An innovative and fluid-sensitive prospection method: Deployment of Nodal Ambient Noise Tomography (NANT) in Muara Laboh geothermal system, Sumatra
Bibliographic record
Abstract
Muara Laboh is a geothermal system located in central Sumatra along a sector of the Great Sumatra Fault and neighboring the Kerinci volcano. The area is characterized by diffused surface manifestations, including hot springs and fumarolic activity at various sites along the valley. In this geological context, the fluid migration is expected to be controlled by the lithological and tectonic discontinuities that characterize such a heavily faulted region.To identify subsurface fluid pathways and reservoirs, a large region of nearly 400 km2 was investigated deploying a network of 212 3-components seismic nodes. 182 instruments were placed within a central 25 km area around the populated centre of Muara Laboh, while 6 external antennas (composed of 5 seismic nodes each) were deployed at 40 km SE from the main network to better constrain the depth within the central region. From the seismic records, we extracted cross-correlation functions and Rayleigh wave group-velocity dispersion curves to perform Nodal Ambient Noise Tomography (NANT). We derived a 3D S-wave velocity model that can be used to identify domains characterized by magmatic, tectonic, sedimentary, structural, hydrothermal, and geothermal features. Empirical Green's Functions (EGFs) were derived from ambient noise cross-correlations for Rayleigh waves using standard data processing methods. Dispersion curves were subsequently determined via the Frequency Time Analysis (FTAN) technique. Nonlinear multi-scale inversion was applied to produce group velocity maps for different periods. Finally, a transdimensional Bayesian approach was utilized for depth inversion, resulting in a 3D S-wave velocity model.Our S-wave velocity model highlights the occurrence of fast and slow domains, often marked by sharp variations. Transition zones of intermediate-velocity are located at the shoulders of the low-velocity zones and are potentially interpreted as the boundaries of intrusive bodies displaying high shear wave velocities. One of these domains is located below the main Muara Laboh geothermal system. Other similar areas can be identified from the 3D tomography data and represent ideal targets for geothermal energy harnessing.NANT is a non-invasive and cost-efficient method that provides high-resolution subsurface 3D images of the first 5 km of the upper crust. This approach is ideal to identify subsurface fluid storage and pathways along e.g., seismically active regions where tectonic discontinuities are broadly distributed. These discontinuities are often suggested to control the laterally extensive diffused hydrothermal fluid migration that is targeted for geothermal energy harnessing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".