical Inference; 8.3.5. 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| id | ZDB-4-EBA-ocn261470345 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:16:32Z |
| institution | BVB |
| isbn | 9789812775740 9812775749 |
| language | English |
| oclc_num | 261470345 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xiv, 133 pages) : illustrations |
| psigel | ZDB-4-EBA |
| publishDate | 2002 |
| publishDateSearch | 2002 |
| publishDateSort | 2002 |
| publisher | World Scientific, |
| record_format | marc |
| series | Series in machine perception and artificial intelligence ; |
| series2 | Series in machine perception and artificial intelligence ; |
| spelling | Huang, Kou-Yuan. http://id.loc.gov/authorities/names/n2002155318 Syntactic pattern recognition for seismic oil exploration / Kou-Yuan Huang. River Edge, NJ : World Scientific, 2002. 1 online resource (xiv, 133 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Series in machine perception and artificial intelligence ; v. 46 Includes bibliographical references (pages 123-129) and index. Print version record. AUTHOR'S BIOGRAPHY; PREFACE; CONTENTS; 1 INTRODUCTION TO SYNTACTIC PATTERN RECOGNITION; 1.1. SUMMARY; 1.2. INTRODUCTION; 1.3. ORGANIZATION OF THIS BOOK; 2 INTRODUCTION TO FORMAL LANGUAGES AND AUTOMATA; 2.1. SUMMARY; 2.2. LANGUAGES AND GRAMMARS; 2.3. FINITE-STATE AUTOMATON; 2.4. EARLEY'S PARSING; 2.5. FINITE-STATE GRAMMATICAL INFERENCE; 2.6. STRING DISTANCE COMPUTATION; 3 ERROR-CORRECTING FINITE-STATE AUTOMATON FOR RECOGNITION OF RICKER WAVELETS; 3.1. SUMMARY; 3.2. INTRODUCTION; 3.3. SYNTACTIC PATTERN RECOGNITION; 3.3.1. Training and Testing Ricker Wavelets. 3.3.2. Location of Waveforms and Pattern Representation3.4. EXPANDED GRAMMARS; 3.4.1. General Expanded Finite-State Grammar; 3.4.2. Restricted Expanded Finite-State Grammar; 3.5. MINIMUM-DISTANCE ERROR-CORRECTING FINITE-STATE PARSING; 3.6. CLASSIFICATION OF RICKER WAVELETS; 3.7. DISCUSSION AND CONCLUSIONS; 4 ATTRIBUTED GRAMMAR AND ERROR-CORRECTING EARLEY'S PARSING; 4.1. SUMMARY; 4.2. INTRODUCTION; 4.3. ATTRIBUTED PRIMITIVES AND STRING; 4.4. DEFINITION OF ERROR TRANSFORMATIONS FOR ATTRIBUTED STRINGS; 4.5. INFERENCE OF ATTRIBUTED GRAMMAR. 4.6. MINIMUM-DISTANCE ERROR-CORRECTING EARLEY'S PARSING FOR ATTRIBUTED STRING4.7. EXPERIMENT; 5 ATTRIBUTED GRAMMAR AND MATCH PRIMITIVE MEASURE (MPM) FOR RECOGNITION OF SEISMIC WAVELETS; 5.1. SUMMARY; 5.2. SIMILARITY MEASURE OF ATTRIBUTED STRING MATCHING; 5.3. INFERENCE OF ATTRIBUTED GRAMMAR; 5.4. TOP-DOWN PARSING USING MPM; 5.5. EXPERIMENTS OF SEISMIC PATTERN RECOGNITION; 5.5.1. Recognition of Seismic Ricker Wavelets; 5.5.2. Recognition of Wavelets in Real Seismogram; 5.6. CONCLUSIONS; 6 STRING DISTANCE AND LIKELIHOOD RATIO TEST FOR DETECTION OF CANDIDATE BRIGHT SPOT; 6.1. SUMMARY. 6.2. INTRODUCTION6.3. OPTIMAL QUANTIZATION ENCODING; 6.4. LIKELIHOOD RATIO TEST (LRT); 6.5. LEVENSHTEIN DISTANCE AND ERROR PROBABILITY; 6.6. EXPERIMENT AT MISSISSIPPI CANYON; 6.6.1. Likelihood Ratio Test (LRT); 6.6.2. Threshold for Global Detection; 6.6.3. Threshold for the Detection of Candidate Bright Spot; 6.7. EXPERIMENT AT HIGH ISLAND; 7 TREE GRAMMAR AND AUTOMATON FOR SEISMIC PATTERN RECOGNITION; 7.1. SUMMARY; 7.2. INTRODUCTION; 7.3. TREE GRAMMAR AND LANGUAGE; 7.4. TREE AUTOMATON; 7.5. TREE REPRESENTATIONS OF PATTERNS; 7.6. INFERENCE OF EXPANSIVE TREE GRAMMAR. 