Volume 10, Number 3 (April 2023):4417-4432, doi:10.15243/jdmlm.2023.103.4417 ISSN: 2339-076X (p); 2502-2458 (e), www.jdmlm.ub.ac.id
Open Access 4417 Research Article
Integration of remote sensing and geophysical data to enhance lithological mapping utilizing the Random Forest classifier: a case study from Komopa, Papua Province, Indonesia
Hary Nugroho1*, Ketut Wikantika2, Satria Bijaksana3, Asep Saepuloh4
1 Doctoral Program of Geodesy and Geomatics Engineering, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Jl. Ganesha 10, Bandung 40132, Jawa Barat, Indonesia
2 Geodesy and Geomatics Engineering Study Program, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Jl. Ganesha 10, Bandung 40132, Jawa Barat, Indonesia
3 Geophysical Engineering Study Program, Faculty of Mining and Petroleum Engineering, Institut Teknologi Bandung, Jl. Ganesha 10, Bandung 40132, Jawa Barat, Indonesia
4 Geological Engineering Study Program, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Jl. Ganesha 10, Bandung 40132, Jawa Barat, Indonesia
*corresponding author: [email protected]
Abstract Article history:
Received 12 November 2022 Accepted 5 January 2023 Published 1 April 2023
Lithological information is important in mineral resource exploration, geological observations, mine planning or degradation vulnerability assessment. Currently, lithology mapping can be performed in a fast, inexpensive, and easy way using remote sensing data and machine learning.
Remote sensing techniques have become a valuable and promising tool for mapping lithological units and searching for minerals. Typically, the integration of remote sensing data with geophysical data provides a better diagnosis to lithological units than single-source mapping methodologies.
Accordingly, this study used a combination of remote sensing and airborne geophysical data utilizing the Random Forest algorithm with small training samples to enhance lithology mapping in Komopa, Papua Province, Indonesia. Geophysical data consisting of magnetic, electromagnetic, and radiometric were added one by one gradually to the remote sensing data, which includes Sentinel 2A, ALOS PALSAR, and DEM (digital elevation model) to compare the accuracy of the classification results from each dataset. The results showed that the model that combined remote sensing data and the three types of geophysical data produced the best classification, with an overall accuracy of 0.81, precision of 0.66, recall of 0.47, and F1 score of 0.52. This fused data can increase the accuracy of the classification results by 8% overall accuracy, 6% precision, 11% recall, and 13% F1 score when compared to the model that only used remote sensing data.
Keywords:
airborne geophysical data lithological mapping machine learning Random Forest remote sensing
To cite this article: Nugroho, H., Wikantika, K., Bijaksana, S. and Saepuloh, A. 2023. Integration of remote sensing and geophysical data to enhance lithological mapping utilizing the Random Forest classifier: a case study from Komopa, Papua Province, Indonesia. Journal of Degraded and Mining Lands Management 10(3):4417-4432, doi:10.15243/jdmlm.2023.103.4417.
Introduction
Lithological information is essential in mineral resource exploration and geological observations. It contains information on the type of rock and its
boundaries, as well as an indication of the presence of minerals (Doveton, 2018). Regarding soil degradation vulnerability assessment, lithology and land use types significantly influence soil aggregate stability (Duan et al., 2021). Likewise, lithology plays a role in mine
Open Access 4418 planning when calculating geological deposits. Even
in calculating the pit revenue factor, lithological boundaries must be made accurately to support the pit optimization process (Holloway and Cowie, 2019).
Currently, remote sensing data has been widely used in lithological mapping (Cracknell and Reading, 2014;
Kuhn et al., 2018, 2019, 2020), which are classified according to their spectral signature using a machine learning algorithm (MLA) (Cracknell and Reading, 2014; Harris and Grunsky, 2015; Kuhn et al., 2018, 2019; Wenhua et al., 2021).
MLA is a data-driven model that classifies based on pixel data. MLA conducts learning processes to recognize rock formations, which are continued by predicting values, labeling, and classifying unknown pixels (Karlhede, 2020; Shebl et al., 2022). To carry out this classification, MLA applies supervised or unsupervised techniques (Bhattacharya et al., 2016). In the supervised technique, training data as materials for
"learning" need to be provided, where MLA uses training samples to study the rock characteristics recorded in the data. In the unsupervised technique, MLA performs clustering based on the spectral characteristics of the pixels. This classification is related to the spectral signature, which is a unique wavelength as a response from rocks to varying wavelengths (Shebl et al., 2021). MLA uses training samples to study the rock characteristics recorded in the data. It should be noted that the supervised method is better than the unsupervised method because supervised techniques can reduce uncertainty (Bhattacharya et al., 2016). The training sample in the supervised technique represents the rocks in the research area in terms of type and spatial distribution.
(Cracknell and Reading, 2014).
