By Guangren Shi
Currently there are significant demanding situations in info mining purposes within the geosciences. this can be due essentially to the truth that there's a wealth of accessible mining information amid a lack of the information and services essential to learn and correctly interpret an identical data. Most geoscientists haven't any functional wisdom or event utilizing facts mining recommendations. For the few that do, they generally lack services in utilizing facts mining software program and in picking out the main applicable algorithms for a given software. This ends up in a paradoxical situation of ''rich facts yet bad knowledge''.
The actual resolution is to use facts mining suggestions in geosciences databases and to change those thoughts for useful purposes. Authored by means of an international concept chief in information mining, Data Mining and information Discovery for Geoscientists addresses those demanding situations by way of summarizing the newest advancements in geosciences facts mining and arming scientists being able to observe key innovations to successfully examine and interpret substantial quantities of serious information.
- Focuses on 22 of information mining's so much sensible algorithms and well known program samples
- Features 36 case reports and end-of-chapter workouts precise to the geosciences to underscore key information mining applications
- Presents a pragmatic and built-in approach of information mining and information discovery for geoscientists
- Rigorous but extensively available to geoscientists, engineers, researchers and programmers in info mining
- Introduces customary algorithms, their uncomplicated rules and prerequisites of functions, various case experiences, and indicates algorithms which may be appropriate for particular applications
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Additional info for Data Mining and Knowledge Discovery for Geoscientists
2. 4. 1. 2. 3. 5. 1. 2. 6. 1. 2. 2. 1. 2. 3. 1. 2. 3. 4. 3. 1. 2. 3. 1. 2. Prediction Process 69 70 71 71 73 54 73 74 74 74 75 78 78 78 78 80 80 80 82 82 82 84 Copyright Ó 2014 Petroleum Industry Press. Published by Elsevier Inc. All rights reserved. 3. 3. 4. Summary and Conclusions 85 55 Exercises 86 References 86 85 Artificial neural networks (ANN) constitute a branch of artificial intelligence. This chapter introduces an error back-propagation neural network (BPNN) in ANN as well as its applications in geosciences.
Simple Case Study 3 is the prediction of fracture-acidizing results, explaining a conventional prediction of BPNN. Concretely, using the learning samples of seven wells for the prediction problem of fracture-acidizing results, the structure of BPNN and the final values of Wij, Wjk, qj and qk at topt ¼ 55095 are obtained by the data mining tool of BPNN, and the results coincide with practicality. This structure and these final values are called mined knowledge. In the prediction process, this knowledge can be adopted to predict the fracture-acidizing results in the eighth well, and the results are basically correct.
Operation 5. It is the elimination to introduce or eliminate xk . 2. 40) ði ¼ k; j ¼ kÞ kk Thus, the regression results of Step 1 are listed here: Introduce or eliminate: No elimination occurs in Step 1. 41) ð1Þ sy sk . ¼ rky ð1Þ Residual variance: Qð1Þ ¼ ryy . Multiple correlation coefficient: Rð1Þ ¼ qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ ﬃ ð1Þ . 1ÀQ Qð0Þ The 2nd, 3rd, . step regression processes are the same as the 1st-step regression process. To clearly describe, s is denoted as the regression process number, and s ¼ 1, 2, .