Geochemical Characteristics of Modern River-Sand and Its Bearing on the Mineral Exploration in the Manufahi Area, Timor-Leste

Abstract

Geochemical mapping of river sands in the Manufahi area of Timor-Leste revealed potential areas for future mineral exploration. River sand samples from the study area were collected and geochemically analyzed to identify anomalous concentration distributions of several valuable elements and locate potential target areas and geological formations that may host mineral deposits. The 26 major and minor elements were identified using wavelength-dispersive X-ray fluorescence. The river sands exhibited varying elemental concentrations, with Cr, Cu, Zn, and Ba showing deviations from the normal distribution patterns. Identification of geochemical anomalies is an important task in mineral exploration geochemistry. The mean+2 standard deviations (mean+2STD), median+2 median absolute deviations (median+2MAD), and Tukey’s inner fence (TIF) methods were used to determine the geochemical thresholds. This study shows that TIF and principal component analysis (PCA) methods are highly effective in calculating appropriate threshold values and identifying relevant elemental associations. These approaches have proven useful for delineating target areas for mineral deposits, resulting in reliable outcomes. Four predicted target areas with high potential for deposits and mineralization anomalies of Cr, Cu, Ni, and Ba were delineated in the study area.

1. Introduction

“River sand” is the term used to refer to the samples under analysis in this study [1,2,3]. Fine-to-coarse sand fractions were obtained from the active channel below the water level. Modern river sand is an effective medium for geochemical mapping in many regions worldwide including Japan [4,5], Portugal [6], Brazil [7], and Australia [8]. The characteristics of modern river sands in drainage basins are often influenced by various factors, including source rock lithologies, mineral deposit occurrence, climate, topographic relief, transportation, and depositional environment [8,9,10]. However, the source area materials appear to have the greatest influence on the geochemistry of modern river sands [11,12,13,14,15]. Geochemical data for river sand and other surface sediments often deviate from a normal distribution [16]. The potential occurrence of mineral deposits or atypical lithologies, such as iron-rich sedimentary rocks, radiolarian cherts, carbonatites, and serpentinites, may be responsible for the high elemental concentrations found in river sands [17].

The main duty of the geochemical assessment of mineral resource prospects in the identification of potential areas for further investigation is to determine and separate anomalies, thresholds, and background values [18,19]. Various statistical analyses and methods have been employed using different approaches and parameters, including univariate, bivariate, multivariate, and spatial analyses. These include mean, median, mode, standard deviation, variance, skewness, kurtosis, median absolute deviation (MAD), interquartile range (IQR), Tukey’s inner fence (TIF), mean+2 standard deviations (mean+2STD), median+2 median absolute deviations (median+2MAD), and an association of distribution plots, such as histograms, boxplots, and normal quantile plots, to characterize anomalies and define threshold and background values in a univariate data set that falls outside a normal distribution. Additionally, the Spearman correlation coefficient, principal component analysis (PCA), and inverse distance weighting (IDW) interpolation methods were applied to identify the original geochemical signatures of the source composition and the distribution of anomalies that may be related to mineral deposits or atypical lithologies [19,20,21,22,23,24,25,26,27].

Timor island, as a part of the continent of Australia, is generally characterized by the presence of the significant amount of highly deformed and partially faulted and folded sedimentary, metamorphic, and a few igneous rocks. These rocks vary from the Permian to the Quaternary, and they are associated with allochthonous, para-autochthonous and autochthonous units with affinity to the Asian, Australian, and synorogenic successions. Most of these rocks appear to have undergone significant geological processes, including metamorphism, deformation, volcanic activity, and other tectonic events, both before and after their emplacement in their current positions. These processes occurred during the orogenic phase, leading to the development of thrust faults, overthrust, and the uplift of the territory [28,29,30,31,32,33]. Timor-Leste has an attractive geological setting with well-developed structures and favorable metallogenic conditions, which have led to the discovery of several types of mineral deposits there [34].

The occurrence of mineral deposits is generally associated with the igneous rocks, predominantly mafic and ultramafic in composition. These rocks are well exposed in outcrops, either as rock units associated with several particular formations or as exotic blocks incorporated into the shales and mudstones of the Middle Miocene Bobonaro Complex. Additionally, they also occurred as erosional sediments deposited in ancient fluvial terraces of the Pleistocene–Holocene Ainaro Formation [35,36,37,38,39].

In 1936, mineral exploration activities were conducted across the entire territory of Timor-Leste, including the study area, by Wittouck and the team from Allied Mining Corporation. According to the report by Wittouck [35] on Portuguese Timor Exploration, several potential mineralization areas were discovered and numerous gold nuggets were discovered using the gold panning method. However, no trenching or test pitting was carried out during this project’s mineral exploration.

A recent study by Lay et al. [38] on the Hili Manu chromites reported the occurrence of podiform chromite deposits, along with massive sulfide mineralizations associated with peridotites in ophiolites. In addition, Vicente et al. [39] also identified potential occurrences of chromite mineral deposits associated with peridotites dominated by the Maquelab Complex in the Maquelab area (Oecusse, Timor-Leste).

Several laterite deposits containing Ni and chromite mineralization have also been identified in ophiolites, including those on the islands of Kalimantan, Sulawesi, Maluku, Papua, Tanimbar, Seram, and Buru, as well as in the Philippines [40,41,42,43]. Chromite occurrences in Timor-Leste are comparable to those in Indonesia and the Philippines, reflecting similar geological settings [38,39,44,45].

This study focused on the geochemistry of river sands with the main objective of gathering important information regarding the anomalous concentration distributions of valuable elements and identifying potential target areas and geological formations that may host mineral deposits in the study area. The results of this study can serve as a basis and provide useful information for future geochemical mapping and mineral exploration campaigns in Timor-Leste and similar global geological settings.