Lithological mapping in an area that is difficult to reach, with dense vegetation and thick humus, and no rock outcrops that can be used as clues, collecting training samples is very challenging. In such areas, drilling methods should be used to determine the rock type. As a result, the number of training samples obtained was limited. A classification that uses a small number of training samples will affect the classification results, and it will not be able to make accurate predictions (Deng et al., 2017; Kuhn et al., 2019; Makienko et al., 2020). The dense vegetation conditions also complicate geological mapping using remote sensing data. In such areas, the bedrock spectral responses present in the pixels are contaminated by the spectral responses of regolith and vegetation cover (Crippen and Blom, 2001; Yu et al., 2011; Pal and Porwal, 2016). Two things need to be done to increase the classification accuracy in dealing with such a situation. Firstly, the emphasis on vegetation's effect, can be done by applying Vegetation Suppression (Pal and Porwal, 2016;
L3Harris Geospatial Solution, 2020), and second is to add data. The application of airborne geophysical data (magnetic, electromagnetic, and radiometric) is a
suitable option because these data might provide a reasonable mapping over extensive terrane (Harris and Grunsky, 2015; Shebl et al., 2021). Furthermore, as stated by Merembayev et al. (2021), the addition of geophysical data in the classification process has improved the classification results. The ability of geophysical data to identify geological features can be further improved if this data is integrated with satellite imagery (Zhang et al., 2017). In addition, the data combination can increase the accuracy of geological mapping because the two types of data can complement each other (Ranjbar et al., 2004) and provide detailed information about trends in subsurface structures (Chinkaka, 2019).
Other data that can be added are digital elevation model (DEM) and satellite radar data such as ALOS PALSAR (Advanced Land Observing Satellite-1- Phased Array type L-band Synthetic Aperture Radar).
DEM data contains information about local topographical textures that may have geological importance (Slavinski et al., 2010). The ALOS PALSAR is used to retrieve ground surface reflectance and backscatter coefficients. The polarization dataset is usually used for geological mapping and morpho- structural lineament extraction in mineral exploration (Bannari et al., 2016).
Airborne magnetic data gives surface and subsurface information on changes in magnetic susceptibility, primarily caused by magnetite. These variations in patterns and anomalies can reveal details about the different types of rocks and their structures, alterations, and mineral deposits (Harris and Grunsky, 2015). Airborne electromagnetic data have been used recently to provide subsurface information for hydrogeological characterization. The data provided information on electrical resistivity (Abraham and Cannia, 2011). Airborne radiometric data directly measure the Earth's surface and has a minimum penetration depth (30 cm). The measured radioactive element contrasts (U, Th, K, K/Th ratio) can be mapped to bedrock and surficial geology and alteration related to mineral deposits (Harris and Grunsky, 2015). These airborne geophysical data can be well implemented in lithology identification, especially with the proven availability and advancement of MLA and remote sensing datasets in geoscience (Harris and Grunsky, 2015; Shebl et al., 2021).
The MLA that has become a favorable and efficient classifier for scientists in many fields, including geology, is Random Forest (RF) (Kuhn et al., 2016; Xie et al., 2018). RF is a supervised machine learning method that uses training samples to construct a model for classification (Myburgh and van Niekerk, 2014; Ren et al., 2017) and an extension of the decision tree method (Bogner et al., 2018). It is widely used for remote sensing image classification (Welsink, 2020).
However, cautiousness in utilizing the RF algorithm has to be taken, mainly since pattern recognition will depend on the number of training points. Furthermore,
Open Access 4419 RF is an MLA that effectively solves various problems
but is not very satisfying for the classification process with imbalanced data (Brownlee, 2020). However, by combining the bagging technique with the random feature selection technique (Xie et al., 2018), this method can improve prediction accuracy. The method generates multiple decision trees and aggregates their result to improve the performance of the classification.
Each decision tree returns a classification, and a Random Forest decides which class each observation is attributed to; based on the majority rule, the class with the most votes across all trees is determined as the final class (Breiman bagging) (Xie et al., 2018).
This study aimed to use the power of RF to classify the fused remote sensing and airborne geophysical data to enhance lithological discrimination over a case study from the Komopa area with limited training samples. An accurate lithology map is crucial because this map is a preliminary and essential step in mineral mapping and provides support in drilling engineering operations (Jellouli et al., 2019;
Liang et al., 2022). In this study, the effect of adding geophysical data on classification accuracy was examined using a small training sample. Komopa is a region in Papua Province, Indonesia, rich in minerals.
However, Komopa is an area that is difficult to access, has high and dense vegetation, is covered with thick humus, and has no outcrops.
Currently, there are few studies on areas with characteristics like Komopa, especially those related to lithological mapping using MLA. This study integrated remote sensing data (Sentinel-2A, ALOS
PALSAR), geophysical data (magnetic, radiometric, and electromagnetic), and DEM (derived from several remote sensing data). The three geophysical data were not used at once but one by one to determine the effect of each data on classification accuracy. The classification results are then compared with the existing lithological map to measure the classifier's ability to identify lithological classes and their boundaries. Because performance metrics alone cannot demonstrate how well a classification performed in lithological mapping.
