Quantitative analysis method and system for rare earth elements of geological rock based on laser-induced breakdown spectroscopy

Abstract

The invention discloses a geological rock rare earth element quantitative analysis method and system based on laser-induced breakdown spectroscopy, and belongs to the technical field of element spectral analysis, and the geological rock rare earth element quantitative analysis method comprises the following steps: constructing a dual-acquisition system to obtain a spectral signal and a plasma image of a rock sample, and preprocessing the spectral signal and the plasma image; determining rock lithology through a classification model, calculating plasma temperature based on the spectral signal, calculating brightness based on the plasma image, and correcting the plasma temperature; and constructing a multi-category bimodal quantitative analysis model, and inputting the lithology, the corrected temperature and the preprocessed image to complete quantitative analysis. The system comprises a data acquisition subsystem and a quantitative analysis subsystem, and data acquisition time sequence consistency and analysis reliability are guaranteed. According to the method, a bimodal data architecture is adopted, temperature correction and an advanced feature fusion network are combined, the limitation of single spectral data is broken through, the analysis precision and stability are improved, and popularization and application in geological exploration scenes are facilitated.

Description

Technical Field

[0001] This invention relates to the field of elemental spectral analysis technology, and more specifically to a method and system for quantitative analysis of rare earth elements in geological rocks based on laser-induced breakdown spectroscopy. Background Technology

[0002] Rare earth elements, as strategically critical mineral resources, play an irreplaceable role in new energy, high-end manufacturing, and other fields. Accurate exploration and elemental content analysis of rare earth elements in rocks are crucial for resource development and utilization. Laser-induced breakdown spectroscopy (LIBS) is a technique that uses high-energy laser pulses to ablate the sample surface to generate plasma, and then analyzes the plasma emission spectrum to determine the sample's composition and content. It has advantages such as speed, non-contact nature, and ease of sample preparation, and has become an important technical direction in rock elemental analysis.

[0003] However, existing LIBS technology faces significant bottlenecks in rare earth element (REE) analysis of geological samples and rocks: First, REEs are present in low concentrations and have weak characteristic signals in rocks. Furthermore, matrix effects lead to poor spectral signal-to-noise ratios and difficulty in detection. Using a single signal enhancement technique may not yield ideal spectral signals suitable for REE analysis. Second, the matrix elements in geological rock samples are mostly non-metallic elements, such as Si and O, which are difficult to excite and suppress trace REE signals. Moreover, the major element composition varies across different lithologies, resulting in different REE signal suppression procedures. Current spectral correction relies on traditional internal standard methods or matrix correction, without incorporating plasma image characteristics and lacking customized correction strategies for different rock types, making it difficult to adapt to matrix differences across lithologies. Third, commonly used quantitative analysis models in LIBS lack sufficient accuracy for quantifying low-concentration REEs and fail to consider the real-time state of plasma excitation and the characteristics of geological rock samples. This results in quantitative results that cannot be adopted as accurate values ​​for geological analysis or REE exploration in practical applications. These issues make it difficult for existing technologies to meet the high-sensitivity and high-accuracy quantitative analysis requirements of rare earth elements in geological rocks, thus limiting the large-scale application of LIBS technology in rare earth resource exploration.

[0004] Therefore, how to provide a method and system for the quantitative analysis of rare earth elements in geological rocks with high sensitivity and high accuracy is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a method and system for quantitative analysis of rare earth elements in geological rocks based on laser-induced breakdown spectroscopy. By combining dual-modal data of laser-induced breakdown spectroscopy and plasma image, and through preprocessing, lithology identification, temperature calculation and correction, and construction of multi-category dual-modal quantitative models, the present invention achieves accurate quantitative analysis of rare earth elements and improves the accuracy and reliability of the analysis.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: On the one hand, this invention provides a method for quantitative analysis of rare earth elements in geological rocks based on laser-induced breakdown spectroscopy, comprising: A laser breakdown spectral signal acquisition system and a plasma image acquisition system were constructed, and spectral signals and plasma images of geological rock samples were acquired based on the laser breakdown spectral signal acquisition system and the plasma image acquisition system, respectively. Preprocessing of spectral signals and plasma images; The preprocessed spectral signal is input into the trained classification model to determine the lithology of the geological rock sample; Calculate plasma temperature based on preprocessed spectral signals; Calculate plasma image brightness based on preprocessed plasma image; Spectral correction of plasma temperature based on plasma brightness; A multi-category dual-modal quantitative analysis model was constructed. Rock lithology and spectrally corrected plasma temperature and preprocessed plasma image were input into the multi-category dual-modal quantitative analysis model for quantitative analysis, and the results of rare earth element quantitative analysis of geological rock samples were obtained.