2. Geographical and Geological Setting

The territory of Timor-Leste comprises the eastern portion of the island of Timor, the small offshore areas of Atauro Island and Jaco Islet, and the enclave of Oecusse (Figure 1). Geologically, the small island of Atauro is part of the inner Banda arc of the volcanic island. Timor and Jaco Islet are part of the outer Banda arcs and are non-volcanic islands formed by orogenic processes resulting from the collision between the passive margin of the Australian continent and the Banda volcanic arc of the Eurasian plate after the subduction of the Indian Ocean crust [29,32,46,47,48,49]. The study area is geographically located on the south coast of Timor-Leste. The study area is largely characterized by rugged to moderate mountainous topography, with various slopes ranging from moderate to steep. Timor-Leste, including the study area are located in a hot intertropical climatic zone characterized by seasonal monsoons. However, the climate varies across regions due to the significant influence of varying altitudes [50], and it has two distinct seasons: a dry period from June to November and a rainy season from December to May [51,52]. Average annual temperatures and rainfall range from <21 to 25 °C and 2000 to 2500 mm in elevated areas, while in lowland and coastal areas, temperatures range from 25 to 27 °C with annual rainfall between 1500 and 2000 mm [50,53].

As delineated by Audley-Charles [28], Bachri and Situmorang [57], and Partoyo et al. [58], along with geological research and mapping carried out by other researchers [32,59,60,61,62,63,64,65,66,67,68,69], the study area encompasses several formations (Figure 1C): (a) Suai Formation, is Pleistocene to the Holocene in age and predominantly consists of rudites and arenites, with minor proportions of mud and marls. These rocks are mainly unconsolidated deposits and typically abundant in foraminifera; (b) Ainaro Formation is Pleistocene to Holocene in age and primarily consists of matrix-supported conglomerates. Ferruginous horizon often caps the irregular upper of these fluvial terrace sediments; (c) Baucau Limestone, which ranges in age from the Lower Pleistocene to the Holocene, primarily consists of coral-reef limestones, along with minor amounts of calcarenites, calcirudites, and conglomerates. Massive coral growths are the predominant components of coral reef limestones; d) Dilor Conglomerate, composed of sandstones and conglomerates with significant detritus input from the quartzites of the Lolotoi Complex, is Pliocene in age; (e) Viqueque Formation, dating from the Upper Miocene to the Lower Pliocene, is lithologically divided into two sections: (i) the upper layer comprises significant proportions of silty marls, marly siltstones, silty claystones, siltstones, and sandstones, as well as lesser calcilutites and biocalcarenites; (ii) the lower layer contains minor proportions of basal conglomerates and mottled marls, along with notable amounts of marls, clayey marls, silty marls, calcilutites, and tuffs; (f) Bobonaro Complex, Middle Miocene in age, is dominated by exotic blocks within a scaly clay matrix. The matrix lithology resembles that of the mudstone of the Wailuli Formation. Exotic blocks that are Permian to Cretaceous in ages are common and extensively distributed; (g) Cablac Limestone, ranging in age from the Oligocene to the Miocene, predominantly consists of oolitic and peloidal limestones, pelagic carbonates, and minor proportions of intraformational conglomerates, calcilutites, calcarenites, agglomerates, and tuffaceous rocks; (h) Barique Formation is Oligocene in age and is primarily composed of mafic to acidic lavas and tuffs, with lesser amounts of serpentinites, volcanic conglomerates, and sandstones; (i) Dartollu Limestone, which has been dated to the Middle–Upper Eocene, is largely composed of algal and alveolina biomicarenites, with minor amounts of calcilutites, siliceous shales, and siltstones; (j) Waibua Formation ranges in age from the Lower to the Upper Cretaceous and mainly consists of radiolarites, radiolarian cherts, marls, and shales, with varying proportions of calcilutites, marls, and calcarenites; (k) Wailuli Formation, in which the major constituents include gray shales and blue-gray marls, with lesser amounts of sandstones, mudstones, quartz-arenites, coarse polymictic conglomerates, calcarenites, and calcilutites, is Late Triassic to Middle Jurassic in age. Most shales are characterized by fine micaceous minerals and microcrystalline carbonates, with small amounts of pyrite; (l) Aitutu Formation, which ranges in age from the Middle to Upper Triassic, is largely composed of calcilutites, shales, and calcareous shales with minor proportions of marls, calcarenites, lumachelles, quartz-arenites, radiolarites, bituminous rocks, and cherts. Several limestones have undergone silicification, dedolomitization, and pyritization processes; (m) Lolotoi Complex mainly consist of metamorphosed sedimentary and volcanic rocks, as well as basic to ultrabasic and felsic volcanic rocks. They are mostly greenschists, phyllites, amphibolite, garnet-bearing pelitic gneisses and schists, metagabbros, garnet mica schists, mafic and felsic igneous dikes, pelitic schists, metabasite schists, carbonate-rich greenschists, and peridotites [28,32,68,69]. Most metamorphic rocks within the Lolotoi Complex are derived from sedimentary rocks. Several meta-volcanic rocks have been extensively altered and they frequently occur in association with meta-sedimentary rocks. The Lolotoi Complex was emplaced to their current allochthonous position as flat thrust sheets (klippen) and this complex dates from the Triassic to the Late Cretaceous; and (n) Maubisse Formation, which is Permian in age, largely comprises fossiliferous limestones and volcanic rocks, along with well-bedded dense biocalcarenites, massive reef limestones, pink crinoidal limestones, calcirudites, sandstones, calcareous shales, micaceous siltstones, tuffs, volcanic conglomerates, basalts, marbles, and metamorphosed basic volcanics.

3. Samples and Methods

3.1. Sampling and Preparation

In this study, basic procedures for sampling river sand and treating samples before chemical analysis, as outlined in Hale and Plant [70], Darnley et al. [71], Tanaka et al. [72], Fletcher [73], Ohta et al. [74], and Yamamoto et al. [5], were implemented. Modern river sand samples were collected from 53 sites in the study area (Figure 1). Samples were dried in an oven at 105 °C and then sieved using a vibrating sieve shaker machine (VSS-200S Type, Tsutsui Scientific Instruments Co., Ltd., Tokyo, Japan). The composite samples of fine-grained river sands sieved through 180–150 and <150 μm metal sieves were ground using an agate mortar and pestle, followed by machine crushing using an agate ball mill (Vibrating ball mill machine, ITO Seisakusho Co., Ltd., Yokkaichi, Japan). Moisture and volatile materials, consisting of “combined water” (hydrates and labile hydroxy-compounds) and carbon dioxide from carbonates, were removed from the samples using the loss-on-ignition method [75]. The removal was quantified by measuring the weight loss of the samples before and after heating them to 950 °C.