7.7. WEIGHTED MINIMUM-DISTANCE SPECTA7.8. MODIFIED MAXIMUM-LIKELIHOOD SPECTA; 7.9. MINIMUM DISTANCE GECTA; 7.10. EXPERIMENTS ON INPUT TESTING SEISMOGRAMS; 7.11. DISCUSSION AND CONCLUSIONS; 8 A HIERARCHICAL RECOGNITION SYSTEM OF SEISMIC PATTERNS AND FUTURE STUDY; 8.1. SUMMARY; 8.2. INTRODUCTION; 8.3. SYNTACTIC PATTERN RECOGNITION; 8.3.1. Linking Processing and Segmentation; 8.3.2. Primitive Recognition; 8.3.3. Training Patterns; 8.3.4. Grammatical Inference; 8.3.5. Finite-state Error Correcting Parsing; 8.4. COMMON-SOURCE SIMULATED SEISMOGRAM RESULTS; 8.5. STACKED SIMULATED SEISMOGRAM RESULTS. The use of pattern recognition has become more and more important in seismic oil exploration. Interpreting a large volume of seismic data is a challenging problem. Seismic reflection data in the one-shot seismogram and stacked seismogram may contain some structural information from the response of the subsurface. Syntactic/structural pattern recognition techniques can recognize the structural seismic patterns and improve seismic interpretations. The syntactic analysis methods include: (1) the error-correcting finite-state parsing, (2) the modified error-correcting Earley's parsing, (3) the par. Petroleum Prospecting Data processing. Pattern recognition systems. http://id.loc.gov/authorities/subjects/sh85098791 Seismic reflection method Data processing. http://id.loc.gov/authorities/subjects/sh85119626 Pétrole Prospection Informatique. Reconnaissance des formes (Informatique) Méthode sismique-réflexion Informatique. TECHNOLOGY & ENGINEERING Mining. bisacsh Pattern recognition systems fast Petroleum Prospecting Data processing fast Seismic reflection method Data processing fast Print version: Huang, Kou-Yuan. Syntactic pattern recognition for seismic oil exploration. River Edge, NJ : World Scientific, 2002 9810246005 9789810246006 (DLC) 2002514235 (OCoLC)50134555 Series in machine perception and artificial intelligence ; v. 46. http://id.loc.gov/authorities/names/n91107585 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=235838 Volltext |
| spellingShingle | Huang, Kou-Yuan Syntactic pattern recognition for seismic oil exploration / Series in machine perception and artificial intelligence ; AUTHOR'S BIOGRAPHY; PREFACE; CONTENTS; 1 INTRODUCTION TO SYNTACTIC PATTERN RECOGNITION; 1.1. SUMMARY; 1.2. INTRODUCTION; 1.3. ORGANIZATION OF THIS BOOK; 2 INTRODUCTION TO FORMAL LANGUAGES AND AUTOMATA; 2.1. SUMMARY; 2.2. LANGUAGES AND GRAMMARS; 2.3. FINITE-STATE AUTOMATON; 2.4. EARLEY'S PARSING; 2.5. FINITE-STATE GRAMMATICAL INFERENCE; 2.6. STRING DISTANCE COMPUTATION; 3 ERROR-CORRECTING FINITE-STATE AUTOMATON FOR RECOGNITION OF RICKER WAVELETS; 3.1. SUMMARY; 3.2. INTRODUCTION; 3.3. SYNTACTIC PATTERN RECOGNITION; 3.3.1. Training and Testing Ricker Wavelets. 3.3.2. Location of Waveforms and Pattern Representation3.4. EXPANDED GRAMMARS; 3.4.1. General Expanded Finite-State Grammar; 3.4.2. Restricted Expanded Finite-State Grammar; 3.5. MINIMUM-DISTANCE ERROR-CORRECTING FINITE-STATE PARSING; 3.6. CLASSIFICATION OF RICKER WAVELETS; 3.7. DISCUSSION AND CONCLUSIONS; 4 ATTRIBUTED GRAMMAR AND ERROR-CORRECTING EARLEY'S PARSING; 4.1. SUMMARY; 4.2. INTRODUCTION; 4.3. ATTRIBUTED PRIMITIVES AND STRING; 4.4. DEFINITION OF ERROR TRANSFORMATIONS FOR ATTRIBUTED STRINGS; 4.5. INFERENCE OF ATTRIBUTED GRAMMAR. 4.6. MINIMUM-DISTANCE ERROR-CORRECTING EARLEY'S PARSING FOR ATTRIBUTED STRING4.7. EXPERIMENT; 5 ATTRIBUTED GRAMMAR AND MATCH PRIMITIVE MEASURE (MPM) FOR RECOGNITION OF SEISMIC WAVELETS; 5.1. SUMMARY; 5.2. SIMILARITY MEASURE OF ATTRIBUTED STRING MATCHING; 5.3. INFERENCE OF ATTRIBUTED GRAMMAR; 5.4. TOP-DOWN PARSING USING MPM; 5.5. EXPERIMENTS OF SEISMIC PATTERN RECOGNITION; 5.5.1. Recognition of Seismic Ricker Wavelets; 5.5.2. Recognition of Wavelets in Real Seismogram; 5.6. CONCLUSIONS; 6 STRING DISTANCE AND LIKELIHOOD RATIO TEST FOR DETECTION OF CANDIDATE BRIGHT SPOT; 6.1. SUMMARY. 