[0007] Preferably, calculating the plasma temperature based on the preprocessed spectral signal includes: For a set of energy levels, the spectral line intensity produced by the transition The following relationship must be satisfied:

[0008] Where λ is the spectral line wavelength. For the transition probability, For energy level statistical weights, To stimulate energy, Boltzmann's constant, The plasma temperature, For particle number density, For the experimental parameters of the instrument; if we let:

[0009] Spectral line intensity The relation can be simplified to:

[0010] R non-self-absorption spectral lines of the same element were selected, and spectral parameters were obtained from the NIST atomic spectral database. The preprocessed spectral signals were then used to... The vertical axis is , A linear fit is performed on the x-axis to calculate the temperature. : .

[0011] Preferably, calculating the plasma image brightness based on the preprocessed plasma image includes: The preprocessed plasma image was divided into M parts according to brightness. The outermost part, representing the experimental environment, was not included in the brightness calculation. The brightness of each of the remaining parts was calculated separately. and data volume The total amount of data included in the calculation is:

[0012] The final plasma image brightness L is: .

[0013] Preferably, spectral correction of plasma temperature based on plasma brightness includes: Based on spectral line intensity When calculating plasma temperature using the relational formula, the spectral intensity can be expressed as:

[0014] By establishing the relationship between plasma temperature and plasma image, the plasma temperature calculated by spectroscopy is corrected to obtain the corrected plasma temperature. Substitute the corrected plasma temperature into the formula Obtain the corrected ; The value of q obtained during plasma temperature calculation is denoted as... The formula for calculating spectral intensity and the design of the correction function F are:

[0015] Corrected spectral signal for: .

[0016] The preferred quantitative analysis model for multi-category dual-modality analysis is as follows: Spectral feature extraction uses a 2-layer LSTM+Attention network, while image feature extraction uses a combination of a 3-layer Conv2d and a 6-layer Transformer encoder. Deep feature fusion is achieved through a modality adaptive dynamic fusion network, and then the Transformer encoder is used to perform quantitative analysis of rare earth elements after the fusion layer.

[0017] On the other hand, the present invention also provides a quantitative analysis system for rare earth elements in geological rocks based on laser-induced breakdown spectroscopy, comprising: a data acquisition subsystem and a quantitative analysis subsystem; The data acquisition subsystem includes a pulse generator, a laser source, a spectrometer, a camera sample stage, an argon gas module, and a synchronization control module. The laser source is an Nd:YAG laser; the argon gas module provides an argon gas environment; the synchronization control module, the spectrometer, and the pulse generator are connected in sequence, and the laser and the camera are respectively connected to the pulse generator. The synchronization control module controls the synchronous triggering of the laser source and the camera, and the spectrometer and the camera synchronously acquire the spectral signals and plasma images of the sample. The quantitative analysis subsystem includes: The preprocessing module is used to preprocess spectral signals and plasma images; The spectral feature extraction module is used to input the preprocessed spectral signal into the trained classification model to determine the lithology of the geological rock sample, and to calculate the plasma temperature based on the preprocessed spectral signal. The plasma feature extraction module is used to calculate the plasma image brightness based on the preprocessed plasma image; The calibration module is used to perform spectral correction of plasma temperature based on plasma brightness; The quantitative analysis module is used to construct a multi-category dual-modal quantitative analysis model. The rock lithology and spectrally corrected plasma temperature and preprocessed plasma image are input into the multi-category dual-modal quantitative analysis model for quantitative analysis to obtain the quantitative analysis results of rare earth elements in geological rock samples.