Materials and Methods Study area and geological setting
Komopa area is located in Aweida Sub-district, Paniai District, Papua Province, Republic of Indonesia (Figure 1). A series of geophysical surveys have been carried out in this area, consisting of magnetic, electromagnetic, and radiometric surveys. The survey area lies within the longitudes 136° 28' 3.8892" E–
136° 33' 54.1368" E and latitudes 3° 44' 38.8608" S–
3° 48' 59.3244" S. This area also has a conventional lithological map and many borehole logs. All this data is owned by Mine Serve International (MSI). The scale of the lithological map that MSI has made is 1:25,000, which was released in 2000 (Mine Serve International, 2000). The types of lithology found in the area are quaternary alluvium, pseudo gossan, inferred porphyry, sedimentary rocks, and undifferentiated porphyry (as shown in Figure 2).
Figure 1. Location of the study area, Komopa (marked by the yellow box), in Papua Province, Indonesia (modified after NASA, 2013).
Open Access 4420 Figure 2. Lithological map of Komopa (Mine Serve International, 2000).
In comparison to other lithology classes, pseudo- gossan is more detailed. However, MSI employs this class as a guide for its mineral prospecting; as Haldar (2018) said that it may correspond to different types of primary mineralization.
Komopa is located in the western part of New Guinea Island. This island is a noted late Miocene- Pliocene copper-gold porphyry province with related gold deposits. Komopa area is located at the intersection of three major structural features:
a) A northeasterly trending fault-controlled valley coincides with the northwest margin of the Enarotali Structural Depression, a transverse fault zone with sinistral displacement that extends from the mountain front to the Derewo Fault. The study area occurs within a dilational jog along this fault zone.
b) WNW trending, southwest-verging thrusts and folds characterize much of the Late Miocene deformation throughout the Irian Jayan Mobile Belt. The project area is along strike from the Grasberg-Ertzberg District concerning these structures.
c) E-W trending structures that are most apparent on the regional aeromagnetic map and reflected by the east-west strike of the Komopa and Dawagu porphyries. These structures are parallel to the southern boundary of the Derewo Metamorphic
Belt, marked by the Derewo Fault Zone, and may reflect deep basement structures parallel to the ancestral margin of the Australian continental plate (Harahap, 2012; Van Gorsel, 2018).
Materials Sentinel-2A
Sentinel-2A is a high-resolution, multispectral photograph with a 290 km broad field of view. Multi- Spectral Instrument (MSI), its instrument, samples 13 spectral bands. However, due to the 60 m resolution's resampling of the 20 m product to 60 m, this study only used the Sentinel-2A bands with a spatial resolution of 10 m and 20 m (European Space Agency, 2019), see Table 1. The acquisition of Sentinel-2A image on January 9, 2019. The USGS Earth Explorer (Open Access Hub:https://earthexplorer.usgs.gov/, provided the granule at Level-2A. In addition, the ENVI software's Vegetation Suppression tool was used to eliminate the impact of vegetation before using this image data (L3Harris Geospatial Solution, 2020).
ALOS PALSAR and DEM
ALOS PALSAR Fine Beam Double Polarization Data (FBD), L-band, 3.17m 14.9 m (azimuth range) resolution dual-polarization (HH+HV), was the radar data used in this study area. ALOS collected the data using the Single Look Complex (SLC) data type. The
Open Access 4421 source of the granule was https://www.asf.alaska.edu/.
The Republic of Indonesia's Geospatial Information Agency provided the DEMNAS (National DEM) data used in this investigation. The stereo-plotting mass point data are added to a variety of data sources, including the IFSAR (5 m resolution), TERRASAR-X
(5m resolution), and ALOS PALSAR (11.25 m resolution) data, to create the National DEM.
DEMNAS has a 0.27-arcsecond (8.1 m) spatial resolution when utilizing the EGM2008 vertical datum (Geospatial Information Agency-Republic of Indonesia, 2018).
Table 1. Summary of data used for lithology classification.
Dataset Data Source Dataset Data Source
Sentinel 2A
Band 2
USGS
ALOS PALSAR HH
www.asf.alaska.edu
Band 3 HV
Band 4 HH-HV
Band 5 Magnetic Reduce to
Pole (RTP) Mine Serve International Band 6
Electromagnetic
2 kHz
Mine Serve International
Band 7 20 kHz
Band 8 36 kHz
Band 8a
Radiometric
Thorium (Th)
Mine Serve International
Band 11 Potassium (K)
Band 12 Uranium (U)
DEM DEM Geospatial
Information Agency,
Republic of Indonesia K/Th ratio
Airborne geophysical data
Magnetic, electromagnetic, and radiometric data were all included in the airborne geophysical data. Residual field magnetic data accurately reflect the distribution of magnetic material within the survey area. Magnetite is a magnetic mineral that predominates in nature (Fe3O4). Even lithological trace amounts will result in distinctive magnetic fingerprints (Pandarinath et al., 2019). Electromagnetic data provide accurate information on the structure and lithological changes.
While magnetic data might only yield a small amount of information, electromagnetics data can offer vital supplementary information (Liang et al., 2021). No matter how powerful the underlying radiation sources are, they are effectively hidden by 30 cm of rock, 60 cm of soil, or 1 m of water due to the short depth of penetration and the naturally complicated character of the gamma-ray spectra. As a result, lithological changes on the surface are reflected in the radiometric data (Wemegah et al., 2015). A 400-meter flight spacing was used for simultaneous magnetic and radiometric data surveys. Separately, electromagnetic data was studied with flights spaced 100 meters apart.
