Comprehensive evaluation and zoning study of potential geothermal water resources in Qinghai Province, northeastern margin of the Tibetan Plateau

The geothermal resources in Qinghai Province are endowed with superior potential, yet overall exploration remains at a relatively low and spatially uneven level. Most sedimentary basins and structural fault zones within the province have not undergone systematic exploration, leading to relatively high exploration risks. To address this challenge, this study developed a novel semi-quantitative methodology that integrates basin heat storage and tectonic heat control theories, breaking through the traditional “heat-following” exploration paradigm. The study divides the province’s hydrothermal resources into 16 potential sedimentary basin (conductive-type) zones and 32 potential fault zones (mountain uplift convective-type). Based on genetic elements of “source-pathway-cap-reservoir-flow,” a multidimensional evaluation index system was established. Innovatively combining the analytic hierarchy process (AHP) and principal component analysis (PCA), this research quantitatively assessed the resource potential of each zone and classified the potential levels using the quartile method. By integrating the evaluation results from both approaches, a total of 32 geothermal resource prospective areas were delineated across the province, including 10 sedimentary basin-type and 22 fault zone-type areas. Building on this, and closely considering resource potential, population distribution, regional development plans, and preliminary environmental screening, 10 target areas were prioritized for early-stage exploration deployment. The research outcomes provide a systematic scientific basis for strategic planning and governmental decision-making regarding the exploration and development of geothermal resources in Qinghai Province.

1 Introduction

Geothermal energy, as a clean, stable, and sustainable renewable energy source, is strategically important to the global low-carbon energy transition. China is rich in geothermal resources with broad prospects for development and utilization. Systematically advancing the exploitation and use of geothermal resources is of great significance for alleviating energy supply–demand contradictions, driving high-quality regional economic development, and strengthening the national energy security barrier (Lin et al., 2013; Hu et al., 2013; Pang et al., 2020; Qiu et al., 2022; Wang and Gong, 2017). Located on the northeastern margin of the Tibetan Plateau at the forefront of the India–Eurasia plate collision zone, Qinghai Province experiences intense neotectonic activity and possesses favorable deep heat flow conditions (Li J. et al., 2025; Chen et al., 2025; Zhang et al., 2025). This has led to the formation of diverse types and abundant reserves of geothermal resources, making it one of the core regions for both national ecological security and clean energy bases.

Guided by the “dual carbon” goals and leveraging its superior geological conditions and policy support, Qinghai Province has implemented over 80 geothermal survey and research projects. Significant progress has been made in areas such as the exploration of hydrothermal resources and hot dry rock in the Gonghe–Guide Basin and the utilization of high-salinity geothermal resources in the Xining Basin (Tang et al., 2020; 2023; Yun et al., 2020; Zhang C. et al., 2020; Zhang S. et al., 2020; Zhang S. et al., 2021; Zhang Y. et al., 2021; Lin et al., 2023; Zhang et al., 2024; Liu et al., 2024). Regarding resource potential assessment, Wang et al. (2017) have conducted classified evaluations of different types of geothermal resources in China. Qinghai Province has also carried out assessments of the current resource status, focusing on key basins (Yang et al., 2015; Zhang et al., 2015; Lu et al., 2024). However, traditional assessment standards and case studies have predominantly focused on calculating heat reservoir resources and fluid quality (Natural Resources and Territory Spatial Planning, 2011; 2020; Technical Committee on Standardization of Geothermal Energy ofthe Energy Industry, 2020; 2021; Qiu et al., 2019; Wang et al., 2024; Zhu et al., 2024), failing to systematically integrate the genetic elements of the geothermal system “source, pathway, caprock, reservoir, and fluid” for a comprehensive evaluation of potential.