[0018] As can be seen from the above technical solution, compared with the prior art, this invention discloses a method and system for quantitative analysis of rare earth elements in geological rocks based on laser-induced breakdown spectroscopy. It employs a dual-modal data acquisition and analysis architecture of laser breakdown spectral signals and plasma images, overcoming the limitations of single-spectral data being susceptible to interference and enriching the characteristic dimensions of rare earth elements in rocks. Correction of plasma temperature calculated from the spectrum based on plasma image brightness effectively reduces errors caused by experimental environment and instrument parameters, improving the accuracy and stability of the spectral signal. Furthermore, this invention first determines the rock lithology through a classification model, and then combines the corrected plasma temperature and image features to construct a multi-category dual-modal quantitative analysis model, achieving accurate quantitative analysis of rare earth elements in geological rocks of different lithologies. Simultaneously, spectral feature extraction uses a combination of LSTM+Attention network, and image feature extraction uses a Conv2d+Transformer encoder, coupled with a modal adaptive dynamic fusion network, which can deeply mine the correlation features of dual-modal data, further improving the accuracy and reliability of quantitative analysis. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the process provided by the present invention.

[0021] Figure 2 This is a schematic diagram of the data acquisition subsystem provided by the present invention.

[0022] Figure 3 This is a schematic diagram of the structure of the quantitative analysis subsystem provided by the present invention.

[0023] Figure 4 This is a spectral image of a geological rock sample.

[0024] Figure 5 Plasma images of geological rock samples.

[0025] Figure 6 This is a comparison of rare earth element spectral peaks before and after spectral correction.

[0026] Figure 7 This is a schematic diagram of a multi-class dual-modal fusion neural network.

[0027] In the figure, 1. First laser, 2. Second laser, 3. ICCD camera, 4. Reflector, 5. PS beam splitter, 6. Pulse generator, 7. Collector, 8. Fiber optic, 9. Spectrometer, 10. Computer, 11. Gas nozzle, 12. Gas tube, 13. Argon cylinder, 14. Sample. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] This invention discloses a method for quantitative analysis of rare earth elements in geological rocks based on laser-induced breakdown spectroscopy, such as... Figure 1 As shown, it includes: A laser breakdown spectral signal acquisition system and a plasma image acquisition system were constructed. Based on these systems, spectral signals and plasma images of geological rock samples were acquired. The acquired spectra and plasma images are shown below. Figure 4 and Figure 5 As shown. In the specific implementation process, sample preparation is also required: select at least 3 types of typical rock standard samples, process them into circular pieces with a diameter of 20 mm and a thickness of 5 mm, and contain different contents of rare earth elements such as La, Ce, and Nd; Before data acquisition, system debugging and system parameter optimization experiments were conducted. System debugging included connecting the Nd:YAG laser to the spectrometer and high-speed camera via a synchronous controller, and fixing the argon gas conduit 5 mm above the sample surface at a 45° angle to ensure full coverage of the plasma region. Parameter optimization experiments included designing a collinearity experiment, measuring each set of parameters 10 times, calculating the SNR, intensity, and LOD of the La II 394.91 nm and Ce II 413.76 nm spectral lines, and fitting the optimal parameter combination using the response surface methodology: a double-pulse delay time of 4 μs, two laser energies of 70 mJ and 80 mJ, an argon gas flow rate of 25 L / min, and an integration time of 100 ms.

[0030] Preprocessing of spectral signals and plasma images is performed. The spectral preprocessing workflow includes baseline correction, Savitzky-Golay smoothing (11-point window), and maximum / minimum normalization to suppress background noise from spectral acquisition. Plasma image preprocessing includes median filtering and Gaussian filtering for noise reduction.