As described and recommended by Yamamoto and Morishita [76], to analyze major and minor elements, glass beads were used for sample preparation in X-ray fluorescence (XRF) analysis. The fused glass beads were prepared by mixing the powder at a sample-to-flux ratio of one to two after heating. The flux used was a white crystalline powder of lithium tetraborate (Li₂B₄O₇ = 169.12). The mixed powder was placed in a platinum crucible and processed using a bead sampler (TK-4100 model, Amena Tech Co., Yokohama, Japan) to form glass beads.

3.2. Analytical Methods and Data Analysis

Fifty-three modern river sand samples were analyzed using wavelength-dispersive XRF (WD-XRF) at the Division of Instrumental Analysis, Gifu University, and 26 major and minor element concentrations were determined. These elements were accurately calibrated using standard reference samples provided by the Geological Survey of Japan (GSJ).

3.2.1. Univariate Analysis

In this study, basic and classical descriptive statistics refer to univariate analyses, which include the calculation and estimation of minimum, maximum, mean, mode, median, variance, range, standard deviation, and quartile values. In addition to the aforementioned parameters, skewness and kurtosis were also employed as initial estimates to indicate the distribution characteristics of the studied geochemical elements. In normal distributions, the skewness is zero and the kurtosis is approximately three, whereas the mean, mode, and median values are nearly identical [77,78,79,80]. In addition, statistical methods such as mean+2STD, median+2MAD, and TIF are the most precise methods for defining anomaly threshold values, proving more effective and straightforward than other approaches. In certain cases, where the raw geochemical data did not follow a normal distribution, the mean+2STD method failed to provide a relevant threshold estimate. The TIF method was calculated using the formula: [TIF = Q3 + 1.5 × IQR], where the third quartile (Q3) represents the 75th percentile value (Q75), and interquartile range (IQR) represents the difference between the 75th and 25th percentile values (Q3-Q1) [19,20,22,81,82,83,84].

The distribution plots, which involve the joint application of several graphs and diagrams, such as normal quantile plots, boxplots, and histograms, are focused on visualizing, simplifying, and clarifying the distribution and characteristics, or any deviations, in the studied geochemical elements that could be attributed to the contributions of the underlying lithologies or mineral deposits [80,85,86,87]. The boxplot method is also one of the most precise approaches for defining anomaly thresholds in exploration targeting, particularly when dealing with complex geochemical data, including non-normal distributions, spatial heterogeneity, and background variability. This approach identifies outliers as values falling beyond 1.5 or 3 times the interquartile range (IQR) above the third quartile or below the first quartile, helping in the detection of outliers or extreme values within a distribution and making it especially effective for skewed geochemical data. Figure 2 shows the elements of boxplot, which categorizes a set of geochemical element data into five reliable classes as follows [85]: (a) the lowest-lower whisker is classified as extremely low background; (b) lower whisker-lower hinge is labelled as low background; (c) lower hinge-upper hinge is defined as background; (d) upper hinge-upper whisker is considered high background; and (e) upper whisker-highest is classified as an anomaly. In addition, the upper inner fence was defined as the threshold for separating the background from anomalous values.

3.2.2. Bivariate and Multivariate Analyses

The Spearman correlation coefficient was employed to identify and evaluate the inter-relationships between the variables of the geochemical elements that deviated from a normal distribution. This approach is more effective than the Pearson correlation coefficient, which is sensitive to outliers and can potentially distort the results [88]. PCA is one of the most commonly used techniques for multivariate statistical analyses and is an effective and reliable approach for extracting information and determining the composition of geochemical populations [9,23,89], outlining the source composition of underlying lithologies and identifying areas of interest for the potential occurrence of mineral deposits [27,90]. Only principal components with eigenvalues greater than or near 1.0 were used to analyze and interpret PCA results in a biplot of joint graphical representations of variables and samples because they represent most of the variance in the data [82,91,92]. IDW is a deterministic, predictable, and reproducible approach for multivariate spatial techniques that can be applied to compute and interpolate values in areas where samples were not collected. IDW assigns more influence on the interpolated value to the points closest to the known value points than to those farther away [93]. In this study, IDW was employed to create a surface model that represented the spatial distribution of multi-element associations derived from the PCA scores along with several independent variables.

Descriptive statistics, distribution plots, Spearman’s correlation coefficients, and PCA were performed using Microsoft Office Excel version 2405 (Build 17628.20144) and JMP Pro 14. Additionally, the maps presented in this study, including interpolated and geological maps, were created using ArcGIS 10.4.

4. Results

4.1. Statistical and Distribution Characteristics of Geochemical Elements

The basic and classical descriptive statistics of the univariate analysis of the 26 raw geochemical elements in river sands from the study area are summarized in

Table 1. Descriptive statistics for raw geochemical elements in river sands from the study area. All major element concentrations are given in weight percent (wt.%), and minor element contents are measured in parts per million (ppm).