Addressing the needs of Qinghai Province’s geothermal resource development planning and the shortcomings of existing evaluation methods, this study aims to break through traditional approaches by constructing a semi-quantitative comprehensive evaluation system applicable to both conductive resources in sedimentary basins and convective resources in uplifted mountainous areas. The innovation of this research is reflected in three aspects: 1. proposing a conceptual model for optimizing exploration targets that integrates the theories of “basin heat storage” and “tectonic heat control;” 2. establishing a multi-factor, semi-quantitative evaluation index system based on the genetic elements of the geothermal system; 3. adopting a combined subjective-objective (analytic hierarchy process (AHP) and principal component analysis (PCA)) analytical method to increase the reliability of potential zoning results. The research findings aim to provide a direct scientific basis for the exploration and planning of geothermal resources in Qinghai Province, as well as to offer a new methodological reference for resource assessment in similar regions.

2 Overview of geothermal resources in the study area

2.1 Distribution of geothermal resources

Qinghai Province, located on the northeastern margin of the Tibetan Plateau, possesses a rich and diverse geothermal resource pattern shaped by its unique geotectonic background and superior geothermal geological conditions. All three types of geothermal resources—shallow geothermal energy, underground hot water, and hot dry rock—have been identified. Among them, hydrothermal resources are widely distributed across all eight prefectures/cities of the province, and are characterized by shallow burial and high temperatures. A total of 91 natural thermal spring outcrops have been discovered in the province (Figure 1), including 20 hot water points (60 °C∼90 °C), 14 warm water points (40 °C∼60 °C), 24 tepid water points (25 °C∼40 °C), and 33 low-temperature water points (below 25 °C). Additionally, there are 84 geothermal anomaly wells, including seven medium-temperature geothermal wells (≥90 °C, mainly distributed in Hainan Prefecture), 25 low-temperature hot water wells (60 °C∼90 °C), 21 low-temperature warm water wells (40 °C∼60 °C), 21 low-temperature tepid water wells (25 °C∼40 °C), and 10 wells with significant geothermal gradient anomalies that are not pumped. According to the latest resource assessment, the total mid-deep geothermal energy resource in the province amounts to 475.754 billion tons of standard coal. Calculating with a 10% recovery rate for geothermal energy from the thermal reservoir, the total recoverable resource is 47.5754 billion tons of standard coal, of which the controlled recoverable resource is 172 million tons, and the inferred recoverable resource is 47.404 billion tons of standard coal (Lu et al., 2024).

Figure 1

Figure 1. Distribution map of hot springs in Qinghai Province. (Note: The data are sourced from the latest hot spring survey data (up to 2023), and the spring locations have been verified through field GPS checks.)

2.2 Types of geothermal resources

Based on the hydrodynamic characteristics, storage conditions, geological structure, and heat conduction systems of underground hot water in Qinghai Province, combined with regional geothermal genetic theory, and starting from the key elements controlling geothermal resource enrichment and occurrence, the hydrothermal resources are classified into two basic types according to the geological elements of “source–pathway–cap–reservoir–fluid” and the characteristics of heat transfer, storage, preservation, and dissipation: sedimentary basin conductive type and uplifted mountainous area convective type.

2.2.1 Sedimentary basin conductive type geothermal resources

Controlled by recent tectonic activity, the strong uplift of the Qilian, Kunlun, and Tanggula mountains led to the relative subsidence of the plains between them, forming a series of basins of varying sizes, such as Qaidam, Haiyan, Gonghe, Xining, Guide, and Tongren. These basins are filled with thick clastic sedimentary sequences, providing favorable space for the formation and occurrence of underground hot water (Zhang et al., 1992; Song et al., 2003; Du et al., 2011; Li et al., 2013). The basins are often bounded by deep faults, which are large-scale, long-extending, and often form a series of secondary fault zones, serving as good channels for fluid migration. Atmospheric precipitation and snowmelt infiltrate through the mountainous areas surrounding the basins and are heated conductively by the surrounding rocks during groundwater flow toward the basin interior. The water accumulates in layered thermal reservoirs within coarse clastic layers with high porosity and permeability, overlain by low thermal conductivity caprocks providing insulation, ultimately forming sedimentary basin conductive-type underground hot water resources (Figure 2). Based on the basins delineated in the “Qinghai Regional Geology Annals,” this type of resource in the province mainly occurs in 16 basins, including Qaidam, Gonghe, Xining, and Tongren. Among these, 13 basins have already discovered thermal springs or have geothermal wells implemented (Table 1). These basins generally feature clear sedimentary boundaries, substantial sedimentary thickness, anomalous geothermal gradients, and relatively high terrestrial heat flow values. Relatively high-temperature underground hot water has been explored in basins like Gonghe, Guide, and Xining.