[0031] The preprocessed spectral signal is input into a trained classification model to determine the lithology of the geological rock sample; the classification model training process includes: Data acquisition: Collect rock samples from different regions / lithologies (no less than 20 samples of each type), acquire enhanced spectra using optimal parameters, 50 samples per sample, for a total of 1000 spectral samples; Preprocessing and feature extraction: Spectral preprocessing is completed according to the process of baseline correction, data smoothing, and normalization to form the input data for the classification model; Model training and validation: SVM (RBF kernel function), RF (300 decision trees), Transformer encoder and other models were trained. After optimization, the Transformer encoder achieved an accuracy of 98.5% on the test set and an average accuracy of 97.8% with 10-fold cross-validation, and was determined to be the optimal classification model.

[0032] Calculate plasma temperature based on preprocessed spectral signals; Calculate plasma image brightness based on preprocessed plasma image; Spectral correction of plasma temperature based on plasma brightness; A multi-category dual-modal quantitative analysis model was constructed. Rock lithology and spectrally corrected plasma temperature and preprocessed plasma image were input into the multi-category dual-modal quantitative analysis model for quantitative analysis, and the results of rare earth element quantitative analysis of geological rock samples were obtained.

[0033] Preferably, calculating the plasma temperature based on the preprocessed spectral signal includes: For a set of energy levels, the spectral line intensity produced by the transition The following relationship must be satisfied: (1) Where λ is the spectral line wavelength. For the transition probability, For energy level statistical weights, To stimulate energy, Boltzmann's constant, The plasma temperature, For particle number density, These are experimental parameters related to the instrument hardware (pulse energy, detector efficiency, and optical path design); if we let: (2) (3) (4) (5) Spectral line intensity The relation can be simplified to: (6) R non-self-absorption spectral lines of the same element are selected (in experiments, R is usually 5-10 non-self-absorption spectral lines). Spectral line parameters are obtained from the NIST atomic spectral database, and the preprocessed spectral signals are used to... The vertical axis is , A linear fit is performed on the x-axis to calculate the temperature. : (7) Furthermore, the plasma image brightness is calculated based on the preprocessed plasma image, including: The preprocessed plasma image is divided into M parts according to brightness (M is generally set to 7). The outermost part represents the experimental environment and is not included in the brightness calculation. The brightness of each of the remaining parts is calculated separately. and data volume The total amount of data included in the calculation is: (8) The final plasma image brightness L is: (9) Furthermore, the plasma temperature was spectrally corrected based on plasma brightness, and the comparison of rare earth element peaks before and after correction is shown in the figure below. Figure 6 As shown, the specific corrections include: The plasma temperature of the sample was fitted to the plasma image brightness, and the temperature and brightness were positively correlated. The plasma temperature was then corrected based on the plasma brightness. When calculating the plasma temperature according to formula (1), the spectral intensity can be expressed as: (10) In this formula, and The value is related to the wavelength of the analysis line. The exponential term, as an intrinsic interference, is related to the plasma temperature and additional factors such as background and noise of the spectral acquisition system. Considering that the plasma image is the most essential factor in the acquisition of spectral signals, by establishing the relationship between plasma temperature and plasma image, the plasma temperature calculated by the spectrum can be corrected to obtain a plasma temperature closer to the real one. Substituting the corrected plasma temperature into formula (4) yields the corrected value. It is represented as: (11) The mathematical essence of the F correction factor is the inverse operation of the exponentially decaying interference term in the original signal. It is mainly used to counteract the Boltzmann distribution attenuation caused by plasma temperature in the original spectral signal, as well as the influence of additional system factors. Therefore, the corrected signal... for: (12) like Figure 7 As shown, the quantitative analysis model for multi-class bimodal modes is as follows: Spectral feature extraction employs a 2-layer LSTM+Attention network, while image feature extraction uses a combination of a 3-layer Conv2d and a 6-layer Transformer encoder. Deep feature fusion is achieved through a modality-adaptive dynamic fusion network, and a Transformer encoder is then used after the fusion layer to perform quantitative analysis of rare earth elements. The specific training process of the model is as follows: Network training: Pre-trained LSTM-Attention (spectral) and CNN-Transformer (image) unimodal modules, 200 training epochs, with cosine annealing as the learning rate. The R of the unimodal modules... 2 Around 0.80; Fusion Network Training: The fusion layer and the category adaptation layer are then trained together. Both LSTM-Attention (spectral) and CNN-Transformer (image) output 256 features. The fusion layer fuses the spectral features and image features and inputs them into the Cross-Transformer to perform quantitative model training on La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Sc, and Y elements in geological rock samples.