Elements	Min.	Q25	Median	Q75	Max.	Mean	Mode	Std.	Variance	Range	Skew	Kurt	MAD	IQR	Mean+2Std	M+2MAD	TIF
SiO2	36.84	49.25	52.47	54.6	59.94	51.55	54.53	4.82	23.212	23.1	−1.14	1.77	2.54	5.35	-	57.55	-
TiO2	0.67	1.06	1.2	1.37	2.17	1.21	1.48	0.27	0.07	1.5	0.7	2.14	0.15	0.32	1.74	1.49	1.84
Al2O3	9.97	13.2	14.1	16.2	17.9	14.5	13.2	1.93	3.739	7.93	−0.11	−0.56	1.3	3	-	16.7	-
Fe2O3	4.75	7.2	8.19	9.24	11.94	8.18	-	1.54	2.369	7.19	0.33	0.16	1.03	2.04	11.26	10.24	-
MnO	0.09	0.17	0.18	0.23	0.32	0.19	0.16	0.04	0.002	0.23	0.43	0.25	0.02	0.07	0.28	0.23	-
MgO	1.66	2.65	3.12	3.76	4.70	3.2	2.98	0.75	0.559	3.04	0.08	−0.62	0.49	1.11	4.7	4.1	-
Na2O	1.06	1.81	2.11	2.47	2.79	2.09	2.06	0.43	0.181	1.73	−0.41	−0.28	0.34	0.66	-	2.79	-
K2O	0.68	1.28	1.4	1.63	2.15	1.45	-	0.29	0.084	1.47	0.35	0.61	0.14	0.35	2.03	1.68	-
P2O5	0.15	0.18	0.2	0.22	0.27	0.2	0.21	0.03	0.001	0.12	−0.03	−0.31	0.02	0.04	0.26	0.24	-
CaO	4.93	6.86	10.00	12.54	24.25	10.5	-	4.49	20.185	19.32	1.39	2.23	3.02	5.68	19.49	16.03	21.05
Cr	49	126.5	166	205.5	1148	195.04	165	152.74	23,328.152	1099	4.9	29.75	39	79	500.51	244	324
Co	0	36	59	79	109	56.36	0	28.69	823.31	109	−0.12	−0.8	21	43	-	101	-
Ni	23	45.5	56	70.5	103	58.93	46	17.63	310.84	80	0.42	−0.19	11	25	94.19	78	-
Cu	0	24.5	38	54	599	51.49	26	80.05	6408.14	599	6.38	44.08	14	29.5	211.59	66	98.25
Zn	38	81	97	109	240	95.72	81	30.07	904.25	202	1.95	9.31	16	28	155.86	129	151
Ga	0	13	21	30.5	41	21.08	0	11.36	129.15	41	−0.27	−0.86	9	17.5	-	39	-
Rb	0	42.5	62	74.5	115	58.6	61	25.71	661.13	115	−0.43	0.17	14	32	110.02	90	-
Sr	225	277.5	321	348	443	316.76	258	49.49	2449.15	218	0.36	−0.15	37	70.5	-	395	-
Y	0	15.5	26	38.5	52	25.42	0	14.7	216.02	52	−0.27	−0.88	12	23	-	50	-
Zr	65	162	196	209.5	265	184.51	167	40.27	1621.52	200	−0.73	0.63	22	47.5	-	240	-
Nb	0	4.5	18	28.5	38	16.81	0	12.51	156.39	38	0.004	−1.37	11	24	-	-	-
Ba	0	330.5	408	553.5	1718	483.26	396	303.92	92,369.85	1718	1.95	5.33	104	223	1091.1	616	888
Pb	0	32.5	61	84.5	149	57.13	0	36.82	1356	149	0.08	−0.48	26	52	130.77	113	-
Th	0	0	22	48.5	73	26.17	0	24.61	605.68	73	0.41	−1.3	22	48.5	-	66	-
Sc	16	23.5	26	29.5	36	26.21	25	4.01	16.09	20	0.05	−0.09	3	6	34.23	32	-
La	0	18	38	53	85	36.17	0	23.06	531.68	85	0.08	−0.79	16	35	82.29	70	-

Min. = minimum; Max. = maximum; Q25 = first quartile (Q1); Q75 = third quartile (Q3); Std. = standard deviation; Skew = skewness; Kurt = kurtosis; MAD = median absolute deviations; IQR = interquartile range; mean+2STD = mean + 2 x standard deviations; M+2MAD = median + 2 x median absolute deviations; and TIF = Tukey’s inner fence.

. In this study, we present geochemical analysis results for both major and minor elements. However, our focus was on minor elements, as the geochemical characteristics of the major elements regarding their provenance have already been discussed and published [94].

Table 1 presents the skewness and kurtosis values, which reveal that TiO₂, Fe₂O₃, MnO, K₂O, CaO, Cr, Cu, Zn, and Ba exhibited values higher than zero, whereas the remaining elements displayed skewness or kurtosis values lower than zero. Table 1 also documents the geochemical threshold values used to separate anomalies from background values for the 26 elements in river sands from the study area. These threshold values were determined using the mean+2STD, median+2MAD, and TIF methods. Anomaly threshold values obtained from statistical approaches that exceeded the maximum measured values were excluded. Nb had a threshold value determined by the median+2MAD approach, whereas SiO₂, Al₂O₃, Na₂O, Co, Ga, Sr, Y, Zr, Nb, and Th had threshold values obtained from the mean+2STD method, which were greater than the maximum measured values. In addition, TiO₂, CaO, Cr, Cu, Zn, and Ba exhibit threshold values lower than the maximum values derived from the TIF approach.

To complement the descriptive statistics, distribution plots for the raw geochemical data of the river sands were generated. The distribution plots for Cr, Cu, Zn, and Ba are presented in Figure 3 and those for the remaining elements are provided in the (Appendix A, Figure A1 and Figure A2). The distribution plots for the analyzed geochemical elements primarily exhibited a normal distribution; however, those for Cr, Cu, Zn, and Ba deviated from the normal distribution.

4.2. Correlation and Multi-Element Relationships

The Spearman correlation coefficients between any pair of variables (elements) for the sampled river sands are presented in

Table 2. Non-parametric correlation coefficient (Spearman) between geochemical elements in the river sands from the study area. The correlations between elements discussed in the text are denoted by boldface type.