Methods
This study used the methodology depicted in Figure 3.
There are five processes as follows: (1) preparation of remote sensing and geophysical data; (2) preparation of training data; (3) lithological classification utilizing Random Forest algorithm; (4) accuracy evaluation;
and (5) visual comparative assessment.
Data preprocessing
Data preprocessing was applied to all remote sensing data, consisting of Sentinel-2A, ALOS-PALSAR, and DEM, as well as airborne geophysical data, which includes magnetic, electromagnetic, and radiometric data. At this stage, all data will be resampled into a grid of 20 m×20 m, and all values will be normalized so that all data will have values that have the same range between 0 and 1. This normalization is necessary because the difference in numerical magnitude between all data is likely to have a detrimental effect on the classification model, so data normalization is necessary before training and testing the model (Zhou et al., 2020). The first data preprocessing was carried out on Sentinel-2A. Atmospheric correction and orthorectification were performed using ESA's Sentinel Application Platform (SNAP) software. Then, the Vegetation Suppression facility from ENVI (L3Harris Geospatial Solution, 2020) was applied to remove vegetation effects. The ALOS PALSAR data used in this study has been orthorectified, slope corrected, and mosaiced by Japan Aerospace Exploration Agency-Earth Observation Research Center (JAXA-EORC). The data was acquired on July 2, 2007, in the form of Fine Beam Double Polarization (FBD), L-band, 3.17 m×14.9 m (azimuth×range) dual- polarization (HH+HV) resolution with Single Look Complex (SLC) data type. Furthermore, the data were calibrated and radiometrically corrected so that the pixel value could represent radar backscatter from the reflected surface.
Open Access 4422 Figure 3. Flowchart of research methodology.
The SLC data was converted into Ground Range Detected (GRD) data to obtain a spatial resolution of 20 m×20 m. Finally, the data was processed into HH- HV polarization backscatter (Ottinger and Kuenzer, 2020). This study used to reduce to the pole (RTP) data derived from magnetic data. To find the RTP, this study performed the preprocessing stage, which begins with filtering; compilation and leveling work; non- linear filtering; gridding; spectral analysis; low pass filter, decorrugation; IGRF removal; contouring, and reduction to pole (Ansari and Alamdar, 2009). The radiometric data consisted of thorium (Th), potassium (K), uranium (U), and the ratio of thorium and potassium (K/Th). The process started with checking the data with preliminary grids and stacked profiles using the minimum curvature gridding method. Next, decorrugation was done using linear contour intervals, and color contour plots were made using an equal area distribution (IAEA Nuclear Energy Series, 2013;
Dumais, 2014). Electromagnetic data included 2 kHz, 20 kHz, and 36 kHz data and was processed in 2 stages, i.e., quality control (frequency and time domain) and 1D inversion (layered model and inversion result) (GeoSci, 2018).
Training and testing samples preparation
This study used 500 training samples and 502 testing samples as a borehole log analysis obtained from Winkie and Longyear drilling because outcrops are difficult to find in this area. Table 2 shows the rock samples' distribution, which denotes the sample dataset's imbalanced condition. Figure 4 depicts their spatial distribution. The two pictures in Figure 4 show that both training and testing samples have an uneven distribution. The majority of points are in the middle
region. It is an attempt by MSI to detect the presence of minerals in more detail.
Table 2. Distribution of training and testing samples.
Rock Types Sum
IP PG QA SR UP Training
Samples 14 8 18 123 337 500 Testing
Samples 8 9 17 124 344 502
Notes: IP: Inferred Porphyry; PG: Pseudo Gossan;
QA: Quaternary Alluvium; SR: Sedimentary Rocks;
UP: Undifferentiated Porphyry.
Lithology classification using Random Forest algorithm
To examine the effect of using a combination of remote sensing and geophysical data to improve lithological classification utilizing Random Forest algorithm, this study built 6 fused classifier inputs:
Model A (remote sensing data, consisting Sentinel-2A, ALOS PALSAR, and DEM); Model B (remote sensing and magnetic (RTP-reduce to pole)); Model C (remote sensing and electromagnetic data (EL, consisting of 2 kHz, 20 kHz, and 36 kHz wavelength)); Model D (remote sensing and radiometric data (RAD, consisting of thorium-Th, potassium-K, uranium-U, and K/Th ratio)); Model E (remote sensing, magnetic and radiometric data); and Model F (remote sensing, magnetic, electromagnetic, and radiometric data). The Random Forest algorithm was built using the Python module from the Scikit-learn library. In this study, the default Scikit Learn hyperparameter settings were employed in the decision tree development process (Scikitlearn, 2020).
Open Access 4423 Figure 4. Spatial distribution of (a) training samples, (b) testing samples.