[0034] Performance verification: The trained model was used to perform quantitative tests on rare earth elements. The R² of the customized bimodal model was ≥0.97, which is a significant improvement over the single-modal model.

[0035] On the other hand, the present invention also provides a quantitative analysis system for rare earth elements in geological rocks based on laser-induced breakdown spectroscopy, comprising: a data acquisition subsystem and a quantitative analysis subsystem; like Figure 2 As shown, the data acquisition subsystem includes a pulse generator 6, laser light sources 1 and 2, a spectrometer 9, a camera 3, a sample stage 14, an argon gas module, and a synchronization control module 10. The laser light source is an Nd:YAG laser; the argon gas module is used to provide an argon gas environment; the synchronization control module, spectrometer, and pulse generator are connected in sequence, and the laser and camera are respectively connected to the pulse generator. This is used to control the synchronous triggering of the laser light source and camera through the synchronization control module, and to enable the spectrometer and camera to synchronously acquire the spectral signals and plasma images of the sample. The laser source is an Nd:YAG laser (wavelength 1064nm, single-pulse energy adjustable from 0-100mJ, double-pulse delay time adjustable from 0-10μs); the spectrometer has a wavelength range of 200-900nm and a resolution of 0.05nm; the argon system provides 99.99% pure argon gas with an adjustable flow rate of 0-25L / min; and a pulse generator enables synchronized laser triggering and spectral acquisition. Collinear double-pulse LIBS experiments were used to optimize key experimental parameters: double-pulse delay time (1-8μs), laser energy (30-80), argon flow rate (0-25L / min), and spectral integration time (10-100ms) were selected as influencing factors. The signal-to-noise ratio (SNR), line intensity, and limit of detection (LOD) of rare earth element characteristic spectral lines were used as evaluation indicators. The optimal parameter combination was determined through range analysis, variance analysis, and regression modeling.

[0036] like Figure 3 As shown, the quantitative analysis subsystem includes: The preprocessing module is used to preprocess spectral signals and plasma images; The spectral feature extraction module is used to input the preprocessed spectral signal into the trained classification model to determine the lithology of the geological rock sample, and to calculate the plasma temperature based on the preprocessed spectral signal. The plasma feature extraction module is used to calculate the plasma image brightness based on the preprocessed plasma image; The calibration module is used to perform spectral correction of plasma temperature based on plasma brightness; The quantitative analysis module is used to construct a multi-category dual-modal quantitative analysis model. The rock lithology and spectrally corrected plasma temperature and preprocessed plasma image are input into the multi-category dual-modal quantitative analysis model for quantitative analysis to obtain the quantitative analysis results of rare earth elements in geological rock samples.

[0037] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0038] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A quantitative analysis method for rare earth elements in geological rocks based on laser-induced breakdown spectroscopy, characterized in that, include: A laser breakdown spectral signal acquisition system and a plasma image acquisition system were constructed, and spectral signals and plasma images of geological rock samples were acquired based on the laser breakdown spectral signal acquisition system and the plasma image acquisition system, respectively. Preprocessing of spectral signals and plasma images; The preprocessed spectral signal is input into the trained classification model to determine the lithology of the geological rock sample; Calculate plasma temperature based on preprocessed spectral signals; Calculate plasma image brightness based on preprocessed plasma image; Spectral correction of plasma temperature based on plasma brightness; A multi-category dual-modal quantitative analysis model was constructed. Rock lithology and spectrally corrected plasma temperature and preprocessed plasma image were input into the multi-category dual-modal quantitative analysis model for quantitative analysis, and the results of rare earth element quantitative analysis of geological rock samples were obtained.