6.2. INTRODUCTION6.3. OPTIMAL QUANTIZATION ENCODING; 6.4. LIKELIHOOD RATIO TEST (LRT); 6.5. LEVENSHTEIN DISTANCE AND ERROR PROBABILITY; 6.6. EXPERIMENT AT MISSISSIPPI CANYON; 6.6.1. Likelihood Ratio Test (LRT); 6.6.2. Threshold for Global Detection; 6.6.3. Threshold for the Detection of Candidate Bright Spot; 6.7. EXPERIMENT AT HIGH ISLAND; 7 TREE GRAMMAR AND AUTOMATON FOR SEISMIC PATTERN RECOGNITION; 7.1. SUMMARY; 7.2. INTRODUCTION; 7.3. TREE GRAMMAR AND LANGUAGE; 7.4. TREE AUTOMATON; 7.5. TREE REPRESENTATIONS OF PATTERNS; 7.6. INFERENCE OF EXPANSIVE TREE GRAMMAR. 7.7. WEIGHTED MINIMUM-DISTANCE SPECTA7.8. MODIFIED MAXIMUM-LIKELIHOOD SPECTA; 7.9. MINIMUM DISTANCE GECTA; 7.10. EXPERIMENTS ON INPUT TESTING SEISMOGRAMS; 7.11. DISCUSSION AND CONCLUSIONS; 8 A HIERARCHICAL RECOGNITION SYSTEM OF SEISMIC PATTERNS AND FUTURE STUDY; 8.1. SUMMARY; 8.2. INTRODUCTION; 8.3. SYNTACTIC PATTERN RECOGNITION; 8.3.1. Linking Processing and Segmentation; 8.3.2. Primitive Recognition; 8.3.3. Training Patterns; 8.3.4. Grammatical Inference; 8.3.5. Finite-state Error Correcting Parsing; 8.4. COMMON-SOURCE SIMULATED SEISMOGRAM RESULTS; 8.5. STACKED SIMULATED SEISMOGRAM RESULTS. Petroleum Prospecting Data processing. Pattern recognition systems. http://id.loc.gov/authorities/subjects/sh85098791 Seismic reflection method Data processing. http://id.loc.gov/authorities/subjects/sh85119626 Pétrole Prospection Informatique. Reconnaissance des formes (Informatique) Méthode sismique-réflexion Informatique. TECHNOLOGY & ENGINEERING Mining. bisacsh Pattern recognition systems fast Petroleum Prospecting Data processing fast Seismic reflection method Data processing fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85098791 http://id.loc.gov/authorities/subjects/sh85119626 |
| title | Syntactic pattern recognition for seismic oil exploration / |
| title_auth | Syntactic pattern recognition for seismic oil exploration / |
| title_exact_search | Syntactic pattern recognition for seismic oil exploration / |
| title_full | Syntactic pattern recognition for seismic oil exploration / Kou-Yuan Huang. |
| title_fullStr | Syntactic pattern recognition for seismic oil exploration / Kou-Yuan Huang. |
| title_full_unstemmed | Syntactic pattern recognition for seismic oil exploration / Kou-Yuan Huang. |
| title_short | Syntactic pattern recognition for seismic oil exploration / |
| title_sort | syntactic pattern recognition for seismic oil exploration |
| topic | Petroleum Prospecting Data processing. Pattern recognition systems. http://id.loc.gov/authorities/subjects/sh85098791 Seismic reflection method Data processing. http://id.loc.gov/authorities/subjects/sh85119626 Pétrole Prospection Informatique. Reconnaissance des formes (Informatique) Méthode sismique-réflexion Informatique. TECHNOLOGY & ENGINEERING Mining. bisacsh Pattern recognition systems fast Petroleum Prospecting Data processing fast Seismic reflection method Data processing fast |
| topic_facet | Petroleum Prospecting Data processing. Pattern recognition systems. Seismic reflection method Data processing. Pétrole Prospection Informatique. Reconnaissance des formes (Informatique) Méthode sismique-réflexion Informatique. TECHNOLOGY & ENGINEERING Mining. Pattern recognition systems Petroleum Prospecting Data processing Seismic reflection method Data processing |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=235838 |
| work_keys_str_mv | AT huangkouyuan syntacticpatternrecognitionforseismicoilexploration |