Influence of layering on vein systematics in line samples

Gillespie, Paul; Johnston, Joseph; Loriga, M. A.; McCaffrey, Ken; Walsh, John J.; Watterson, J.

doi:10.1144/gsl.sp.1999.155.01.05

Cited by 86 publications

(100 citation statements)

References 49 publications

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“…The average separation between samples (nearest neighbour distance) has been analysed in terms of the Coefficient of Variation (CV), defined as the ratio between the standard deviation and the mean of the sampled population (Gillespie et al, 1999). A value of CV > 1 results from the clustering of samples, when CV = 1 a random or Poisson distribution of samples is evident, and CV b 1 is the result of anti-clustering (a homogeneous distribution) of samples.…”

Section: Spatial Distribution Analysismentioning

confidence: 99%

Spatial relationship between earthquakes and volcanic vents in the central-northern Main Ethiopian Rift

Mazzarini

Keir

Isola

2013

Journal of Volcanology and Geothermal Research

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During the breakup of continents extension is commonly accommodated by connected networks of fluid filled fractures (dykes) and by faults. Despite the importance of these two extension mechanisms their spatial relationship in three dimensions is poorly understood primarily because it is difficult to quantify the subsurface distribution of faulting and intrusion. In order to address this problem, we conduct a quantitative analysis of the spatial distribution and clustering of earthquakes and volcanic vents in the Main Ethiopian Rift in East Africa in order to understand how extension by faulting and intrusion is distributed throughout the volcanic rift. We use fractal analysis of earthquake epicentres in order to infer the 2D characteristics of the subsurface fault network, and directly test our model results against the 3D distribution of earthquake hypocentres. Our results show that fractal analysis of these features is a reliable means to characterise the 3D properties of the fault network. In addition, the strong similarity between the properties of the fault network derived from earthquakes and properties of the magma-filled fracture network derived from fractal analysis of volcanic vents strongly suggests that these are genetically linked. We then explore their spatial link using computation of earthquake and vent density, which shows that the zone of seismicity is generally around 20-30-km-wide, while the zone of vents is narrower and centred within the zone of seismicity. This spatial relationship suggests that the faults, which form rift axial grabens, are induced above a narrower and central zone of diking. We also demonstrate significant along-rift variation in degree of magmatism and faulting with regions of increased degree of diking inferred from a higher cone density characterised by reduced degree of faulting.

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Section: Spatial Distribution Analysismentioning

confidence: 99%

Spatial relationship between earthquakes and volcanic vents in the central-northern Main Ethiopian Rift

Mazzarini

Keir

Isola

2013

Journal of Volcanology and Geothermal Research

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“…56, no. 1, 2006 Statistical and Johnston, 1996; Gillespie et al, 1999;Loriga, 1999;Roberts et al, 1999;Brathwaite et al, 2001), cumulative frequencies of vein thickness obey the power-law distributions. They indicate a positive feedback process (Manning, 1994;Clark et al, 1995).…”

Section: Stage Of Vein Developmentmentioning

confidence: 95%

“…The cores examined were of the non-oriented 1-D line samples, and the measurement in this study was limited to the vein position and width (i.e., true thickness of Gillespie et al, 1999). The followings were cautioned in the measurement: i) fine veins forming networks were separated individually as possible, ii) rock fragments (breccias) in veins were deducted from the vein width, iii) quartz occurring as lenses (of <0.067 m in width) along layers in shale of the sedimentary rocks, which frequently appears like veins, was deducted as possible, iv) vein position was expressed as a position where the core axis crossed the central plane of a vein, v) the alteration zones (argillized and silicified) were not treated as veins, and vi) lacked portions of cores due to previous researches were partly complemented by a report of MITI (1982), in which the veins more than 1 to 10 cm in apparent or true thickness with high gold contents were described.…”