Figure 2

Figure 2. Schematic diagram of the genesis of sedimentary basin-type geothermal resources.

Table 1

Table 1. Overview of major sedimentary basin-type geothermal resources.

2.2.2 Uplifted mountainous area convective-type geothermal resources

Also known as tectonic fissure type, these resources often manifest as springs on the surface, typically distributed linearly along tectonic fault zones, and are commonly found in zonal reservoirs formed by marginal fault zones of fault basins. Deep faults cutting through the crust or even extending into the mantle often form fault fracture zones of considerable scale or a series of secondary derivative faults. Geothermal manifestations occur at locations like fault fracture zones or intersections of faults with different orientations (Zhou et al., 2020; Zhou, 2007; Li X. et al., 2025). Atmospheric precipitation and snowmelt infiltrate and recharge in mountainous areas. Under conditions of generally low rock permeability, deep circulation and heat absorption occur primarily along fractures and fracture zones. Driven by topographic relief and hydraulic gradients, heated fluids ascend along highly permeable fracture zones and discharge as springs at favorable locations (e.g., intersections of fault sets), forming typical uplifted mountainous area convective-type underground hot water resources (Figure 3). This study first considered 2 first-order faults, 9 second-order faults, and 20 third-order faults from the “Qinghai Regional Geology Annals,” as well as 24 faults from the distribution characteristics of active faults in Qinghai Province as potential occurrence zones. After removing duplicates and faults without geothermal anomalies, combined with field surveys, 32 fault zones in the province were identified as hosting this type of resource (Table 2).

Figure 3

Figure 3. Schematic diagram of the genesis of convective geothermal resources in uplifted mountainous areas.

Table 2

Table 2. Overview of major uplifted mountainous area-type geothermal resources.

3 Research methods and process

To overcome the limitations of single subjective or objective evaluation methods, this study employs a comprehensive evaluation strategy that combines the analytic hierarchy process (AHP) and principal component analysis (PCA). AHP effectively integrates experts’ empirical judgments on the genetic factors of complex geothermal systems (Saaty, 1990; 2008; Vaidya and Kumar, 2006), while PCA extracts objective statistical patterns from multivariate data. The combination of both methods enhances the scientific rationality of the evaluation results (Abdi and Williams, 2010; Zou et al., 2006). Key indicators closely related to the occurrence of geothermal resources are selected to evaluate the geothermal resource potential of the study area. By comparing and overlaying the results of the two methods, the potential of different types of geothermal resources in the province is categorized into zones, including high-potential, medium-potential, and low-potential zones. All computational processes are implemented using SPSS software.

3.1 Construction of the potential area evaluation structural model

The 16 sedimentary basin geothermal resource occurrence areas in Qinghai Province are the evaluation objects for sedimentary basin conductive-type resources. Based on their geothermal geological characteristics, five first-level indicators (Source, Pathway, Cap, Reservoir, and Water) are selected and further subdivided into 11 second-level indicators: “Source” includes Terrestrial Heat Flow, Presence of Low-Velocity Body at 20 km depth, Basement Lithology; “Pathway” is Basin-Controlling Fault Scale; “Cap” is Caprock Lithology; “Reservoir” includes Reservoir Thickness, Temperature, Lithology, and Recoverable Modulus; “Water” includes Water Richness Grade and Number of Geothermal Fields. A hierarchical model is built using these 11 indicators to evaluate the potential of different basins (Figure 4).

