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Pandora Sicilia s.r.l. 082 04$a733.3 100 1 $aRizzo, Giulio Emanuele$089079 245 10$aPrassitele /$cG. E. Rizzo 260 $aMilano ;$aRoma :$bTreves-Treccani-Tumminelli,$c1932-40 300 $a121 p., CLIX p. di tav. :$bill. ;$c31 cm 440 2$aI geni e le opere 650 4$aScultura$xPrassitele$xOpere 907 $a.b13016234$b02-04-14$c12-07-04 912 $a991003047229707536 945 $aLE001 AR IV 11 4$g1$i2001000107667$lle001$nC. 1$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i13628677$z12-07-04 996 $aPrassitele$9289019 997 $aUNISALENTO 998 $ale001$b12-07-04$cm$da $e-$fita$git $h0$i1 LEADER 05608nam 2200685 a 450 001 9910782392103321 005 20230607222125.0 010 $a1-281-92835-6 010 $a9786611928353 010 $a981-277-574-9 035 $a(CKB)1000000000538032 035 $a(EBL)1679492 035 $a(OCoLC)879074231 035 $a(SSID)ssj0000254780 035 $a(PQKBManifestationID)11213493 035 $a(PQKBTitleCode)TC0000254780 035 $a(PQKBWorkID)10213515 035 $a(PQKB)10871841 035 $a(MiAaPQ)EBC1679492 035 $a(WSP)00004682 035 $a(Au-PeEL)EBL1679492 035 $a(CaPaEBR)ebr10255381 035 $a(CaONFJC)MIL192835 035 $a(EXLCZ)991000000000538032 100 $a20021010d2002 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aSyntactic pattern recognition for seismic oil exploration$b[electronic resource] /$fKou-Yuan Huang 210 $aRiver Edge, NJ $cWorld Scientific$dc2002 215 $a1 online resource (149 p.) 225 1 $aSeries in machine perception and artificial intelligence ;$vv. 46 300 $aDescription based upon print version of record. 311 $a981-02-4600-5 320 $aIncludes bibliographical references (p. 123-129) and index. 327 $aCONTENTS; AUTHOR'S BIOGRAPHY; PREFACE; 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 327 $a3.3.2. 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 327 $a4.6. 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 327 $a6.2. 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 327 $a7.7. 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 327 $a8.6. CONCLUSIONS 330 $a The use of pattern recognition has become more and more important in seismic oil exploration. Interpreting a large volume of seismic data is a challenging problem. Seismic reflection data in the one-shot seismogram and stacked seismogram may contain some structural information from the response of the subsurface. Syntactic/structural pattern recognition techniques can recognize the structural seismic patterns and improve seismic interpretations. The syntactic analysis methods include: (1) the error-correcting finite-state parsing, (2) the modified error-correcting Earley's parsing, (3) the p 410 0$aSeries in machine perception and artificial intelligence ;$vv. 46. 606 $aPetroleum$xProspecting$xData processing 606 $aPattern recognition systems 606 $aSeismic reflection method$xData processing 615 0$aPetroleum$xProspecting$xData processing. 615 0$aPattern recognition systems. 615 0$aSeismic reflection method$xData processing. 676 $a622/.1828/0285 700 $aHuang$b Kou-Yuan$01508188 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910782392103321 996 $aSyntactic pattern recognition for seismic oil exploration$93739464 997 $aUNINA