Aspects of Estimation and Reporting of Mineral Resources of Seabed Polymetallic Nodules: A Contemporaneous Case Study

Automated estimation of offshore polymetallic nodule abundance based on seafloor imagery using deep learning

The burgeoning demand for critical metals used in high-tech and green technology industries has turned attention toward the vast resources of polymetallic nodules on the ocean floor. Traditional methods for estimating the abundance of these nodules, such as direct sampling or acoustic imagery are time and labour-intensive or often insufficient for large-scale or accurate assessment. This paper advocates for the automatization of polymetallic nodules detection and abundance estimation using deep learning algorithms applied to seabed photographs. We propose UNET convolutional neural network framework specifically trained to process the unique features of seabed imagery, which can reliably detect and estimate the abundance of polymetallic nodules based on thousands of seabed photographs in significantly reduced time (below 10 h for 30 thousand photographs). Our approach addresses the challenges of data preparation, variable image quality, coverage-abundance transition model and sediments presence. We indicated the utilization of this approach can substantially increase the efficiency and accuracy of resource estimation, dramatically reducing the time and cost currently required for manual assessment. Furthermore, we discuss the potential of this method to be integrated into large-scale systems for sustainable exploitation of these undersea resources.

Exploration of Polymetallic Nodules and Resource Assessment: A Case Study from the German Contract Area in the Clarion-Clipperton Zone of the Tropical Northeast Pacific

In 2006, the BGR signed a contract with the International Seabed Authority (ISA) for the exploration of polymetallic nodules in the Clarion-Clipperton Zone of the northeast Pacific. During nine expeditions, in particular, “Area E1”, the eastern part of the contract area, with a size of ~60,000 km2, was explored in detail. Here, we outline BGR’s exploration methods and provide resource estimates for Area E1 and three sub-areas. The resource assessment is predominantly based on statistical analyses of data obtained by 12-kHz multibeam bathymetry and backscatter mapping, box core sampling and geochemical analysis of nodules. The main parameter for the assessment is the nodule abundance (kg/m2), as its coefficient of variation (CoV) over the entire eastern contract area is relatively high at 36%. In contrast, the metal contents of nodules show only minor variation, with a CoV of 8% for manganese and 8% for the sum of copper, nickel and cobalt. To estimate mineral resources for the entire Area E1, we used an artificial neural network approach with a multivariate statistical correlation between nodule abundance derived from box cores and hydro-acoustic data. The total estimated resources are 540 ± 189 million tonnes (Mt) of dry nodules, and the total estimated metal contents are 168 Mt of manganese, 7.5 Mt of nickel, 6.3 Mt of copper, 0.9 Mt of cobalt, 0.4 Mt of rare-earth elements and 0.3 Mt of molybdenum. A geostatistical resource estimate of three economically prospective areas with a total size of 4498 km2, intensively sampled by box cores, was carried out using ordinary kriging of nodule abundance and metal grades. Within these three nodule fields, 7.14 Mt of dry nodules are classified as measured mineral resources covering an area of 489 km2. Indicated mineral resources amount to 11.2 Mt, covering an area of 825 km2, and inferred mineral resources of 35.5 Mt of dry nodules were estimated for an area of 3184 km2. In total, the metal contents of the three prospective areas amount to 16.8 Mt of manganese, 0.74 Mt of nickel, 0.63 Mt of copper and 0.09 Mt of cobalt.

Empirical Application of Generalized Rayleigh Distribution for Mineral Resource Estimation of Seabed Polymetallic Nodules

An efficient empirical statistical method is developed to improve the process of mineral resource estimation of seabed polymetallic nodules and is applied to analyze the abundance of seabed polymetallic nodules in the Clarion Clipperton Zone (CCZ). The newly proposed method is based on three hypotheses as the foundation for a model of “idealized nodules”, which was validated by analyzing nodule samples collected from the seabed within the Tonga Offshore Mining Limited (TOML) exploration contract. Once validated, the “idealized nodule” model was used to deduce a set of empirical formulae for predicting the nodule resources, in terms of percentage coverage and abundance. The formulae were then applied to analyzing a total of 188 sets of nodule samples collected across the TOML areas, comprising box-core samples and towed camera images as well as other detailed box-core sample measurements from the literature. Numerical results for nodule abundance and coverage predictions were compared with field measurements, and unbiased agreement has been reached. The new method has the potential to achieve more accurate mineral resource estimation with reduced sample numbers and sizes. They may also have application in improving the efficiency of design and configuration of mining equipment.

Application of General Linear Models (GLM) to Assess Nodule Abundance Based on a Photographic Survey (Case Study from IOM Area, Pacific Ocean)

The success of the future exploitation of the Pacific polymetallic nodule deposits depends on an accurate estimation of their resources, especially in small batches, scheduled for extraction in the short term. The estimation based only on the results of direct seafloor sampling using box corers is burdened with a large error due to the long sampling interval and high variability of the nodule abundance. Therefore, estimations should take into account the results of bottom photograph analyses performed systematically and in large numbers along the course of a research vessel. For photographs taken at the direct sampling sites, the relationship linking the nodule abundance with the independent variables (the percentage of seafloor nodule coverage, the genetic types of nodules in the context of their fraction distribution, and the degree of sediment coverage of nodules) was determined using the general linear model (GLM). Compared to the estimates obtained with a simple linear model linking this parameter only with the seafloor nodule coverage, a significant decrease in the standard prediction error, from 4.2 to 2.5 kg/m2, was found. The use of the GLM for the assessment of nodule abundance in individual sites covered by bottom photographs, outside of direct sampling sites, should contribute to a significant increase in the accuracy of the estimation of nodule resources.