Figure 4

Figure 4. Structural model for potential zoning of sedimentary basin-type geothermal resources.

The 32 fault zones hosting this resource type are the evaluation objects for uplifted mountainous area convective-type resources. Four first-level indicators are selected: “Fault Scale, Springs related to the fault, Fault Activity, and Strata exposed along the fault,” subdivided into nine second-level indicators: “Fault Scale” includes Cutting Depth, Extension Length, Fracture Zone Width; “Springs related to the fault” includes Average Spring Temperature, Number of Springs, Reservoir Temperature; “Fault Activity” includes Activity History, Earthquake Magnitude; “Strata exposed along the fault” is represented by Igneous Rock Proportion. A hierarchical model is built using these nine indicators to evaluate the potential of different fault zones (Figure 5).

Figure 5

Figure 5. Structural model for potential zoning of uplifted mountainous area-type geothermal resources.

3.2 Analytic hierarchy process (AHP)

AHP is a systematic, hierarchical decision-analysis method that decomposes a problem into different constituent factors and constructs a multi-level structural model according to their relationships, ultimately determining the relative weight or ranking of the underlying factors relative to the top-level goal (He et al., 2023; Zhang, 2022). The specific steps are as follows:

1. Construction of judgment matrices: Ten experts in the fields of geothermal geology, hydrogeology, and structural geology within the province were invited to provide scores using the 1–9 scale method, forming an n × n positive reciprocal judgment matrix A. The consistency ratio (CR) for each matrix was calculated, and all CR values were below the threshold of 0.1, indicating acceptable consistency in the expert judgments.

$\mathrm{A}=\left(\text{aij}\right)\mathrm{n}\times \mathrm{n},\text{aij}>0,\text{aji}=1/\text{aij},\text{aii}=1,$

(where aij represents the relative importance of indicator i compared to indicator j).

1. 2. Calculation of weight vectors: The geometric mean ${w*}_i$ of each row of the judgment matrix was calculated, and the vector w* = (${w*}_1,{w*}_2,$ …, ${w*}_n$)^T was normalized to obtain the weight vector w.

${\mathrm{w}}_{\mathrm{i}}=\frac{w_i^{*}}{{\sum }_{k=1}^n{w}_k^{*}},$

where w = (w₁, w₂, … ,w_n)^T. The vector w represents the weights of the respective indicators (Tables 3, 4).

1. 3. Score calculation: The standardized values of each indicator were multiplied by their corresponding weights and then summed to obtain the AHP composite score for each potential zone. The zones were ranked from highest to lowest based on these scores, reflecting the relative quality of metallogenic conditions. This ranking serves as the basis for potential zonation.

Table 3

Table 3. List of indicator weights for the evaluation of potential sedimentary basin-type geothermal resources.

Table 4

Table 4. List of indicator weights for the evaluation of potential uplifted mountainous area-type geothermal resources.

3.3 Principal component analysis (PCA)

PCA reduces dimensionality by transforming multiple variables into a few uncorrelated principal components that reflect most of the information from the original variables, overcoming the limitations of single-indicator evaluation (Mielke et al., 2016; Pang et al., 2018; Zhang Y. et al., 2021). Specific steps are as follows:

1. Construction of the evaluation structure model: This is consistent with the AHP model.

2. Data standardization: The original matrix of m samples and n indicators was standardized to eliminate the influence of different measurement units.

3. Calculation of the correlation matrix and its eigenvalues: The correlation matrix R (n × n dimension) of the standardized data was computed. The eigenvalues λ_k of R (sorted in descending order: λ₁ ≥ λ₂ ≥ … ≥ λ_n ≥ 0) and their corresponding eigenvectors u_k (k = 1, 2, …, n) were calculated.