	SiO2	TiO2	Al2O3	Fe2O3	MnO	MgO	Na2O	K2O	P2O5	CaO	Cr	Co	Ni	Cu	Zn	Ga	Rb	Sr	Y	Zr	Nb	Ba	Pb	Th	Sc	La
SiO2	—
TiO2	−0.03	—
Al2O3	0.52	0.39	—
Fe2O3	0.02	0.76	0.66	—
MnO	0.23	0.5	0.08	0.20	—
MgO	0.18	0.45	0.79	0.78	−0.01	—
Na2O	0.49	0.44	0.77	0.61	0.17	0.74	—
K2O	0.45	−0.13	0.41	−0.03	0.11	−0.01	0.02	—
P2O5	0.06	0.47	0.59	0.68	0.17	0.55	0.5	0.37	—
CaO	−0.76	−0.3	−0.9	−0.53	−0.21	−0.65	−0.72	−0.51	−0.49	—
Cr	0.24	0.51	0.76	0.81	0.13	0.88	0.68	0.01	0.53	−0.68	—
Co	0.37	0.6	0.87	0.87	0.18	0.84	0.72	0.24	0.68	−0.82	0.9	—
Ni	0.29	0.34	0.84	0.69	0.06	0.86	0.62	0.31	0.61	−0.77	0.89	0.87	—
Cu	0.29	0.47	0.81	0.75	0.22	0.7	0.62	0.35	0.75	−0.73	0.74	0.85	0.79	—
Zn	0.48	0.35	0.88	0.62	0.12	0.67	0.57	0.53	0.58	−0.86	0.69	0.84	0.79	0.89	—
Ga	0.49	0.49	0.92	0.79	0.16	0.78	0.71	0.37	0.65	−0.9	0.84	0.97	0.86	0.84	0.89	—
Rb	0.58	0.13	0.76	0.37	0.15	0.41	0.38	0.8	0.45	−0.84	0.46	0.67	0.66	0.64	0.83	0.77	—
Sr	−0.27	−0.3	−0.4	−0.49	0.06	−0.52	−0.34	0.01	−0.22	0.48	−0.4	−0.53	−0.36	−0.34	−0.4	−0.51	−0.33	—
Y	0.59	0.48	0.91	0.7	0.19	0.72	0.7	0.42	0.56	−0.94	0.78	0.93	0.81	0.76	0.87	0.97	0.81	−0.53	—
Zr	0.53	0.5	0.81	0.69	0.24	0.62	0.56	0.47	0.56	−0.87	0.7	0.88	0.74	0.72	0.83	0.92	0.82	−0.56	0.94	—
Nb	0.63	0.38	0.87	0.64	0.20	0.69	0.65	0.49	0.54	−0.96	0.74	0.9	0.8	0.78	0.89	0.95	0.84	−0.54	0.97	0.92	—
Ba	0.06	0.45	0.12	0.25	0.42	−0.05	−0.07	0.29	0.21	−0.18	0.15	0.22	0.13	0.23	0.27	0.24	0.28	0.22	0.27	0.37	0.25	—
Pb	0.39	0.45	0.87	0.77	0.19	0.7	0.61	0.46	0.7	−0.84	0.8	0.93	0.83	0.84	0.86	0.95	0.82	−0.4	0.91	0.89	0.91	0.3	—
Th	0.51	0.48	0.89	0.76	0.21	0.73	0.67	0.39	0.6	−0.91	0.79	0.94	0.81	0.82	0.88	0.97	0.79	−0.52	0.96	0.92	0.96	0.26	0.94	—
Sc	0.01	0.5	0.56	0.73	−0.13	0.76	0.6	−0.24	0.41	−0.4	0.68	0.66	0.55	0.51	0.47	0.58	0.15	−0.49	0.52	0.47	0.45	−0.09	0.48	0.55	—
La	0.44	0.6	0.87	0.82	0.23	0.76	0.67	0.37	0.65	−0.86	0.81	0.96	0.83	0.83	0.87	0.97	0.75	−0.5	0.95	0.91	0.93	0.30	0.94	0.95	0.56	—

. The PCA results for the variables are presented in

Table 3. Variable results of the PCA for the first four components with eigenvalues greater than 1.0. Loading values are presented for variables with values greater than +0.3 and less than −0.3.

	PC1	PC2	PC3	PC4		PC1	PC2	PC3	PC4
Eigenvalues	14.57	3.41	2.01	1.46	Eigenvalues	14.57	3.41	2.01	1.46
Explanation (%)	56.04	13.1	7.72	5.63	Explanation (%)	56.04	13.1	7.72	5.63
SiO2	0.582	0.519			Cu			−0.496	0.737
TiO2	0.504	−0.374	0.675		Zn	0.635	0.338	−0.396	0.484
Al2O3	0.939				Ga	0.983
Fe2O3	0.773	−0.479			Rb	0.79	0.502
MnO			0.745	0.307	Sr	−0.504			0.509
MgO	0.776	−0.506			Y	0.965
Na2O	0.718				Zr	0.923
K2O	0.364	0.816			Nb	0.936
P2O5	0.685				Ba		0.469	0.705
CaO	−0.882				Pb	0.941
Cr	0.519	−0.394			Th	0.94
Co	0.975				Sc	0.569	−0.743
Ni	0.872				La	0.962

and depicted in the component pattern biplot of the first four principal components, as shown in Figure 4 and Figure 5 (A and B). The percentages of variance explained by the first, second, third, and fourth principal components were 56.04%, 13.10%, 7.72% and 5.63%, respectively. PC1 exhibited strong positive relationship with Ga, Co, Y, La, Pb, Th, Al₂O₃, Nb, Zr, Ni, Rb, MgO, Fe₂O₃, Na₂O, P₂O₅, Zn, SiO₂, Sc, Cr, TiO₂, and K₂O. In contrast, the negative PC1 scores had strong loadings for CaO and Sr. K₂O, SiO₂, Rb, Ba, and Zn showed relative enrichment in the positive values of PC2, while the negative PC2 scores were strongly associated with Sc, MgO, Fe₂O₃, Cr, and TiO₂. MnO, Ba, and TiO₂ showed strong positive correlations with PC3, while the negative PC3 values were strongly associated with Cu and Zn. In addition, PC4 exhibited strong positive correlations with Cu, Sr, Zn, and MnO.

Table 4. Sample results for the first four components of the PCA. Loading values for samples with values greater than +2.0 and less than −2.0 are shown in bold.

