Geophysical inversion in an integrated exploration program : examples from the San Nicolas deposit
Bibliographic record
Abstract
The recent ability to produce three-dimensional physical property models of the subsurface from surface geophysical data, coupled with an increasing need to explore for minerals in concealed terranes, results in geophysical inversions providing more significant information to the exploration team. This thesis examines the role that geophysical inversion can play in an integrated mineral exploration program, and the impact it can have on the results. As an example, geophysical data from the San Nicolas copper-zinc massive sulphide deposit in Mexico are inverted. Such deposits are distinguished by high density, magnetic susceptibility, conductivity, and chargeability values. Within the framework of an integrated exploration program (which is communicated through the use of a flowchart) many different, and hard-to-interpret, geophysical data sets are given geologic context. This is achieved by interpreting physical property models that have been generated by inversion modeling. The aim of generating these models, and interpreting geological information from them, is to: 1) assist mineral exploration in the area around the deposit, and roughly define areas of mineralization that can be used as a starting point for more detailed modeling ; 2) define the size and location of the San Nicolas deposit; 3) further improve the models and delineate ore more accurately by combining the detailed modeling with core physical property measurements; and 4) determine how best to use the information that has been acquired through modeling to find additional sulphide ore-bodies, including those that may be deeper than the existing deposit. Density and magnetic susceptibility distribution models, inverted from regional gravity and magnetic data respectively, define large-scale structures that reflect the tectonic setting of the region. Several distinct anomalies that exhibit high density and magnetic susceptibility values are identified. Since massive sulphides are often dense and magnetic, a correlation method is employed that determines volumes that have high density and magnetic susceptibility. The correlation procedure isolates five anomalies. Two of these are easily dismissed as having poor exploration potential, and two of the remaining anomalies, one of which is the San Nicolas deposit, are associated with mineralization. At a more detailed scale, the deposit is well defined by gravity, magnetic, CSAMT, and IP methods individually. However, a drill hole that is targeted on the intersection of these favorable physical property distributions would have intersected the heart of the deposit. This demonstrates the advantages of using these methods in concert. Other sources of information, such as core physical property measurements and geologic constraints, are also used to improve modeling results. The inclusion of data from a single drill-hole is shown to significantly enhance detailed physical property distributions, and produces models that correlate better which known mineralization. Finally, forward modeling of the physical property models is used to demonstrate how deep ore can be detected by implementing different survey designs that increase signalto- noise of the data. This newly acquired information can then be used in the next step of the exploration program as the search for mineralization continues i n the surrounding area. In addition to the main focus of the thesis, it was found that polarizable material is needed in order to fit time-domain, airborne electromagnetic data collected over the deposit. This demonstrates the potential for detecting chargeable bodies from the air, and has significant implications for future mineral exploration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".