2. The method for quantitative analysis of rare earth elements in geological rocks based on laser-induced breakdown spectroscopy according to claim 1, characterized in that, The plasma temperature is calculated based on the preprocessed spectral signal, including: For a set of energy levels, the spectral line intensity produced by the transition The following relationship must be satisfied: Where λ is the spectral line wavelength. For the transition probability, For energy level statistical weights, To stimulate energy, Boltzmann's constant, The plasma temperature, For particle number density, For the experimental parameters of the instrument; if we let: Spectral line intensity The relation can be simplified to: R non-self-absorption spectral lines of the same element were selected, and spectral parameters were obtained from the NIST atomic spectral database. The preprocessed spectral signals were then used to... The vertical axis is , A linear fit is performed on the x-axis to calculate the temperature. : 。 3. The method for quantitative analysis of rare earth elements in geological rocks based on laser-induced breakdown spectroscopy according to claim 1, characterized in that, The plasma image brightness is calculated based on the preprocessed plasma image, including: The preprocessed plasma image was divided into M parts according to brightness. The outermost part, representing the experimental environment, was not included in the brightness calculation. The brightness of each of the remaining parts was calculated separately. and data volume The total amount of data included in the calculation is: The final plasma image brightness L is: 。 4. The method for quantitative analysis of rare earth elements in geological rocks based on laser-induced breakdown spectroscopy according to claim 2, characterized in that, Spectral correction of plasma temperature based on plasma brightness includes: Based on spectral line intensity When calculating plasma temperature using the relational formula, the spectral intensity can be expressed as: By establishing the relationship between plasma temperature and plasma image, the plasma temperature calculated by spectroscopy is corrected to obtain the corrected plasma temperature. Substitute the corrected plasma temperature into the formula Obtain the corrected ; The value of q obtained during plasma temperature calculation is denoted as... The formula for calculating spectral intensity and the design of the correction function F are: Corrected spectral signal for: 。 5. The method for quantitative analysis of rare earth elements in geological rocks based on laser-induced breakdown spectroscopy according to claim 1, characterized in that, The quantitative analysis model for multi-class dual-modality is as follows: Spectral feature extraction uses a 2-layer LSTM+Attention network, while image feature extraction uses a combination of a 3-layer Conv2d and a 6-layer Transformer encoder. Deep feature fusion is achieved through a modality adaptive dynamic fusion network, and then the Transformer encoder is used to perform quantitative analysis of rare earth elements after the fusion layer.

6. A quantitative analysis system for rare earth elements in geological rocks based on laser-induced breakdown spectroscopy, characterized in that, include: Data acquisition subsystem and quantitative analysis subsystem; The data acquisition subsystem includes a pulse generator, a laser source, a spectrometer, a camera, a sample stage, an argon gas module, and a synchronization control module. The laser source is an Nd:YAG laser; the argon gas module provides an argon gas environment; the synchronization control module, the spectrometer, and the pulse generator are connected in sequence, and the laser and camera are respectively connected to the pulse generator. This is used to control the synchronous triggering of the laser source and the camera through the synchronization control module, and to allow the spectrometer and the camera to synchronously acquire the spectral signals and plasma images of the sample. The quantitative analysis subsystem includes: The preprocessing module is used to preprocess spectral signals and plasma images; The spectral feature extraction module is used to input the preprocessed spectral signal into the trained classification model to determine the lithology of the geological rock sample, and to calculate the plasma temperature based on the preprocessed spectral signal. The plasma feature extraction module is used to calculate the plasma image brightness based on the preprocessed plasma image; The calibration module is used to perform spectral correction of plasma temperature based on plasma brightness; The quantitative analysis module is used to construct a multi-category dual-modal quantitative analysis model. The rock lithology and spectrally corrected plasma temperature and preprocessed plasma image are input into the multi-category dual-modal quantitative analysis model for quantitative analysis to obtain the quantitative analysis results of rare earth elements in geological rock samples.

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