	PC1	PC2	PC3	PC4		PC1	PC2	PC3	PC4
Eigenvalues	14.57	3.41	2.01	1.46	Eigenvalues	14.57	3.41	2.01	1.46
Explanation (%)	56.04	13.1	7.72	5.63	Explanation (%)	56.04	13.1	7.72	5.63
SS1	6.241	1.91	−0.167	−0.279	SS28	−1.369	0.128	−0.044	0.422
SS2	1.795	1.945	−0.84	−0.266	SS29	−8.541	−2.186	−1.95	−1.434
SS3	−2.082	−1.894	−0.521	−0.407	SS30	−5.838	−0.517	−1.524	0.255
SS4	−6.08	−4.906	−0.515	−0.484	SS31	−3.878	−0.54	−0.939	−0.138
SS5	−0.41	0.349	1.025	0.455	SS32	−2.069	0.997	−1.185	−0.219
SS6	−5.92	−1.675	−0.853	−0.827	SS33	−4.552	−0.524	0.144	−0.006
SS7	1.277	0.131	0.166	0.454	SS34	−1.158	1.178	1.457	−0.317
SS8	1.041	0.483	0.148	−0.189	SS35	2.759	−0.232	0.805	−0.712
SS9	−1.57	2.325	0.873	−1.247	SS36	−1.977	0.428	0.453	0.364
SS10	2.101	−0.774	−1.291	−2.191	SS37	−0.29	−0.559	0.99	0.809
SS11	−3.52	−2.292	−0.501	−0.17	SS38	−2.657	0.558	−0.406	−0.26
SS12	−0.632	3.786	−1.375	−2.795	SS39	−1.509	4.305	−5.062	5.982
SS13	−2.188	2.016	2.731	0.817	SS40	5.329	−0.149	−1.263	−0.236
SS14	−3.546	1.994	−0.891	−1.376	SS41	5.991	−0.907	−0.933	0.09
SS15	−7.762	−0.435	3.481	1.546	SS42	5.387	−0.705	−1.064	−0.381
SS16	−4.358	0.418	1.244	0.451	SS43	1.27	2.588	0.389	−0.473
SS17	0.961	1.531	−1.053	−0.293	SS44	3.672	−1.408	−0.843	−0.078
SS18	−0.465	1.047	−0.961	0.166	SS45	5.258	−4.152	−0.039	2.173
SS19	1.482	−0.967	1.393	0.872	SS46	3.949	−1.411	−0.963	−0.155
SS20	2.01	0.079	1.01	0.31	SS47	2.445	−1.105	−1.047	−0.922
SS21	0.032	−0.602	1.697	0.96	SS48	5.193	−0.235	0.951	0.499
SS22	4.61	−0.499	0.333	−0.288	SS49	−1.696	5.748	3.063	−0.59
SS23	6.534	−0.502	0.938	0.173	SS50	5.409	−2.045	2.706	1.177
SS24	−4.508	−1.248	−0.083	0.422	SS51	3.578	−0.471	−0.622	−0.75
SS25	−0.995	−1.209	1.446	0.951	SS52	3.554	−0.393	−0.681	−0.851
SS26	−3.164	−0.407	0.059	0.023	SS53	1.763	0.918	−0.551	−1.636
SS27	−0.905	0.088	0.665	0.603

presents the results derived from the PCA analysis of the samples. According to the results, samples SS23, SS1, SS41, SS50, SS42, SS40, SS45, and SS48, which exhibited high loading values greater than 5.0, showed strong positive associations with the first principal component. These samples are located in the upstream areas of the northern and northwestern branches of the Karau Ulun River, as well as in the upstream areas of the Laclo do Sul and Clerec Rivers, suggesting a predominance of carbonate rocks. In contrast, the negative values of PC1 were strongly associated with samples SS29, SS15, SS4, SS6, and SS30, which showed low loading values of less than −5.0. These samples were collected from basins located in the midstream areas of the Karau Ulun River’s north-nortwest branches and midstream area of the Sahe River, demonstrating a dominance of non-carbonate rocks. The positive values of the second principal component showed great relationships with samples SS49, SS39, SS12, SS43, SS9, and SS13, all of which had high loading values exceeding 2.0. These samples were collected from the midstream areas of the Laclo do Sul, Clerec, and Sahe Rivers, as well as upstream area of the Sahe River. On the other hand, samples SS4, SS45, SS11, SS29, and SS50, which displayed low loading values below −2.0, were located in the midstream areas of the Karau Ulun River’s north-nortwestern branches and in the midstream areas of the Laclo do Sul and Clerec Rivers. These results indicate that mafic-rich rocks either dominate or are absent in these basins. Sample SS39 was strongly associated with the negative values of PC3, whereas the positive PC3 scores showed strong correlations with samples SS15, SS49, SS13, and SS50. These samples were collected from the midstream areas of the Sahe, Clerec, and Laclo do Sul Rivers, indicating the potential presence of rocks with or without Cu-Zn mineralization.

Furthermore, samples SS039 and SS045 have strong positive correlation with fourth principal component, suggesting the presence of a mineralization-forming environment in these basins.

5. Discussion

5.1. Geochemistry and Provenance of River Sands

The concentration ranges of minor elements in Earth surface materials, as presented by Kabata-Pendias and Mukherjee [95], are provided in the (Appendix A,

Table A1. The abundance range values of minor elements in argillaceous, sandstone, and basic to ultramafic rocks (data from Kabata-Pendias and Mukherjee [95]).

Minor Elements	Basic-Ultramafic Igneous Rocks (in ppm)	Argillaceous Sedimentary Rocks (in ppm)	Sandstones (in ppm)
Cr	170–3400	80–120	20–40
Co	35–200	14–20	0.3–10
Ni	130–160	40–90	5–20
Cu	10–120	40–60	5–30
Zn	40–120	80–120	15–30
Ga	15–24	15–25	5–12
Rb	2–45	120–200	10–45
Sr	140–460	300–450	20–140
Y	0.5–20	25–40	15–250
Zr	80–200	160–200	180–250
Nb	10–35	15–20	0.5–10
Ba	250–400	500–800	100–320
Pb	0.1–8	14–40	5–10
Th	1–14	10–12	2–4
Sc	5–35	10–15	1–3
La	2–70	30–90	17–40

) and can be used as references for the study area. Certain minor elements in basic rocks have higher concentrations than those in argillaceous rocks and sandstones, and vice versa. The basic and ultramafic rocks are identified by a significant percentage of mafic minerals [96], which typically contain higher concentrations of Cr, Co, Ni, Cu, and Sc [95,97]. In the basic and ultramafic igneous rocks, the values of Cr, Co, Ni, Cu, Nb, and Sc range from 170–3400, 35–200, 130–160, 10–120, 10–35, and 5–35 ppm, respectively. In addition, argillaceous rocks are characterized by a significant proportion of clay minerals [98]. However, carbonate materials are also abundant and have been found to be integrated into sediment and sedimentary rocks in different proportions [99].

The concentrations of Zn, Ga, Rb, Sr, Ba, Pb, Th, and La in the argillaceous sedimentary rocks range from 80–120, 15–25, 120–200, 300–450, 500–800, 14–40, 10–12, and 30–90 ppm, respectively. The values of Y and Zr in the sandstones vary from 15–250 and 180–250 ppm, respectively.

The high concentrations of Cr are probably related to the presence of the mafic minerals rich lithologies with or without chromite deposits. In addition, the elevated concentrations of Cu and Zn recorded in the river sands within the study area are possibly related to the occurrence of rocks with Cu-Zn deposits [39,100]. Ba, Sr, and Rb are widely recognized as mobile elements that are often depleted in river sands because of their mobility and leachability during weathering [101,102]. However, the high concentrations of Sr and Ba measured in the study area are likely due to the presence of the barite minerals occurring within carbonate-rich lithologies associated with hydrothermal processes [39,103].