Accuracy evaluation
Classification accuracy is a metric that generally describes how the model performs across all classes. It is the sum of the number of correct predictions in a dataset divided by the number of predictions (Brownlee, 2020). This equation yields the training accuracy when applied to the training dataset. When applied to the testing dataset, it yields test accuracy. In measuring the classification performance with imbalanced conditions, accuracy is not sufficient (Brownlee, 2020). The accuracy must be combined with other metrics, such as precision, recall, and F1 score, which are computed from the confusion matrix (Gosain and Sardana, 2017; Luque et al., 2019). The
confusion matrix elements describe the performance of a classification or prediction model. Each element shows the number of classifications or predictions made by the model that classifies the class correctly or incorrectly (Brownlee, 2020; Karlhede, 2020).
Figure 5 shows an overview of binary classification, which has two classes to classify:
positive and negative. The elements of the confusion matrix are (Polvimoltham and Sinapiromsaran, 2021):
- True Positive (TP): refers to the number of predictions where the classifier correctly predicts the positive class as positive.
- True Negative (TN): refers to the number of predictions where the classifier correctly predicts the negative class as negative.
Open Access 4424 - False Positive (FP): refers to the number of
predictions where the classifier incorrectly predicts the negative class as positive.
- False Negative (FN): refers to the number of predictions where the classifier incorrectly predicts the positive class as negative.
Figure 5. Confusion matrix to assess the performance of classification results.
For the calculation of accuracy, precision, recall, and F1 score, we used the equation (1), (2), (3), and (4) as follows (Gosain and Sardana, 2017; Polvimoltham and Sinapiromsaran, 2021).
Accuracy = (1)
Precision = (2)
Recall = (3)
F1 Score = 2 ∗ (4)
Precision indicates the ratio of the correct positive predictions to the total positive predictions. It refers to the quality of the positive predictions made by the model. Meanwhile, recall is the ratio of correctly predicted positive classes to the total number of positive samples. It refers to the quality of a positive detection made by the model. The F1 score is a
performance metric that considers both precision and recall. It is a single performance metric calculated using the harmonic mean of the two metrics. The F1 score is used to consider false positives and negatives equally important (Brownlee, 2020).
Visual comparative assessment
This study also performed a visual comparison between the lithological map from the RF classification and the existing map (the MSI lithology map). This comparison is made because performance metrics from the confusion matrix alone are insufficient to provide a complete picture of the classification results' quality. Comparative visual assessment includes accuracy in detecting lithological types and boundaries. This comparison will show which combination of remote sensing and geophysical data works best for lithology mapping when the training samples are imbalanced and have uneven spatial distribution.
Results
Accuracy assessment
Six classification processes were run using Random Forest and the same training data in order to facilitate a wise comparison of the six models (A, B, C, D, E, and F). Model A served as the baseline model.
Classification performance was assessed via confusion matrix, accuracy, precision, recall, and F1 score (F1).
Table 3 shows the accuracy of the classification results. In general, adding geophysical data to remote sensing data can enhance the performance of Random Forest classification. Only precision values for models with the addition of RTP (model B), EL (model C), and RTP-RAD (model E) got precision values below the baseline model. Meanwhile, all other metrics in the B, C, D, E, and F models were above the baseline model values.
Table 3. Classification accuracy.
Model Train Accuracy Test Accuracy Precision Recall F1 score
A 1.00 0.73 0.60 0.36 0.39
B 1.00 0.78 0.48 0.38 0.40
C 1.00 0.78 0.59 0.37 0.40
D 1.00 0.78 0.69 0.38 0.43
E 1.00 0.80 0.52 0.40 0.43
F 1.00 0.81 0.66 0.47 0.52
Notes: Model A: RS, Model B: RS+RTP, Model C: RS+EL, Model D: RS+RAD, Model E: RS+RTP+RAD, Model F:
RS+RTP+EL+RAD.
The value of train accuracy for all models shows a maximum value of 1, meaning that the results of the comparison between the lithology class from the classification results and the lithology class from the training samples are the same. It means the trained model has a good understanding of the training set.
However, it must be remembered that just because a model achieves extremely high training accuracy does not imply that it is a good model (Yoon, 2021).
Meanwhile, all models have test accuracy values lower than train accuracy (19-27%). It means that all models are overfitting or close to overfitting. In other words,
Open Access 4425 the classification model built is in line with the training
points, but when tested with testing points or new data, the results are not completely right (Karlhede, 2020).
According to Hoens and Chawla (2013), this overfitting condition can occur due to the imbalanced model (see Table 2), and this potential will be even more significant if the imbalance ratio between the majority classes (UP and SR classes) and the minority classes (IP, PG, and QA) is getting worse. However, in the models that add geophysical data (models B, C, D, E, and F), the impact of overfitting becomes smaller according to the amount of data added. Rezaeezade et al. (2022) claimed that adding more data would help prevent overfitting.
Table 3 reveals that model A (RS) has a precision value of sixty percent. Its performance can be surpassed by models D (RS+RAD) and F (RS+RTP+RAD+EL). Furthermore, it shows that the model with a high precision value is a model that uses radiometric data; Sentinel-2A itself can record radiometric data (Kääb et al., 2016). Thus, the addition of radiometric data can improve the ability to predict lithology class. However, in Model E (RS+RTP+RAD), this increase did not occur. It is because of the strong influence of magnetic data, which can record data to a depth of tens of kilometers (Mohamed and Deep, 2021). High electrical ground conductivities caused by salty groundwater or high levels of contamination do not affect magnetic data (Mariita, 2007).
