Mineral analysis systems, such as the QEMSCAN® (Quantitative Evaluation of Minerals by Scanning electron microscopy) and MLA® (Mineral Liberation Analyzer) both from FEI Company, the assignee of the present invention, have been used for many years to analyze samples from mines to determine the presence of valuable minerals. Such systems direct an electron beam toward the sample and measure the energy of x-rays coming from the material in response to the electron beam for elemental analysis or chemical characterization of a sample. One such process is called “energy dispersive x-ray analysis” or “EDS.”
EDS systems rely on the emission of X-rays from a sample to perform elemental analysis. Each element has a unique atomic structure, which produces x-rays that are characteristic of an element's atomic structure, thereby allowing the element to be uniquely identified by its x-ray spectrum. To stimulate the emission of x-rays from a sample, a beam of charged particles is focused onto the sample, which causes electrons from inner shells to be ejected. Electrons from outer shells drop to the inner shells to fill this electron void, and the difference in energy between the higher energy shell and the lower energy shell is released as an x-ray, which can be measured by an EDS detector.
Backscattered electron (BSE) detectors are also used for mineral analysis in conjunction with electron beam columns. The intensity of the BSE signal is a function of the average atomic number of the material under the electron beam, and this relationship can be used to develop a useful mineral identification method.
The QEMSCAN and MLA both comprise an SEM, one or more EDS detectors, a BSE detector, and software for controlling automated data acquisition and analysis. The QEMSCAN system identifies and quantifies elements within an acquired spectrum. This process is known as “elemental decomposition” because the spectrum is “decomposed” into spectra of individual elements. The elements found in the decomposition may then be compared with “mineral definitions,” which specify criteria that must be met to identify the elements with a particular mineral. A mineral definition may include fixed ranges of the elemental proportions. For example, a QEMSCAN mineral definition may define a sample to be quartz if the EDS analysis shows a silicon proportion of between 45% and 47% and an oxygen proportion of between 52% and 55%.
The widths of the ranges in the mineral definitions are functions not only of variability in the composition of the mineral, but also of the accuracy of the measurement. The widths of the x-ray peaks depend directly on the number of x-rays measured, which defines the quality of the spectrum. A range width used in a mineral definition is determined for a spectrum having a specific number of x-rays; the same width cannot be applied to higher or lower quality spectrum composed of more or fewer x-rays. Thus, it is not possible to define a universal rules database for an arbitrary number of X-ray counts using the QEMSCAN.
This approach becomes increasingly difficult as fewer x-ray counts are used, because the signal-to-noise ratio drops below the point where discrimination between certain elements is possible. For example, Na and Zn have a very similar peak in an x-ray spectrum around 1 keV. Zn has another peak at 8.6 keV. If the concentration of Zn is low, the second x-ray peak becomes indistinguishable from the background, and as a result, identifying whether an x-ray spectrum contains Na or Zn becomes difficult. Because it can be difficult to identify individual elements, the mineral definitions being matched may have to be sufficiently flexible to accommodate elements that are not in the mineral, but that can be misidentified for elements in the mineral.
Moreover, the Qemscan indicates a match as either true or false, without considering how well the unknown spectrum matches a standard spectrum for a mineral. The system picks the first match it finds, even if a better match might be present elsewhere in the mineral database.
MLA, on the other hand, compares a measured mineral spectrum with known mineral spectra from a library and computes a probability match between a measured mineral spectrum and a reference mineral spectrum. This method works well, but the probability value obtained tends to be dominated by the size of the largest peak in the x-ray spectrum.
A BSE detector provides additional information about a mineral and can assist in identification of an unknown sample. The acquisition time of a suitable BSE signal is typically on the order of microseconds per pixel, while EDS systems have a longer acquisition time, typically requiring several seconds per pixel to obtain a spectrum adequate to be differentiated from all other mineral spectra. Unlike a BSE detector, EDS systems are typically insensitive to light atoms. BSE data may be useful for differentiating between minerals composed of the same elements, but in different proportions. For such minerals, the average atomic weight which determines the BSE intensity, will be different, while the elemental spectra will be similar. Because both EDS detectors and BSE detectors each have advantages, it is sometimes useful to use both BSE and x-ray spectra to accurately identify a mineral. Using both signals, however, requires more analysis time, which may not be available in commercial applications.
A mineral classification system should, like the MLA, be capable of comparing each unknown measured spectrum to a library of known mineral spectrums, and then making a selection based on which known mineral is most similar to the measured spectrum.
To compare spectra, the similarity of two spectra is typically reduced to a single number, a similarity metric. The single number quantifies similarity so that it is possible to determine which of two spectra is more similar to a third spectrum. A spectrum may be considered as a histogram showing the number of x-rays detected at various energy ranges, referred to as “channels.” One similarity metric is the sum over the energy channels of the differences between the two normalized spectrums. Another similarity metric is a calculated probability that the unknown mineral is composed of the mineral defined by the mineral definition. The MLA uses a chi-squared probability statistic as a similarity metric to compare the value at each energy channel of the measured spectrum to the value at the corresponding channel of the known mineral spectrum. A problem of using a comparison on a channel-by-channel basis is that there is no guarantee that all required peaks in the mineral spectrum are present in the measured spectrum. It is possible that a measured spectrum appears to be similar to a mineral yet it is missing an element that is required by the definition of that mineral. The measured spectrum may also have an additional element not found in that definition of a mineral.
FIG. 1 provides an example of a prior art mineral comparison spectrum 100 containing an unknown measured spectrum 102 and a defined mineral spectrum 104. In this particular case, the defined mineral spectrum 104 is that of the mineral dolomite. Using a prior art comparison mechanism that calculate a probability directly from the spectrums, the unknown measured spectrum 102 produces a maximum similarity of 97% with the dolomite definition spectrum 104. This 97% similarity would indicate a very close match of the spectrums. However, the unknown spectrum 102 clearly contains an additional element at approximately 175 keV that is not found in dolomite. This misclassification would likely go undetected without significant quality assurance performed by the operator of the software.
Another difficulty with current mineral identification systems is that, many minerals are as non-stoichiometric mixtures. Such minerals may have a range of elemental proportions, rather than always appearing with the same proportion of each element. In the prior art, this was typically ignored, and each mineral defined as having a specific proportion of elements, leading to misidentification of minerals.
Thus, there is a need for an improved mineral identification method.