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Record W2175594671 · doi:10.1190/tle34080882.1

Overview of moment-tensor inversion of microseismic events

2015· article· en· W2175594671 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Leading Edge · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsUniversity of Alberta
FundersMicroseismic Industry Consortium
KeywordsMicroseismInversion (geology)AmplitudeMoment tensorWaveformComputer scienceGeologySeismologyAlgorithmPhysicsTelecommunicationsOptics

Abstract

fetched live from OpenAlex

Abstract Understanding the source mechanisms of microseismic events is important for understanding the fracturing behavior and evolving stress field within a reservoir, knowledge of which can help to improve production and minimize seismic risk. The most common method for calculating the source mechanisms is moment-tensor inversion, which can provide the magnitudes, modes, and orientations of fractures. An overview of three common methods includes their advantages and limitations: the first-arrival polarity method, amplitude methods, and the full-waveform method. The first-arrival method is the quickest to implement but also the crudest, likely producing the least reliable results. Amplitude methods are also relatively simple but can better constrain the inversion because of the increased number of observations, especially those using S/P amplitude ratios. Full-waveform methods can provide results of very good quality, including source-time functions, but involve much more complex and expensive calculations and rely on accurate seismic-velocity models.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.077
GPT teacher head0.280
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it