Table 3 also shows that the recall value is lower than the precision in all models. However, the recall value increases with the addition of geophysical data.
In other words, adding geophysical data can improve the classifier ability to detect positive samples.
Referring to the statement of Juba and Le (2019), adding more data always increases both precisions and
recall significantly, this study shows that recall values increase for all models, but precision only increases for models D and F. Meanwhile, Hoens and Chawla (2013) said that in situations with imbalanced data where the goal of classification is to increase recall without losing precision, there are often conflicting objectives. This is because increasing the number of true positives (TP) for the minority class often means increasing the number of false positives (FP), which makes precision worse. However, the magnitude of the increase in recall value for the addition of geophysical data indicates that the addition of geophysical data will be significant if two or three types of geophysical data are integrated (see models E and F). This means that with dense vegetation, thick humus, and imbalanced data conditions, the addition of one type of geophysical data has not been able to have a significant impact on the classifier's ability in the lithology class identification process.
The F1 score shows conditions similar to the recall. The addition of geophysical data increases the F1 score in all models. The best F1 score is shown by model F, which also has the best score of test accuracy and recall. This condition is in line with Zhu et al.
(2018) statement that F1 and recall are commonly used to assess classifier performance with imbalanced data conditions and a high proportion of minority classes.
The F1 score can be interpreted as the weighted average of precision and recall. Both the F1 score and recall can differentiate performance among classifiers.
Thus, in this study, it can be seen that adding geophysical data causes the recall value to increase, which is in line with the increase in the F1 score.
To evaluate the precision and recall scores in each lithology class, the following comparison of Models A and F is provided. Reference should be made to Tables 4, 5, 6, and 7.
Table 4. Classification performance of Model A.
Class Precision Recall F1 score Sum of Test Samples
IP 0.33 0.12 0.18 8
PG 1.00 0.11 0.20 9
QA 0.31 0.29 0.30 17
SR 0.58 0.32 0.41 124
UP 0.77 0.93 0.84 344
Notes: IP: Inferred Porphyry; PG: Pseudo Gossan; QA: Quaternary Alluvium; SR: Sedimentary Rocks, UP: Undifferentiated Porphyry.
Table 5. Confusion matrix of Model A.
Class Actual
Sum of Test Samples
IP PG QA SR UP
Predicted IP 1 0 2 2 3 8
PG 0 1 1 3 4 9
QA 0 0 5 1 11 17
SR 1 0 7 40 76 124
UP 1 0 1 23 319 344
Open Access 4426 Table 6. Classification performance of Model F.
Class Precision Recall F1 score Sum of Test Samples
IP 0.67 0.25 0.36 8
PG 0.67 0.22 0.33 9
QA 0.37 0.41 0.39 17
SR 0.78 0.52 0.63 124
UP 0.84 0.96 0.90 344
Table 7. Confusion matrix of Model F.
Class Actual
Sum of Test Samples
IP PG QA SR UP
Predicted IP 2 0 3 0 3 8
PG 0 2 1 6 0 9
QA 1 0 7 1 8 17
SR 0 1 6 65 52 124
UP 0 0 2 11 331 344
Tables 4 and 6 show the performance metrics for Models A and F, while Tables 5 and 7 show how many pixels each model's classifiers were able to identify.
When the performance metrics in the two models are compared, it can be seen that the performance of Model F is better than Model A (Table 4 and Table 6).
This is shown in detail in Table 5 and Table 7, where in Model F the number of pixels identified as true lithology class or true positive (diagonal elements) is higher than in Model A. Likewise, the false positive (FP-vertical column outside of the diagonal elements) and false negative (FN-horizontal column outside the diagonal elements) in Model F are less than those in Model A. This causes the precision and recall values for each lithology class in Model F to be better than in Model A.
Visual comparative assessment
Figure 6 shows lithological classification via Random Forest of the six models. In order to compare each classification result to the existing lithological map, the lithological boundaries of the existing map were superimposed on each classification result. In general, the classification of Model A provided a result that could show all types of lithology. However, the lithology type depicted is not very good because of misclassification in some areas. The misclassification is a reflection of the recall value obtained by Model A (0.36) (Table 3), and all classes had low recall scores (0.1-0.32) except for UP (0.93) (Table 4). Therefore, it can be seen that the classifier has successfully identified the UP class and shown its boundaries.
However, the SR class shows that the recall score obtained is lower than UP and almost the same as QA (see Table 4), whereas in the existing lithology map, the SR area is more expansive than QA. Data presented in Table 5 show that the false negative (FN) for SR is quite high (76 points were identified as UP), so it can be understood that in some areas (south, southeast,
northeast), which should be occupied by SR, but were identified as UP (see Figure 6a).
