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Enregistrement W3168666834 · doi:10.4133/sageep.33-070

The evolution of geoscientific software — The past, present, and future

2021· article· en· W3168666834 sur OpenAlexaboutno aff
Lorraine Godwin, Nick Valleau, Darren Mortimer

Notice bibliographique

RevueSymposium on the Application of Geophysics to Engineering and Environmental Problems 2021 · 2021
Typearticle
Langueen
DomaineComputer Science
ThématiqueDistributed and Parallel Computing Systems
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPaceSoftwareComputer scienceIBMGeologyEarth scienceGeophysics

Résumé

récupéré en direct d'OpenAlex

Geoscience software has kept pace with the rapidly changing evolution of both computer hardware and geoscience technology over the last 35 years. Advancements in scientific research, personal computers, operating systems, geophysical and remote sensing instruments, data storage, and computing power have all provided drivers for change. Measurements of physical properties of the Earth date back to ancient times but it wasn't until the 19th century that geophysics emerged as a discipline to understand our planet's shape, density and gravity field, the water cycle, plate tectonics, resource exploration and later, in the 20th century, for remote exploration of the solid Earth and the ocean. The first exploration magnetometers evolved from fluxgate magnetometers which had been used during World War II to detect submarines. Early geophysical maps were hand-contoured – a time consuming and tedious affair. The skill of the draftsperson was used to interpolate data between survey lines which could lead to variable results. When IBM first introduced the personal computer (PC) in 1981 they revolutionized computer technology. The 1980s became an explosive period of growth in the computer and software industry. Prior to this, companies hired in-house programmers to write software for their specific needs. With the advent of the PC, independent publishers and open architecture changed the face of software development. In 1982, Canadian earth scientists Colin Reeves and Ian MacLeod saw the potential for personal computers to transform the work of geophysics and exploration. From this early vision, Geosoft Inc. was established in 1986 and has developed to become the sustainable industry standard for geophysical data processing and analysis in multiple industry sectors. The computing power of PCs and laptops, combined with software, provided an efficient way to process, interpret, present and enhance large volumes of geophysical measurements of the Earth to create maps and models of the subsurface which could be seen, examined and discussed with others. When advancements such as Global Positioning Systems (GPS), originally created for military purposes, became accessible for civilian use, positioning of survey data vastly improved. Ground, airborne, and marine geophysical surveys could easily and accurately pinpoint anomalies using GPS. Geologists could now create pseudo-geologic maps from magnetic or resistivity images, along with the vast quantity of numerical data that was being collected by large arrays of other geophysical and remote sensing techniques. Geosoft's first software program was called MAGMOD, which modeled magnetic profiles over selected anomalies. In the early 1990's the “Sushi” menu-driven interface was developed. With the introduction of Windows 95, Sushi was replaced by the Oasis montaj graphical Windows interface and data structures in 1996 for powerful processing and interpretation of geophysical data. Oasis montaj has continued to evolve to provide an extensive suite of processing, modelling and analysis tools for a variety of geoscience and remote sensing data, encompassing geophysics, geology, geochemistry and GIS to provide advanced understanding of the Earth's subsurface and subsea environments. In 2012, Geosoft launched VOXI, a rapid and responsive solution for large, multi-parameter geophysical inversion modelling that harnesses the power of cloud computing. In the early 2000s, Seequent (formerly ARANZ Geo) was founded in Christchurch, New Zealand, with the development of ground-breaking medical 3D imaging technology, that offered a rapid and visual way of gaining accurate information for medical diagnostics. This implicit modelling technology was further developed and in 2004 was first applied to geological science. Offering a new approach to geological modelling, the software made the modelling process not only dramatically faster, but the 3D capabilities also made it highly visual and intuitive. The solution provided a paradigm shift in understanding the earth's subsurface, and quickly became well known as the flagship product called Leapfrog. In 2018, Seequent Limited and Geosoft Inc. joined forces, bringing together Leapfrog 3D geological modelling software with Geosoft's subsurface geoscience software. Today, Seequent is a global leader in the development of powerful geoscience analysis, modelling, and collaborative technologies that enable people to uncover valuable insights from data, creating rich stories and ultimately make better decisions about earth, environment, and energy challenges to address some of the world's biggest challenges.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,010
score de la tête « metaresearch » (Gemma)0,029
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: aucune
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,054

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0100,029
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,004
Études des sciences et des technologies0,0020,007
Communication savante0,0100,020
Science ouverte0,0030,004
Intégrité de la recherche0,0030,006
Charge utile insuffisante (le modèle a refusé de juger)0,0060,004

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,003
Tête enseignante GPT0,165
Écart entre enseignants0,162 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2021
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueSymposium on the Application of Geophysics to Engineering and Environmental Problems 2021Même sujetDistributed and Parallel Computing SystemsTravaux en français237 207