4. Determination of principal components and calculation of weights: The variance contribution rate V_k and the cumulative contribution rate of the kth principal component F_k were calculated.

${\mathrm{V}}_{\mathrm{k}}=\frac{{\lambda }_k}{{\sum }_{i=1}^n{\lambda }_i}.$

Principal components were extracted based on the criterion of eigenvalues greater than 1 or a cumulative variance contribution rate exceeding 80%. For the basin-type and fault-zone-type evaluations, the first four principal components explained more than 76% and 83% of the original information, respectively (Tables 5, 6).

1. 5. Calculation of principal component scores: The weights in PCA typically reflect the comprehensive influence of each original indicator on the principal components. The scores of each potential zone on the principal components were calculated and used as the basis for potential zonation.

Table 5

Table 5. Variance explanation table for evaluating the potential of sedimentary basin-type geothermal resource.

Table 6

Table 6. Variance explanation table for evaluating the potential of uplifted mountainous area-type geothermal resources.

3.4 Determination of potential zones and prospective areas

To intuitively differentiate potential levels without presupposing the data distribution, this study employed the quartile method to divide the assessment results of both the AHP and PCA into three potential levels: high, medium, and low. Ultimately, the intersection of areas classified as either “high” or “medium” level in the assessment results of both methods was identified as a prospective geothermal resource area, thereby increasing the reliability of the results.

4 Calculation results

4.1 Zoning of potential sedimentary basin conductive-type geothermal resources

Values were assigned to the 11 indicators based on the methods above (Table 7). Terrestrial heat flow data came from 98 data points from the national fourth edition heat flow database for the province (Jiang et al., 2016), 29 from literature, and 8 measured data points. Heat flow values range from 25.5 mW/m² to 157.1 mW/m², with an average of 69.8 ± 26.2 mW/m², higher than the Chinese continental average of 61.5 mW/m². The presence of low-velocity bodies at 20 km depth was determined based on seismic tomography, crustal velocity profiles, and artificial seismic data, identifying their presence under the Gonghe Basin, Qaidam Basin, and Hoh Xil–Tuotuohe Basin. Basement lithology was assigned based on actual exposure or regional geological data. Reservoir temperature is the average of measured values from geothermal wells or calculated values from geothermometers. Reservoir thickness was estimated based on drilling data or as 22% of basement depth. Reservoir lithology was assigned based on porosity development. Recoverable modulus was calculated as the recoverable resource amount divided by the basin area. Water richness grade was determined based on production test data or analogy. The number of geothermal fields was assigned based on the actual count of 25 fields identified from existing exploration projects.

Table 7

Table 7. Basic parameter assignment table for the evaluation indicators of potential sedimentary basin-type geothermal resources.

4.1.1 AHP calculation results

Based on the distribution of AHP comprehensive scores, potential areas were divided using the quartile method: Gonghe Basin, Qaidam Basin, Hoh Xil–Tuotuohe Basin, and Xining Basin are large-potential areas; Tongren Basin, Nangqen Basin, Tanggula Hot Spring Basin, Minhe Basin, Qumarleb–Zhiduo Basin, Menyuan Basin, Haiyan Dongdatan Basin, and Hala Lake Basin are medium-potential areas; Suli Basin, Xinghai–Zeku Basin, Xunhua and Hualong Basin, and Mado Residual Basin are small-potential areas (Table 8; Figure 6).

Table 8

Table 8. AHP potential evaluation results table.

Figure 6

Figure 6. AHP geothermal resource potential zoning map.

4.1.2 PCA calculation results

The PCA results show: Gonghe Basin, Hoh Xil–Tuotuohe Basin, Tongren Basin, and Xining Basin are large-potential areas; Qaidam Basin, Minhe Basin, Tanggula Hot Spring Basin, Nangqen Basin, Qumarleb–Zhiduo Basin, Haiyan Dongdatan Basin, Xinghai–Zeku Basin, and Mado Residual Basin are medium-potential areas; Hala Lake Basin, Suli Basin, Menyuan Basin, and Xunhua and Hualong Basin are small-potential areas (Table 9; Figure 7).