The Spearman correlation coefficient results (Table 2) revealed that the contribution from the destruction of carbonate, clay, mica, and feldspar minerals was reflected in the negative correlation between most of the minor elements and Sr and CaO, significant positive associations between most minor elements and Ga and Al₂O₃, and significant positive correlations between K₂O and Rb and Pb and Rb. Ba displayed a positive relationship with K₂O, Rb, and Sr. Moderate-to-strong positive associations were observed among Cr, Co, Ni, Sc, TiO₂, Fe₂O₃, and MgO, likely reflecting the contributions of minerals such as chromite, garnet, olivine, pyroxene, amphibole, sphene, rutile, hematite, ilmenite, magnetite, biotite, and chlorite to the river sand compositions.

The major contributions of clastic sedimentary rocks (such as shales, claystones, siltstones, and mudstones), mafic igneous rocks with chromite deposits, carbonate sedimentary rocks, metamorphic rocks, and Cu-Zn deposits in the river sands were confirmed by observing the distribution plots of minor elements (Figure 3 and Appendix A, including Figure A1 and Figure A2), comparing the concentrations of minor elements with the range in concentrations presented by Kabata-Pendias and Mukherjee [95], and the results of the Spearman correlation coefficient [97,100,104,105,106]. The potential occurrence of hydrothermal and metamorphic processes, along with the presence of clastic and carbonate sedimentary, igneous, and metamorphic rocks in the study area, has been reported by Vilanova et al. [94].

The PCA results indicated that the pattern plot of PC1 and PC2 (Table 3 and Table 4; Figure 4) defined the geochemical associations of elements related to the origin and composition of the underlying lithologies, whereas PC3 and PC4 (Table 3 and Table 4; Figure 5) allowed identification of the presence and distribution of a mineralization-forming environment in the study area. The PCA findings corroborate the aforementioned results.

The pattern plot of PC1 may effectively separate the presence and distribution of compositional variations between the carbonate- and non-carbonate-rich river sands from the study area. Carbonate minerals associated with carbonate sedimentary rocks or carbonate-rich lithologies whose source rock derives from a carbonated environment, may have significantly contributed to the elemental association observed in the negative scores of PC1. This component was prominently recorded at sample locations SS29, SS15, SS4, SS6, and SS30, which were mainly drained from the Aitutu, Viqueque, and Wailuli Formations. The elemental relationship seen in the positive values of PC1 may have been strongly influenced by non-carbonate minerals, such as clay, mica, quartz, feldspar, heavy, and other accessory minerals commonly found in clastic sedimentary rocks. These rocks are sourced from both continental and oceanic parent rocks, which are often associated with active tectonic environments. The elemental association observed in these positive values was highly concentrated in sample locations SS23, SS1, SS41, SS50, SS42, SS40, SS45, and SS48, which were primarily drained from the Wailuli, Viqueque, and Aitutu Formations, as well as the Bobonaro Complex. The PC2 pattern plot effectively captures the compositional variations between the mafic- and non-mafic-rich river sands in the study area. The negative PC2 scores reflect contributions from chromite, garnet, spinel, olivine, pyroxene, amphibole, biotite, magnetite, sphene, ilmenite, and chlorite minerals, which are associated with mafic environment characteristics or clastic sedimentary rocks derived from predominantly oceanic parent rocks typical of active tectonic regions. Representing this component, samples SS4, SS45, SS11, SS29, and SS50 were collected from areas drained from the Lolotoi and Bobonaro Complexes as well as from the Ainaro, Viqueque, and Wailuli Formations. The basic rocks associated with the Bobonaro Complex were identified as exotic blocks that were incorporated into clastic sedimentary rocks (such as shales and mudstones) [107]. The elemental association observed in the positive values of PC2 was potentially consistent with the relative abundance of feldspar, quartz, mica, and clay minerals in the river sands. These minerals can be attributed to the presence of acidic rock compositions or clastic sedimentary rocks sourced from major contributions of Australian continental plate. The elemental relationship observed in these positive scores was highly reported in samples SS49, SS39, SS12, SS43, SS9, and SS13, which were collected from areas drained from the Wailuli Formation.

5.2. Influence of Mineral Deposit Potential on the Geochemical Characteristics of River Sands

The statistical parameters of the raw geochemical elements, particularly skewness and kurtosis, indicated that Cr, Cu, Zn, and Ba had values greater than 1 and 3, respectively. These elements also exhibited relatively noticeable differences in the mean, median, and mode values. For CaO, the skewness value was greater than 1, whereas the kurtosis value was less than 3. The threshold values for TiO₂, CaO, Cr, Cu, Zn, and Ba were obtained using these three methods. The threshold values achieved by the methods followed the decreasing order TIF > mean+2STD > median+2MAD for TiO₂ and CaO, whereas Cr, Cu, Zn, and Ba decreased in the order mean+2STD > TIF > median+2MAD. The TIF approach values corresponded closely to the upper inner fence values of the boxplot [81,82,108], and the results of this approach were used as the threshold values [83,84].

Five geochemical classes for the concentrations of Cr, Cu, Zn, and Ba were identified using the boxplot model [85], as shown in

Table 5. Geochemical classes derived from the boxplot model for Cr, Cu, Zn, and Ba concentrations (in ppm) in river sands from the study area.

Geochemical Class	Calculated Values (in ppm)
	Cr	Cu	Zn	Ba
Extremely low background	-	-	38–45	-
Low background	49–126.5	0–24.5	45–81	0–330.5
Background	126.5–205.5	24.5–54	81–109	330.5–553.5
High background	205.5–324	54–98.25	109–151	553.5–888
Anomaly	324–1148	98.25–599	151–240	888–1718

. A distribution map of geochemical classes for Cr, Cu, Zn, and Ba in the study area was delineated using the IDW method for gridding and illustrating the spatial distributions of geochemical anomalies (Figure 6). Anomalous values of Cr were observed in the midstream to downstream areas of the Laclo do Sul River basin. In contrast, anomalous values of Ba were concentrated in the northern branch of the Clerec River catchment, midstream areas of the Sahe River basin, and midstream areas of the western branch of the Karau Ulun River catchment. In addition, the nearly identical spatial distribution patterns of Cu and Zn likely indicated that they were derived from the same source materials [109]. Anomalous values of Cu and Zn were observed in the midstream area of the Laclo do Sul basin.

