US 12,412,036 B2
Method and system for generating geological lithostratigraphic analogues using theory-guided machine learning from unstructured text
Paul Hugh Cleverley, Wallingford (GB)
Filed by Paul Hugh Cleverley, Wallingford (GB)
Filed on Jul. 31, 2020, as Appl. No. 16/944,425.
Prior Publication US 2022/0036008 A1, Feb. 3, 2022
Int. Cl. G06F 40/295 (2020.01); G06F 40/284 (2020.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01)
CPC G06F 40/295 (2020.01) [G06F 40/284 (2020.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01)] 3 Claims
OG exemplary drawing
 
1. A computer implemented process for suggesting space-time aware geological Lithostratigraphic analogues from text using theory-guided machine learning, the process comprising:
receiving into a computer memory: sentences of text, geographical lexicons, negation/negative terms, geological time lexicons and Natural Language Processing (NLP) rules, processing a data in the computer memory with a processor to detect geological Lithostratigraphic entity names,
processing the data in the computer memory with a processor to compute similarity between Lithostratigraphic entities through word associations,
applying a filter around the detected geological Lithostratigraphic entities, the filter having a dynamic width defined by a window length comprising a first mention of a Lithostratigraphic entity and a subsequent mention of a different Lithostratigraphic entity, removing all geographical terms from these text and combining the texts as it relates to each Lithostratigraphic entity, the filter output providing an input to a learned statistical model where the positive and negative user feedback can be incorporated in the statistical ranking model, weighting from high to low nouns, adjectives, and verbs, from which a Lithostratigraphic entity-entity similarity is determined; and
outputting the Lithostratigraphic entity-entity similarity through cosine similarity.