Model A used radiometric and electromagnetic data (from Sentinel-2A), active radar (ALOS PALSAR), and DEM. The problem with geological information extraction is dense vegetation (Engelbrecht, 2016). However, this problem can be solved by using vegetation suppression (Yu et al., 2011; Grebby et al., 2014), and integrating it with synthetic aperture radar (SAR) and DEM (Engelbrecht, 2016; Kurnianto et al., 2020; Ghosh et al., 2022), where lithology identification is based on radiation backscatter and landforms that are related to the character of the subsurface processes. Variations in surface backscatter can be related to texture variations in different lithologies. Consequently, the visualization of classification results in Model A appears fairly well.
Integrating RTP data in model B significantly improves Random Forest's ability to identify SR and UP. It can be seen by how well the classifier was able to identify this class and its boundaries. This condition is understandable because RTP can detect magnetic material distribution and identify magnetic intrusion areas, and as is well known, the SR and UP lithology types have absolutely magnetite (King, 2022). Thus, the SR area can be adequately identified. However, in locations identified as SRs, there are very few training samples. Different things happen to the QA class, which is in an area close to the SR class. In this model, the ability to identify the QA class was reduced, especially in the southern part, which is close to the SR. It is predicted that in this region, the QA is a thin layer covering the SR, so the magnetic information obtained is subsurface magnetic information. In this model, the lithological boundaries of the QA, SR, and UP classes can be identified well in certain areas.
Model C utilized a combination of remote sensing and electromagnetic data. Electromagnetic data is sensitive to conductive and resistive materials (Emond, 2021).
Open Access 4427 Therefore, this model should improve the
classification performance, especially in areas where resistive (SR) and conductive (QA, UP) regions meet in a transition zone. However, the classification results indicate that this model has a visualization similar to Model A. Likewise, the confusion matrix shows almost the same performance metric scores, except for the test accuracy. Thus, adding electromagnetic data does not significantly increase lithology identification ability. This condition is probably caused by
weathered layers, where in the study area, the thickness of this layer can reach 200 meters.
According to Salako and Adepelumi (2016), this is a conductive layer. However, excessive weathering depth presents the greatest obstacle to the effective use of airborne electromagnetic methods. Based on studies in semi-arid and temperate regions of Brazil, it is proved that the use of regional airborne electromagnetic surveys is efficient if the study area has a weathering layer of 10 to 20 m thick.
Figure 6. Comparison between the classification results and the existing lithological map.
Open Access 4428 In Model D, where the remote sensing data was
combined with radiometric data, it can be seen that the model built, compared to Model A, is better at detecting the lithology type of SR. It makes sense because radiometric data can distinguish intrusive rocks from surface materials (Emond, 2021), so it is responsive to detecting alluvium and porphyry.
However, unfortunately, in the area where the QA class should be (in the central part), some of this area is detected as an SR class. This region's lack of QA training samples is thought to be due to this. Model D is a model that can detect the presence of PG lithology.
Although the identification results are very few (see Figure 6d, southwest area), they can be visualized quite clearly.
Model E is a remote sensing, magnetic, and radiometric data-integrated model. These data can improve the model's ability to detect areas with magnetic intrusions and distinguish intrusive rocks based on the radioactive elements they contain. In the southern region, it can be seen that the classifier's ability to detect SR is increasing, although there are only a few training samples at this location. Thus, in the southern region, the SR class was detected well enough, but the SR class displaced the QA class in some areas. It is considered to be the impact of the lack of QA training samples in the southern region.
However, as in Model D, the ability to detect the PG class in Model E shows good results because of the radiometric data.
Model F is the one with complete remote sensing and geophysical data. All lithology types can be detected in this model, but the classification results are not good enough, especially for the QA class.
However, IP lithology type can be detected in areas that match the existing lithological map. Similar to Model E, Model F also misclassified the space that QA should occupy. It is likely because there are few QA training samples in this area and the RTP data's significant influence. It can be seen in detail in Table 7, which shows the FN value for SR from QA, where the value is 6 points; this means that there are 6 points out of a total of 19 test points (31.5%) that should have been QA but were identified as SR. The type of PG class can be detected quite well in this model, although there are only 9 test points in this area, and only 2 points are correctly identified (see Table 7). Thus, it can be concluded that the classifier's ability increases with the addition of geophysical data to detect the PG class, which includes hydrous oxides of iron and manganese, gold, and silver in their native states, as well as various sulfates, carbonates, and silicate minerals (Hosch, 2022).
Discussion
The accuracy of the RF classification results in this study was then compared with that of the current publications (although this comparison cannot be
made directly due to the diversity of assessment methodologies and metrics). Comparisons were made to the results of a study conducted by Shebl et al.
