1 / 13

A Draft for Complex Formal Approach in Geoscience

A Draft for Complex Formal Approach in Geoscience. Cyril Pshenichny Levinson-Lessing Earthcrust Institute (NIIZK), Faculty of Geology, St. Petersburg State University 7/9 Universittskaya Naberezhnaya 199034 St. Petersburg, Russia email pshenich@kp1306.spb.edu, pshenich@pochtamt.ru;

laplante
Download Presentation

A Draft for Complex Formal Approach in Geoscience

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. A Draftfor Complex Formal Approach in Geoscience Cyril Pshenichny Levinson-Lessing Earthcrust Institute (NIIZK), Faculty of Geology, St. Petersburg State University 7/9 Universittskaya Naberezhnaya 199034 St. Petersburg, Russia email pshenich@kp1306.spb.edu, pshenich@pochtamt.ru; www.volcanology.ru

  2. Flourishing of formal approaches Problems we face on Earth Our results should be: (i) methodologically correct, (ii) strict and definite (iii) understandable by non-professionals Our approaches are not organized Complex formal approach!

  3. Abundant data If contradiction Preceding knowledge (types SEE, THINK, IMAGINE) Inductive generalization Strict theory If no contradiction Scarce data Strict model Complex forecast Deductive forecast Inductive forecast The Draft Proper DATA KNOWLEDGE

  4. What Is What: Data-Oriented Approaches Abundant data Inductive generalization Geostatistics: two-dimensional - multidimensional, parametric - non-parametric,etc.(Matheron, etc.) Database theory (Codd) Fuzzy sets theory (Zadeh) SMTH ELSE? Scarce data Inductive generalization Inductive “logic”, multivalued logic, SMTH ELSE?

  5. knowledge SEE: observations knowledge THINK: physical/chemical models knowledge IMAGINE: tectonic, cosmologic, etc., theories What Is What: Knowledge-Oriented Approaches Preceding knowledge (types SEE, THINK, IMAGINE) Margins conventional!!!

  6. Knowledge SEE: Rhyolite is an ash forming extended sheets or a massive or flow-banded crystal-poor lava forming short thick flows or domes. Knowledge THINK: Rhyolite is a residual melt resulting from crystallization of most silica-rich and low-temperature mineral phases. Knowledge IMAGINE: Rhyolite is a result of partial melting in the crust caused by collision of lithosphere plates or crustal contamination of products of melting of oceanic plate at subduction. Types of Contents of Knowledge in Geoscience: An Example A point for Leo Maslov’s approach?

  7. What Is What: Knowledge-Oriented Approaches Preceding knowledge (types SEE, THINK, IMAGINE) Strict theory 1. “Purification” of knowledge: knowledge engineering, information technologies, languages of knowledge representation, WHAT ELSE? 2. Building strict theories: logic, deterministic mathematics theories, topology, network theory, systems theory, catastrophe theory, geosemiotics, philosophy, physics, chemistry, ANYTHING ELSE FORMAL TO GEO-KNOWLEDGE “Concept - model” polemics at Georeasoning; C. Smyth’s talk!

  8. The Draft Reads 1. Complete formal solution  data and knowledge. 2. Knowledge lacking  data; inductive assessment; statistical probability. 3. Data lacking  knowledge; deductive assessment; conceptual probability. 4. Data contradict to existing knowledge  inductive approaches lead to re-formulation of knowledge. 6. Data do not contradict to the knowledge  logical modeling of the studied phenomenon/process can be performed. 7. Logical model is a working (i.e., complete and self-consistent) strict theory + logical calculus + data.. It should give perfect assessment with complex (statistical + conceptual) value of probability. Answers end, questions start

  9. Interrelation of Approaches: Probability vs. Logic (Data vs. Knowledge)? Strictness of knowledge: Strict knowledge; logical assessments; no data needed ??? No knowledge; statistical assessments; complete data needed C o n c e p t u a l u n c e r t a i n t y!

  10. Deductive reasoning; formulation of context Growth of a theory Probability vs. Logic - continued Continuum of formal approaches: Inductive reasoning; formulation of premises Probabilistic approach Logical approach Database/predicate logic polemics at Georeasoning; S. Henley’s talk!

  11. Knowledge Engineering, Logic, Network Theory and Probability Theory in a Growing Deductive System Knowledge Base Intuitive Formal Interpretation in terms of network theory Formalization by propositional logic Probabilistic treatment (for big knowledge bases) Logical model by means of predicate logic

  12. Interrelation of Approaches: More Examples Fuzzy Sets vs. Probability Theory & Statistics - Technometrics, REF., G. Bardossy’s talk Probability theory vs. Geosemiotics (Baker, 1999) Geostatistics vs. Deterministic Mathematical Modeling (S. Kotov’s talk at S???) Logical Tools for Qualitative Data Processing - Sirotinskaya (1978) Small Groups Statistics vs. Inductive Logic Whatever else

  13. Conclusion Georeasoning is a study of interrelation of various formal approaches, which have formed and now transform geoscience. This study aims to identify, • what approach (or method) to use in a given case, • how do different approaches and methods relate to each other, • how to make their application most perfect?

More Related