Effects of fracture lineaments and in-situ rock stresses on groundwater flow in hard rocks: a case study from Sunnfjord, western Norway

Systematic field mapping of fracture lineaments observed on aerial photographs shows that almost all of these structures are positively correlated with zones of high macroscopic and mesoscopic fracture frequencies compared with the surroundings. The lineaments are subdivided into zones with different characteristics: (1) a central zone with fault rocks, high fracture frequency and connectivity but commonly with mineral sealed fractures, and (2) a damage zone divided into a proximal zone with a high fracture frequency of lineament parallel, non-mineralized and interconnected fractures, grading into a distal zone with lower fracture frequencies and which is transitional to the surrounding areas with general background fracturing. To examine the possible relations between lineament architecture and in-situ rock stress on groundwater flow, the geological fieldwork was followed up by in-situ stress measurements and test boreholes at selected sites. Geophysical well logging added valuable information about fracture distribution and fracture flow at depths. Based on the studies of in-situ stresses as well as the lineaments and associated fracture systems presented above, two working hypotheses for groundwater flow were formulated: (i) In areas with a general background fracturing and in the distal zone of lineaments, groundwater flow will mainly occur along fractures parallel with the largest in-situ rock stress, unless fractures are critically loaded or reactivated as shear fractures at angles around 30° to σ_H; (ii) In the influence area of lineaments, the largest potential for groundwater abstraction is in the proximal zone, where there is a high fracture frequency and connectivity with negligible fracture fillings. The testing of the two hypotheses does not give a clear and unequivocal answer in support of the two assumptions about groundwater flow in the study area. But most of the observed data are in agreement with the predictions from the models, and can be explained by the action of the present stress field on pre-existing fractures.     

Weights of Evidence Modeling of Mineral Potential: A Case Study Using Small Number of Prospects,Abra, Philippines

Weights of evidence (WofE) modeling usually is applied to map mineral potential in areas with large number of deposits/prospects. In this paper, WofE modeling is applied to a case study area measuring about 920 km² with 12 known porphyry copper prospects. A pixel size of 100 m × 100 m was used in the spatial data analyses to represent in a raster-based GIS lateral extents of prospects and of geological features considered as spatial evidence. Predictor maps were created based on (a) estimates of studentized values of positive spatial association between prospects and spatial evidence; (b) proportion of number of prospects in zones where spatial evidence is present; and (c) geological interpretations of positive spatial association between prospects and spatial evidence. Uncertainty because of missing geochemical evidence is shown to have an influence on tests of assumption of conditional independence (CI) among predictor maps with respect to prospects. For the final predictive model, assumption of CI is rejected based on omnibus test but is accepted based on a new omnibus test. The final predictive model, which delineates 30% of study area as zones with potential for porphyry copper, has 83% success rate and 73% prediction rate. The results demonstrate plausibility of WofE modeling of mineral potential in large areas with small number of mineral prospects.     

Data-Driven Predictive Modeling of Mineral Prospectivity Using Random Forests: A Case Study in Catanduanes Island (Philippines)

The Random Forests (RF) algorithm is a machine learning method that has recently been demonstrated as a viable technique for data-driven predictive modeling of mineral prospectivity, and thus, it is instructive to further examine its usefulness in this particular field. A case study was carried out using data from Catanduanes Island (Philippines) to investigate further (a) if RF modeling can be used for data-driven modeling of mineral prospectivity in areas with few (i.e., <20) mineral occurrences and (b) if RF modeling can handle predictor variables with missing values. We found that RF modeling outperforms evidential belief (EB) modeling of prospectivity for hydrothermal Au–Cu deposits in Catanduanes Island, where 17 hydrothermal Au–Cu prospects are known to exist. Moreover, just like EB modeling, RF modeling allows analysis of the spatial relationships between known prospects and individual layers of predictor data. Furthermore, RF modeling can handle missing values in predictor data through an RF-based imputation technique whereas in EB modeling, missing values are simply represented by maximum uncertainty. Therefore, the RF algorithm is a potentially useful method for data-driven predictive modeling of mineral prospectivity in regions with few (i.e., <20) occurrences of mineral deposits of the type sought. However, further testing of the method in other regions with few mineral occurrences is warranted to fully determine its usefulness in data-driven predictive modeling of mineral prospectivity.     