The positive PC3 and PC4 values as well as the negative PC2 and PC3 scores were subjected to distribution plots (Figure 7) to separate the five geochemical populations with extremely low background, low background, background, high background, and anomalies (

Table 6. Geochemical classes derived from the boxplot models for the positive PC3 and PC4 scores and negative values of PC2 and PC3.

	Geochemical Class
	Extremely Low Background	Low Background	Background	High Background	Anomaly
Negative score of PC2	-	-	0–(−0.94)	(−0.94)–(−2.29)	(−2.34)–(−4.91)
Positive score of PC3	-	-	0–0.94	0.94–1.7	2.36–3.48
Negative value of PC3	-	-	0–(−0.94)	(−0.94)–(−1.95)	(−2.34)–(−5.06)
Positive value of PC4	-	-	0–0.45	0.45–0.96	1.13–5.98

). Five distinct geochemical classes were delineated using the IDW approach for gridding and illustrating the spatial distributions of anomalous values from the selected PCA components in the study area (Figure 8).

Sc, MgO, Fe₂O₃, Cr, and TiO₂ were grouped together on the negative side of PC2, likely reflecting the presence of mafic, heavy, and accessory minerals concentrated in the igneous and metamorphic rocks [100,106,110]. However, the high anomalous value distributions of Cr and elemental associations in the negative PC2 scores (Figure 6 and Figure 8) may be related to the presence of igneous rocks (basic and ultramafic) containing chromite mineral deposits [39,94]. The samples representing these anomalies were collected from areas that were drained from the Lolotoi and Bobonaro Complexes, particularly in the upstream areas of the northwestern branch of the Karau Ulun River catchment and the midstream areas of the Laclo do Sul basin. The existence of igneous rocks with chromite mineral deposits associated with the Bobonaro Complex as exotic blocks were incorporated into shales and mudstones [107].

The occurrence of a potential mineralization zone dominated by Cu and Zn could explain their high anomalous value distributions, as well as the elemental relationships in the negative PC3 scores [100,105], in the midstream areas of the Laclo do Sul River basin (Figure 6 and Figure 8). This anomaly is particularly evident in the sample drained from rocks dominated by the Wailuli Formation, which could be the host rocks for the Cu-Zn mineralization.

Overall, the contributions of carbonate and muscovite were most likely responsible for the presence and distribution of Ba [97,104] in the river sands of the study area. However, the high anomalous value distributions of Ba and the elemental association of MnO-Ba-TiO₂ in the positive PC3 scores likely indicate the occurrence of secondary alteration mineral assemblages associated with mineralization and formation of barite, rutile, goethite and Mn-bearing minerals [39,111]. The distribution of geochemical classes for positive PC3 values in the study area shows that anomalous values of this component were concentrated in the northern branch and downstream areas of the Clerec River catchment, as well as in the midstream areas of the Sahe River basin (Figure 8). Samples carrying these anomalies were collected from locations drained from rocks dominated by the Aitutu and Wailuli Formations, which may contain hydrothermal mineralizations.

The elemental associations reflected in the positive PC4 scores could be significantly influenced by the presence of Cu-Zn deposits, as well as carbonate and manganese minerals [39,100,105]. Anomalous values of this component were identified in the midstream areas of the Laclo do Sul, Clerec, and Sahe River catchments (Figure 8). These anomalies were most pronounced in samples derived from rocks dominated by the Aitutu and Wailuli Formations, along with the Bobonaro Complex, indicating potential mineralization-related environments.

5.3. Prediction of Target Areas for Future Research and Exploration

Four potential target areas for mineral deposits were identified in the study area and proposed for further study and exploration, as illustrated in Figure 9. Target areas A and B show evident distributions of anomalous values of Cr and elemental assemblages with the negative PC2 scores, as shown in Figure 6 and Figure 8. The distributions of anomalous values of Cu and Zn as well as the elemental association with negative PC3 scores (Figure 6 and Figure 8), corresponded well with the delineation of target area C. In addition, the proposed boundary of target area D is consistent with the distribution of anomalous Ba values and anomalous scores of multi-element relationships in the positive PC3 and PC4.

6. Conclusions

The characteristics of Cr, Cu, Zn, and Ba in the river sands of the study area fell outside a normal distribution. The threshold estimated for these elements using the mean+2STD approach failed to yield adequate values, whereas the median+2MAD method delivered low threshold values, leading to the detection of the greatest number of outliers. This study presents the findings that the TIF and PCA methods are highly applicable and effective for calculating appropriate geochemical thresholds to separate anomalies from background values and identify relevant elemental associations. These approaches have proven useful for delineating target areas for mineral deposits, resulting in reliable outcomes [83,84,112,113].

The geochemical analysis of the river sand samples across the study area reveals substantial variation in element concentrations, pointing to a complex interplay of geology, lithology, and mineralogy. Anomalies of Cr and Ba in midstream areas of the Laclo do Sul and Sahe Rivers align with the presence of mafic minerals rich lithologies with or without chromite deposits and carbonate rocks, while elevated Cu and Zn concentrations suggest potential Cu-Zn mineralization zones, especially within the Wailuli and Aitutu Formations. Additionally, anomalies and elevated concentrations of Ba and Mn in several areas could be associated with the presence of barite and manganese minerals, which may be indicative of hydrothermal activity. Geochemical evidence, lithological compositions, and anomalous geochemical distributions underscore the presence and distributions of mineralization-forming environment associated with the Lolotoi and Bobonaro Complexes, as well as the Aitutu and Wailuli Formations. These geological formations sourced from both continental and oceanic parent rocks typical of active tectonic regions and are possible prospective for hosting economically viable mineral deposits.

The occurrence of hydrothermal mineralization associated with the Gondwana Megasequence has been reported previously by Vicente et al. [39]. Further geochemical investigations and mineral exploration campaigns are recommended in areas underlain by the Wailuli Formation, particularly within the proposed boundaries of Target Area D (Figure 9). This area is considered a potential source of the detected Ba geochemical anomalies and may provide evidence of significant hydrothermal alteration of the associated rocks.