Section: Samples and Measurement Methodsmentioning

confidence: 99%

“…In this analysis, the vein spacing including the lacked portions of cores with long intervals was excluded. Coefficients of variation (Cv) of vein spacing, which are defined as the standard deviation divided by the mean, corresponds to the clustered, random and regular distributions of veins at Cv>1, Cv=1 and Cv=0, respectively (Gillespie et al, 1999). The values for the samples studied were between 1.2 and 2.5 (Fig.…”

Section: Size-frequency Distributionsmentioning

confidence: 99%

“…Physico-chemical environments of the fracture systems (i.e., tectonic setting, host rock type, temperature and pressure) affect their evolution (e.g., Paterson, 1978;Scholz, 1990). Statistical features of fracture systems (e.g., cumulative profile, size-frequency distribution and fractal size: e.g., Turcotte, 1997;Bonnet et al, 2001) are useful for elucidating the timing during their evolution (e.g., Rives et al, 1992;Cowie et al, 1995;Ackermann et al, 2001) and their physico-chemical environments (e.g., Velde et al, 1993;Gillespie et al, 1999;Brathwaite et al, 2001). Vein systems, which are of the fracture systems filled by minerals at their formation stages, could be argued by the same manner.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Statistical Features of Vein Systems in the Hishikari Epithermal Gold Deposit, Japan

Sasaki¹

2006

Resource Geology

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Abstract:The Hishikari epithermal gold deposit consists of parallel vertical veins hosted by the Quaternary volcanic rocks (QVR) and Cretaceous sedimentary rocks (CSR) at the shallow and deep levels, respectively. In order to reveal the lithological influence on the evolution of vein systems in the Hishikari field, vein density, width, spacing and position were statistically examined using cores sampled from three boreholes.The statistical features of the veins identified were summarized as follows. The feature (i) indicates higher ductility of the QVR against fracturing than the CSR. The feature (ii) implies that large deformation was accumulated in rocks themselves (e.g., alteration zones) in addition to the veins examined in the QVR compared to only the veins in the CSR. The feature (iv) suggests that the vein systems in the CSR reached a stage at which the several selected large veins grew as main conduit, that potentially provided pathways for ore-forming fluids to ascend from deep levels. The coefficients of variation of vein spacing and fractal dimensions of vein position (features v and vi) indicate clustered distribution of the veins in the field.This study might be helpful for a better understanding of lithological influence on the evolution of vein systems not only in the Hishikari field but also in other fields.

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A technique for revealing scale‐dependent patterns in fracture spacing data

et al. 2014

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Data on fracture spacing along scan lines have been widely analyzed for the purposes of characterization. Most of these studies, however, either consider the cumulative frequency of spacing data without regard to the actual sequence of the spacing values or compute an average spacing that may not work for clustered fractures. The coefficient of variation parameter is often used to differentiate between clustered, random, and anticlustered fractures in a scan line but does not address the issue of scale-dependent variations in spacing. Lacunarity is a parameter that has been previously used for delineating scale-dependent clustering in fracture networks with similar fractal dimensions. This technique has the further capability of identifying scales at which different patterns emerge within the same data set. Lacunarity can also delineate possible fractal behavior. This paper tests the ability of lacunarity to find patterns (fractal/uniform/random) within synthetic and natural fracture clusters. A set of four model scan lines (uniformly spaced fractures, periodically spaced fracture clusters, fractal fracture clusters, and random fractures) was considered. The first derivative of the lacunarity curves of these models was used to find the intercluster distance and organization of fractures within the clusters. The same technique was then applied to a set of two natural fracture scan line data, one with fracture clusters with fractal organization within and the other with randomly spaced fractures. It was found that the proposed technique could discriminate between the random and clustered patterns, find the intercluster distance, and identify the type of spatial organization within the clusters.

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Influence of layering on vein systematics in line samples

Cited by 86 publications

References 49 publications

Spatial relationship between earthquakes and volcanic vents in the central-northern Main Ethiopian Rift

Spatial relationship between earthquakes and volcanic vents in the central-northern Main Ethiopian Rift

Statistical Features of Vein Systems in the Hishikari Epithermal Gold Deposit, Japan

A technique for revealing scale‐dependent patterns in fracture spacing data

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