Table 9

Table 9. PCA comprehensive score table.

Figure 7

Figure 7. Principal component analysis geothermal resource potential zoning map.

4.2 Zoning of potential uplifted mountainous area convective-type geothermal resources

For assigning values to the indicators for the uplifted mountainous area type, fault cutting depth, extension length, and fracture zone width were determined based on geological records, tectonic maps, active fault studies, and exploration reports. Spring count, temperature, and reservoir temperature are based on the latest survey results and previous data. Fault activity history was assigned based on provincial data and categorized into pre-Triassic, Late Triassic, Himalayan, Late Pleistocene, and Holocene. Earthquake magnitude is based on seismic network data. Igneous rock proportion was determined from regional geological maps (Table 10).

Table 10

Table 10. Basic parameter assignment table for evaluation indicators of potential uplifted mountainous area-type geothermal resources.

4.2.1 AHP calculation results

The AHP comprehensive evaluation results were divided using the quartile method: areas ranked 1–8 are large-potential areas, 9–24 are medium, and 25–32 are small (Table 11).

Table 11

Table 11. AHP evaluation results for potential uplifted mountainous area-type geothermal resources.

4.2.2 PCA calculation results

The PCA results were also divided using the quartile method: areas ranked 1–8 are large-potential areas, 9–24 are medium, and 25–32 are small (Table 12).

Table 12

Table 12. PCA evaluation results for potential uplifted mountainous area-type geothermal resources.

4.3 Delineation of geothermal resource prospective areas

Based on the evaluation results of the two resource types, the analytic hierarchy process (AHP) delineated 36 prospective areas (12 sedimentary basins and 24 faults), while principal component analysis (PCA) also identified 36 prospective areas (12 sedimentary basins and 24 faults). By taking the intersection of the results from both methods, a total of 32 geothermal resource prospective areas were ultimately delineated across the province. Among these, 10 are sedimentary basin conductive types, and 22 are mountainous uplift convective types (Table 13; Figure 8).

Table 13

Table 13. List of prospective geothermal resource areas in Qinghai Province.

Figure 8

Figure 8. Zoning map of prospective geothermal resource potential areas in Qinghai Province.

5 Discussion

5.1 Methodological discussion and comparison

The AHP-PCA combined method employed in this study effectively integrates expert knowledge with the objective patterns within the data. The AHP results place greater emphasis on control factors deemed critical by experts (e.g., terrestrial heat flow and fault activity). In contrast, the PCA results more comprehensively reflect the integrated statistical characteristics of all indicators. The intersection of results from both methods increases the reliability of the delineated prospective areas. Compared with previous evaluations in Qinghai Province, which primarily focused on resource quantity estimation, the “genetic factors–multi-indicator–semi-quantitative” system established in this study better reveals the controlling mechanisms and spatial variations governing resource occurrence, providing more direct evidence for target area prioritization.

5.2 Relationship between fault structures and geothermal manifestations

In-depth analysis of the spatial relationship between hot springs and faults indicates that high-temperature, high-flow geothermal manifestations are often not located directly on regional major faults. Instead, they are more commonly found where secondary faults have developed or at intersections of faults with different orientations (e.g., the Guide–Duohemao fault zone). These locations typically feature highly fractured rock with good permeability, which is more conducive to the rapid upflow of deep thermal fluids. In contrast, some large-scale but structurally simple major faults (e.g., the Altyn Tagh Fault F1) show less significant geothermal manifestations.

5.3 Target area prioritization

From the 32 delineated prospective areas, 10 exploration target areas were further prioritized (Figure 9; Table 14). The prioritization process systematically considered the following factors in addition to the resource potential assessment: 1. Location and infrastructure: Prioritizing areas near population and economic centers, such as towns with convenient transportation access. 2. Local demand: Aligning with Qinghai Province’s “Dual Carbon” goals and clean energy industry development plans, prioritizing regions with strong local demand. 3. Preliminary environmental screening: Avoiding environmentally sensitive areas like ecological conservation red lines.

