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Record W7160772811 · doi:10.83080/rejost.vol4no5.135

Depth to basement estimation from aerogravity data over the Southeastern part of Niger Delta region of Nigeria

2024· article· en· W7160772811 on OpenAlexaboutno aff
Aniekan E. Ekpo, Nsikak E. Bassey, Nyakno J. George, Itoro G. Udo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsnot available
Fundersnot available
KeywordsBasementBouguer anomalyLineamentResidualNiger deltaKarst

Abstract

fetched live from OpenAlex

This study presents a geological interpretation of aerogravity anomalies over the southeastern part of the Niger Delta Basin, Nigeria, with a focus on identifying subsurface structures related to hydrocarbon and mineral prospecting. High-resolution aerogravity data, collected by Fugro Airborne Surveys Canada between 2005 and 2010, were processed using Oasis Montaj (v7.0.1), Surfer (v16.6), and ArcGIS (v10.4) software. The study employed various data filtering and enhancement techniques, including Fast Fourier Transform (FFT) for spectral analysis, Euler deconvolution, and 2D forward modeling. The Butterworth filter, with a central wave number of 1900 and degree 2, was used to separate regional and residual anomalies. The regional gravity anomaly map exhibited a Bouguer gravity range from -7.3 mGal to 39.4 mGal, with prominent trends in NE-SW, N-S, and NW-SE directions, indicating deep-seated subsurface structures. The residual gravity map, filtered with a central wave number of 3 and degree 8, revealed structural features including linear, ellipsoidal, and irregular-shaped anomalies extending primarily in the N-S, NE-SW, and NW-SE directions. Euler deconvolution analysis showed depth ranges from a minimum of less than 34.4 m to a maximum of over 8,228 m, identifying subsurface fault structures aligned with the prevailing lineament patterns of the Niger Delta. Radial power spectrum analysis, applied to eight subdivided blocks of the study area, identified three depth ranges: shallow, intermediate, and deep sources. The 2D forward modeling using the GM-SYS package delineated the basement topography and subsurface structures across five key profiles, revealing undulating basement features with depths varying from approximately 6,000 m to over 10,000 m. Significant geological features were identified, including a major dyke intrusion along Profile 1 and domal uplift features along Profile 2, suggesting mantle plume activity. The results indicate a complex subsurface architecture characterized by variations in rock density and structural configurations, suggesting the presence of geological conditions favorable for hydrocarbon accumulation and mineral deposits. Regions with high-frequency anomalies, characterized by the concentration of short wavelength anomalies, such as the Oban Massif, are associated with dense rock formations like granite, granodiorite, gneiss and migmatite and may contain valuable minerals and rare earth elements. The high-frequency anomalies around Ikot Ekpene, Aba, and Degema areas may be due to high density materials of lateral extent. Overall, the study demonstrates the effectiveness of using Bouguer gravity data in subsurface geological mapping and resource exploration. Blocks D1-D8 are recommended for further investigation for hydrocarbon exploration because they exhibit depths greater than 4000 m.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.289
Teacher spread0.240 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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