Anuncio • May 31

Belararox Ltd Announces That Prospectivity Modelling Has Identified 34 New Exploration Targets At the Belara Project (Belara) in NSW

Belararox Ltd. announced that prospectivity modelling has identified 34 new exploration targets at the Belara Project (Belara) in NSW. The modelling has revealed significant resource expansion potential not only in the known historical mine areas but also regionally. Prospectivity modelling at Belara is intended to determine the possibility for near mine resource extensions as well as the potential of the Project to host additional commercial quantities of sulphide zinc - copper mineralisation. The Belara and Native Bee historical mine areas are the first high priority targets for resource drilling, with resource drilling continuing. Recent gravity and gradient array IP data suggest that there is the potential to expand the known resource areas at the Belara and Native Bee mines. Modelling of this data also suggest that there may be significant extensions to mineralisation along strike to the south of the project area and also that there may be sub- parallel zones of mineralisation on new trends to the west and east. Historic drilling and resource estimation work provides known locations of zinc, copper, lead, silver and gold mineralisation that can be used with Machine Learning prospectivity modelling techniques to identify the data that allow the prediction of new deposits of zinc, copper, lead, silver and gold mineralisation in the Belara project area. These techniques also allow historic data like soil geochemistry to be combined with the new geophysical data and other publicly available data, such as satellite imagery, to objectively map exploration targets that have the highest statistical probability of hosting new deposits of zinc, copper, lead, silver and gold mineralization in the Belara project area. Data used in the prospectivity modelling study include soil and rock geochemistry collected by previous explorers between 1982 and 2020, magnetic data collected in 2012, gravity and gradient array IP data collected by the Company in 2022, and recent Sentinel satellite imagery data that were acquired for this study. A total of 137 maps were created from the various data, using filters, structure edge detection and GIS techniques that were spatially statistically tested to measure how well the maps predicted the location of the known deposits of zinc, copper, lead, silver and gold mineralisation in the Belara study area. The area for the prospectivity modelling study included the full area of the 2022 gravity and IP surveys and all other data and derivative maps were clipped to this area. The scale of the study area and the mixing of 2D and 3D datasets required two sets of training data to be used. The first were historic drill intersections of more than 0.6% Zn equivalent, projected to surface. These training points were used to test maps developed from data that include 3D measurements from depth (magnetic, IP, and gravity data). The second set of training points were surface shafts and workings, and the surface projection of the interpreted 3D geometry of the mineralised domains used for the 2007 resource model. These were used to test maps developed from 2D surface data (soil and rock geochemistry, satellite imagery, and radiometric data). The Weights of Evidence modelling technique was used to statistically measure the predictive capacity of the various maps developed from the available geophysical, satellite and geochemical data. Weights of Evidence uses Bayesian statistics to spatially statistically analyse individual maps and combinations of maps, developed from the various datasets, to predict the location of the feature in question. The technique is based on the presence or absence of a characteristic or pattern (e.g., presence of a gravity high) and the occurrence of a training point (e.g., drill intersection). The spatial analysis process allows an objective assessment of a large number of predictive maps, derived from available data, to determine their relevance to the mineral system. Raster maps, for example geophysical grids, were reclassified into various classes and tested using weights of evidence to identify the classes with the best spatial correlation with mineralisation. Raster maps were created from line maps by buffering the lines at 10m intervals. The buffered maps were tested using Weights of Evidence to identify the buffer interval with the best statistical correlation with the training points. Historic soil and rock chip geochemical sampling was at sufficient density to create grid maps for each element of interest. All maps were then reclassified into binary maps, with the area covered by the classes or buffer intervals that gave the best statistical correlation as `feature present', the area outside this as `feature absent', and areas where measurements were not taken as `no data'. For the geochemical maps, anomalous values were defined, and binary maps produced that mapped areas above the anomalous value, areas below the anomalous value, and areas of no data where no sampling was done. The spatial statistics were used to select the best maps to use in the prospectivity models. Criteria for map selection were strong spatial correlation with the training points (high C-value measured in Weights of Evidence analysis), strong confidence in the results (high Studentised C-value measured in Weights of Evidence analysis), minimised repetition of map patterns (for example not using multiple similar maps from magnetic data), and maps that did not excessively restrict the prospective area. Maps selected for the surface model were: Soil Zinc anomaly highs; Sentinel satellite data Band 09 lows; Structure detection lines from a topographic grid buffered to 50 m; Total count radiometric highs. Maps selected for the depth model were: IP Metal Factor highs; Magnetic highs; Chargeability highs; Gravity highs from a 0.5 vertical derivative filter. Two prospectivity models were produced. One using the surface maps and surface training points, and one using the training points from depth and the maps representing data from depth. Using the input parameters of the study area, unit cell, and number of training points, a prospectivity probability value was calculated for each 10m grid cell over the study area. The prior probability represents the chance of randomly finding a deposit within the study area before any additional evidence for mineralisation is applied. The aim of Weights of Evidence modelling is to add evidence to increase or decrease the prior probability of each grid cell in the study area. The surface and depth prospectivity model maps were then combined using the fuzzy logic `or' function, which preserves the highest potential areas from both models, to create the final prospectivity model for the Belara study area.

Porcentaje del salario del CEO	n/a
Permanencia del CEO	less than a year
Participación del CEO	n/a
Permanencia media de la dirección	2.4yrs
Promedio de permanencia en la Junta Directiva	1.1yrs

Nombre	Posición	Permanencia	Compensación	Propiedad
Graeme Morissey	Chief Financial Officer	2.4yrs	AU$168.13k	0.025% A$ 3.3k
William Dix	CEO & Director	less than a year	sin datos	sin datos
Sherrie Baker	Executive Assistant & Administration Manager	4.3yrs	sin datos	sin datos
Chris Blaser	General Manager of Exploration	1.3yrs	sin datos	sin datos
Benjamin Donovan	Company Secretary	2.4yrs	sin datos	sin datos

Nombre	Posición	Permanencia	Compensación	Propiedad
William Dix	CEO & Director	less than a year	sin datos	sin datos
Christopher Gale	Non-Executive Director	less than a year	sin datos	0.42% A$ 55.6k
Neil Warburton	Independent Non-Executive Chairman	4.9yrs	AU$140.81k	1.56% A$ 206.9k
John Traicos	Non-Executive Director	3.9yrs	AU$142.91k	0.13% A$ 17.4k
Tim Zuo	Non-Executive Director	1.1yrs	AU$10.00k	sin datos

Datos	Última actualización (huso horario UTC)
Análisis de la empresa	2026/05/22 05:32
Precio de las acciones al final del día	2026/05/22 00:00
Beneficios	2025/12/31
Ingresos anuales	2025/06/30

Paquete	Datos	Marco temporal	Ejemplo Fuente EE.UU. ^*
Finanzas de la empresa	10 años	Cuenta de resultados Estado de tesorería Balance	Formulario SEC 10-K Formulario SEC 10-Q
Estimaciones del consenso de analistas	+3 años	Previsiones financieras Objetivos de precios de los analistas	Informes de análisis Blue Matrix
Precios de mercado	30 años	Precios de las acciones Dividendos, escisiones y acciones	Datos de mercado ICE Formulario S-1 de la SEC
Propiedad	10 años	Accionistas principales Información privilegiada	Formulario SEC 4 Formulario SEC 13D
Gestión	10 años	Equipo directivo Consejo de Administración	Formulario SEC 10-K Formulario SEC DEF 14A
Principales avances	10 años	Anuncios de empresas	Formulario SEC 8-K

Belararox Dirección

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