Figure 9

Figure 9. Target area prioritization map for geothermal resources in Qinghai Province.

Table 14

Table 14. List of optimized geothermal resource target areas in Qinghai Province.

5.4 Study limitations and future prospects

The evaluation system established in this study has been successfully applied within the specific geological context of Qinghai Province. However, its direct application to other regions with significantly different tectonic settings may require adjustments to the evaluation indicators and their weights. Future research could explore the following directions: 1. validating and calibrating this evaluation system in other typical geothermal regions; 2. incorporating reservoir numerical simulation to dynamically assess resource extraction potential; 3. integrating full lifecycle economic cost and environmental impact assessments more deeply into the target area decision-making model. The development of geothermal resources must focus on sustainability. Future detailed exploration stages require targeted evaluations of reservoir sustainable exploitation potential and environmental monitoring (e.g., handling of high-salinity fluids, monitoring of induced seismicity, etc.).

6 Conclusion

Qinghai Province boasts abundant geothermal resources with substantial reserves. There are 91 natural hot spring outcrops and 84 geothermal anomaly wells. The total mid-to-deep geothermal energy resource is estimated at 475.754 billion tons of standard coal equivalent, with an extractable resource of 47.5754 billion tons. Based on hydrodynamic conditions, geological structure, and heat conduction characteristics, the hydrothermal resources are classified into two types: sedimentary basin conductive type and mountainous uplift convective type.

This study applies the analytic hierarchy process (AHP) and principal component analysis (PCA) to assess the potential of hydrothermal resources in Qinghai Province. Moving beyond the traditional “follow the heat” approach, it adopts a “Source–Pathway–Cap–Reservoir–Fluid” element framework. Utilizing 11 evaluation indicators for basins and 9 for fault zones, a semi-quantitative assessment was conducted for 16 sedimentary basins and 32 fault zones. This established a multi-factor, semi-quantitative evaluation system, providing a scientific basis for prioritizing exploration targets and effectively reducing exploration risks.

By integrating the evaluation results from both methods, 32 geothermal resource prospective areas were delineated across the province. Comprehensively considering resource potential, locational conditions, and development needs, 10 target areas suitable for further near-term exploration were prioritized. This offers clear scientific guidance for the exploration and development planning of geothermal resources in Qinghai.

The evaluation framework proposed in this study holds methodological significance and can serve as a reference for similar work in other regions, although its specific application requires adjustments based on local geothermal geological conditions. Advancing the exploration and sustainable development of geothermal resources necessitates collaborative innovation across multiple fields, including resource assessment, engineering technology, and environmental management.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material; further inquiries can be directed to the corresponding authors.

Author contributions

WX: Formal analysis, Data curation, Writing – original draft, Writing – review and editing, Investigation. RL: Supervision, Writing – review and editing, Conceptualization, Investigation, Methodology, Funding acquisition, Project administration, Resources, Writing – original draft, Formal analysis. BL: Formal analysis, Validation, Conceptualization, Methodology, Writing – review and editing. JZ: Writing – original draft, Supervision, Conceptualization, Investigation. JC: Methodology, Investigation, Writing – original draft, Visualization, Formal analysis. DZ: Writing – review and editing, Validation, Investigation, Data curation, Formal analysis. XW: Visualization, Writing – original draft, Data curation, Software.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This study was supported by the Qinghai Province Clean Energy Minerals Special Project (Nos 2022013004qj004 and 2023086020qj002).

Acknowledgements

The authors extend their sincere gratitude to the experts who generously gave their time and expertise to this study and its publication.

Conflict of interest

Authors WX, RL, BL, JZ, and XW were employed by the Qinghai Geological Survey Bureau.

The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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