Application of Discriminant Analysis and Support Vector Machine in Mapping Gold Potential Areas for Further Drilling in the Sari-Gunay Gold Deposit,NW Iran

In this contribution, we used discriminant analysis (DA) and support vector machine (SVM) to model subsurface gold mineralization by using a combination of the surface soil geochemical anomalies and earlier bore data for further drilling at the Sari-Gunay gold deposit, NW Iran. Seventy percent of the data were used as the training data and the remaining 30 % were used as the testing data. Sum of the block grades, obtained by kriging, above the cutoff grade (0.5 g/t) was multiplied by the thickness of the blocks and used as productivity index (PI). Then, the PI variable was classified into three classes of background, medium, and high by using fractal method. Four classification functions of SVM and DA methods were calculated by the training soil geochemical data. Also, by using all the geochemical data and classification functions, the general extension of the gold mineralized zones was predicted. The mineral prediction models at the Sari-Gunay hill were used to locate high and moderate potential areas for further infill systematic and reconnaissance drilling, respectively. These models at Agh-Dagh hill and the area between Sari-Gunay and Agh-Dagh hills were used to define the moderate and high potential areas for further reconnaissance drilling. The results showed that the nu-SVM method with 73.8 % accuracy and c-SVM with 72.3 % accuracy worked better than DA methods.     

Groundwater metering: revisiting a ubiquitous ‘best practice’

Hydrogeology Journal - It is often taken for granted that metering is an unarguable ‘best practice’ when it comes to the quantitative management of groundwater resources. This paper...     

Co-simulated Size Number: An Elegant Novel Algorithm for Identification of Multivariate Geochemical Anomalies

Natural Resources Research - Identification of geochemical anomalies is of particular importance for tracing the footprints of anomalies. This can be implemented by advanced techniques of...     

3D Mineral Prospectivity Mapping with Random Forests: A Case Study of Tongling,Anhui, China

Natural Resources Research - In the past few decades, a variety of data-driven predictive modeling techniques has led to a dramatic advancement in mineral prospectivity mapping (MPM). The random...     

Extensional fault and fold growth: Impact on accommodation evolution and sedimentary infill

Extensional faults and folds exert a fundamental control on the location, thickness and partitioning of sedimentary deposits on rift basins. The connection between the mode of extensional fault reactivation, resulting fault shape and extensional fold growth is well‐established. The impact of folding on accommodation evolution and growth package architecture, however, has received little attention; particularly the role‐played by fault‐perpendicular (transverse) folding. We study a multiphase rift basin with km‐scale fault displacements using a large high‐quality 3D seismic data set from the Fingerdjupet Subbasin in the southwestern Barents Sea. We link growth package architecture to timing and mode of fault reactivation. Dip linkage of deep and shallow fault segments resulted in ramp‐flat‐ramp fault geometry, above which fault‐parallel fault‐bend folds developed. The folds limited the accommodation near their causal faults, leading to deposition within a fault‐bend synclinal growth basin further into the hangingwall. Continued fold growth led to truncation of strata near the crest of the fault‐bend anticline before shortcut faulting bypassed the ramp‐flat‐ramp structure and ended folding. Accommodation along the fault‐parallel axis is controlled by the transverse folds, the location and size of which depends on the degree of linkage in the fault network and the accumulated displacement on causal faults. We construct transverse fold trajectories by tracing transverse fold hinges through space and time to highlight the positions of maximum and minimum accommodation and potential sediment entry points to hangingwall growth basins. The length and shape of the constructed trajectories relate to the displacement on their parent faults, duration of fault activity, timing of transverse basin infill, fault linkage and strain localization. We emphasize that the considerable wavelength, amplitudes and potential periclinal geometry of extensional folds make them viable targets for CO₂ storage or hydrocarbon exploration in rift basins.     

Compositional Balance Analysis: An Elegant Method of Geochemical Pattern Recognition and Anomaly Mapping for Mineral Exploration

Natural Resources Research - Geochemical pattern recognition and anomaly mapping are always involved in the fields of environmental and exploration geochemistry. Principal component analysis (PCA)...