(2021), which uses the MLA Support Vector Machine (SVM) with Sentinel2A data integrated with radiometric data (total count), and Bachri et al. (2022), which uses the MLA Random Forest (RF) with Sentinel-2A data integrated with DEM from ALOS PALSAR. The comparison of the performance metrics is presented in Table 8. The classification performance metrics of this study are slightly lower than the classification results of other researchers because they used a much larger number of training samples, and the study area is in a desert, so there is no vegetation contamination on spectral satellite images. However, it should be understood that the geological conditions of the three study locations are also different, and the classifications carried out by other researchers use different machine-learning settings. Looking at the confusion matrix in Table 3, in general, the classification performance in this study is relatively low. This is likely due to the small number of training samples and the imbalanced data. However, according to Fernández et al. (2018), this condition occurs not only because of these two things but is also caused by intrinsic characteristics, which include, among other things, data complexity, data rarity or lack of data, class overlap, and noisy data. Data complexity is an indicator that shows the difficulty level of the classifier's learning on specific or minor datasets (Anwar, 2012). The four indicators include the dimensionality of the dataset, the number of features relative to the sample size, the imbalance ratio (ratio between the number of major and minor class training samples), and the effectiveness of available features to separate classes (Anwar, 2012; Barella et al., 2021).
In this study, the number of training samples for the minor and major classes is significantly different (see Table 2), so the imbalance ratio is quite large.
Likewise, the number of features used reaches 22 (see Table 1). Thus this study has quite complex data, so in order to identify minor classes that have a small number of training samples, the classifier has difficulty. This can be seen from the low F1 score (see Table 3). Likewise, looking at all the classification results in Figure 5, all models have difficulty identifying minor classes. Data rarity (lack of data) is related to the distribution of training samples (Weiss, 2004), which states the insufficiency of information for the learning algorithm to generalize to the dataset (Fernández et al., 2018). It can also be said that there are only a few minor class training samples used to build classification models. In conditions where the data shows absolute rarity, classification will give bad results. In this study, situations like this can be seen in classifying PG and IP classes for all models; see Figure 6. Overlapping classes occur due to several training samples in several classes in the same area and seem to be together or not separated (Fernández et al., 2018).
Open Access 4429 Table 8. Comparison of the accuracy of classification results with other studies.
Researcher MLA Litho- logical
Class
Training Samples
per lithology
class
Overall Test Accuracy
Precision per lithology
class
Recall per lithology
class
Training Samples Distribution
Land cover
Shebl et al.
(2021)
SVM 13 955-3397 0.78-0.86 0.41-0.97 0.52-1.00 Imbalanced Desert Bachri et al.
(2022) RF 10 186-2467 >0.88 0.85-0.95 - Imbalanced Desert
This study RF 5 14-337 0.73-0.81 0.37-0.84 0.22-0.96 Imbalanced Vegetation and humus
In this study, overlapping classes occurred in the IP class training samples whose position was between the UP and SR classes (see Figure 5). As a result, the classifier cannot classify minor classes accurately.
According to Barella et al. (2021), overlapping and imbalanced data classes are different problems. But, both of them together have a terrible effect on decision-making. This can be understood from Figure 6, wherein all models, the IP classes that are in the central part, are difficult to identify correctly.
Noisy data is defined as a wrong label (class noise) or errors in the attribute value (attribute noise) (Salgado et al., 2016; Asniar et al., 2022). Noise originates from the large number of data inconsistencies that affect data quality. In this study, noisy data is unavoidable because the data used comes from 6 data sources with a total of 22 features (see Table 1). Before entering the classification stage, all data goes through a preprocessing step which can add noise (see Methods section).
Considering the aforementioned conditions, it is clear that all classification results provide a performance metric that is relatively low. The addition of complete geophysical data only gives a precision value of 66%, a recall of 47%, and an F1 score of 52%.
In order to improve the accuracy of classification results, it is not sufficient to simply add more data;
additional methods must also be employed.
Improvements to the data and algorithm levels can be added as a potential solution (Sun et al., 2009;
Fernández et al., 2018; Noorhalim et al., 2019) or a combination of both, which is called the hybrid method (Krawczyk, 2016).
Conclusion
Integrating remote sensing and geophysical data utilizing the Random Forest classifier for lithology mapping can accurately classify most rock types and their boundaries in the study area, Komopa. Even with a small number of training samples, magnetic data can improve the ability to identify rock types and rock boundaries containing magnetite. Quaternary alluvium class can be correctly identified using only remote sensing data since they are a relatively shallow layer,
and their depth corresponds to the depth of wave penetration used by remote sensing. Similarly, when electromagnetic data are combined with remote sensing data, the resulting performance metrics improve, even though the difference is not visually significant. Adding radiometric data also significantly improves the identification of the pseudo gossan class containing radioactive elements. Compared to a model that only uses remote sensing data, adding airborne geophysical data that includes magnetic, electromagnetic, and radiometric data can improve classification performance metrics by 8% overall accuracy, 6% precision, 11% recall, and 13% F1 score.
However, the performance metrics resulting from the fused data - precision of 0.66, recall of 0.47, and F1 score of 0.52 - remain still relatively low, which can be improved by reducing risks due to the implications of the intrinsic characteristics, which include data complexity, data rarity, overlapping training samples, and noisy data. Considering the limitations of this research, investigating several problems to determine how the intrinsic characteristics affect the classification results and applying various techniques to improve the classification accuracy by making improvements at the data and algorithm levels should be made.
Acknowledgements
This research was supported by PT. Eksplorasi Nusa Jaya, Mine Serve International and funded by Institut Teknologi Nasional Bandung.
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