The temperature of well JGS-ZK02 was measured using the well logging system (PSJ-2) equipped with a well temperature probe PS2512, which has a measurement range of 0–65°C and an accuracy of 0.1°C. The temperature was measured according to the Chinese industrial standard DZ/T0080-2010 Coal Geophysical Logging Specification. To ensure that the well temperature was in a (quasi-) steady state, the well temperature was repeatedly logged at a time interval of 12 h after its deviation was less than 0.5°C following the completion of drilling.

The temperature measurement of wells in Qutan and Huang’ao villages was carried out using WD-016A-embedded sensors and TD-016C data acquisition instruments. The embedded sensors have a temperature measurement range of -50–100°C and an accuracy of 0.1°C at 0–80 and 0.5°C at 50–0°C and 81–125°C. These temperature sensors (diameter: 11 mm) were implanted into a PVC cable (diameter: 12 mm) at an interval of 5 m (distance 0–50 m) or 10 m (>50 m).

Thermal conductivity (unit: W/(mK)) is a measure of a given material’s ability to transfer heat, representing the transferred heat per unit area at per unit time when the temperature gradient is 1°C per unit length (Wang et al., 2015). The thermal conductivities of the rock samples were measured using thermal conductivity scanning (TCS) in the East China University of Technology, with a measurement range of 0.2–25 W/(mK) and an accuracy of 3%.

Anand et al. (1973) suggested that thermal conductivity would increase with a decrease in temperature. The temperature at the burial depth of rocks before sampling was probably different from room temperature (25°C), at which the thermal conductivity was measured. Therefore, the measured thermal conductivity at room temperature should be corrected according to the in situ temperature.

Regarding exposed rocks or rocks buried at a relatively shallow depth that could be obtained by drilling, the measured temperatures should be corrected according to their in situ temperature using the following empirical formulas (Sass et al., 1992): K ( 0 ) = K ( 25 ) ( 1.007 + 25 ( 0.0037 − 0.0074 K ( 25 ) ) ) , (1) K ( T ) = K ( 0 ) ( 1.007 + T ( 0.0036 − 0.0072 k ( 0 ) ) ) , (2) where T denotes the in situ temperature of rock samples and K (0), K (25), and K(T) denote the thermal conductivity values of rock samples at 0°C, 25°C, and the in situ temperature, respectively.

For the rocks buried at a depth that could not be acquired through drilling, their thermal conductivity was corrected using the following formula (Cermak et al., 1990; Zang et al., 2002; Xu et al., 2011): K = k 0 ∗ ( 1 + c z ) ( 1 + b ∗ T ) , (3) where k ₀ is the thermal conductivity at the top surface of each rock stratum; c is 0.0015; z is the depth of each rock stratum; and b is 0.0015, 0.0001, and 0.0001 for the upper, middle, and lower crust, respectively.

The crust is generally divided into four layers: a sedimentary layer, and an upper, middle, and lower crust. Its lower boundary—the Moho—is the contact surface between the lower crust and the mantle.

As the primary seismic wave (P wave, V _p ) is distinct in each layer due to different geophysical characteristics, the distribution of V _p in the crust provides evidence of the contact surface between adjacent crust layers and crust structure. In the case where no geophysical analysis of an area has been conducted, the model of Crust 1.0 (Laske et al., 2013) is effective in establishing the crust structure in the area (Liu et al., 2021). Crust 1.0 is a global crustal model specified on a 1 × 1 degree grid and can be used for compiling global artificial seismic data. It provides the crustal structure and related parameters of each crust layer, such as V _p , density and boundary depths.

Since no geophysical study has been carried out in Jinggangshan City, Crust 1.0 is an effective tool for establishing the crust structure of the city. Nevertheless, the Crust 1.0 model has low resolution. To better constrain the crust structure, the geophysical information from a surrounding geoscience transect was collected. The geoscience transect from Menyuan in Qinghang Province to Ningde in Fujian Province (perpendicular distance: ∼340 km) is similar to that of Jinggangshan City (Editorial Committee of Geoscience Transect, 1994; Wang et al., 1995). Though this geoscience transect is a little far away from the study area, it provides more accurate geophysical information.

The surface heat flow (q ₀ ), which is calculated according to the thermal gradient and thermal conductivity (as mentioned in Section 3.1), consists of the crustal heat flow (q _c ) and mantle heat flow (q _m ).

The crustal heat flow is the sum of the radioactive heat generated by the heat production elements in the crust (U, Th, and K). The layer-stripping method is usually used to quantitatively estimate the radioactive heat generated in each crust layer and the whole crustal heat flow. The calculation formulas are as follows: q i = D i · A i , (4) q c = ∑ q i , (5) where q _i is the radioactive heat flow in each crust layer; D _i is the thickness of each crust layer; A _i is the heat production rate of each crust layer; and q _c is the crustal heat flow.

The heat production rate (A) is traditionally calculated based on the U, Th, and K concentrations of representative rock samples using the following formula (Rybach and Buntebarth, 1981; Artemieva et al., 2017): A = ρ ( 0.0952 C U + 0.0348 C K + 0.0256 C T h ) , (6) where A is the heat production rate (μW/m³); ρ is rock density (g/cm³); and C_U (ppm), C_K (ppm), and C_Th (ppm) are the concentrations of U, Th, and K, respectively.

The U, Th, and K contents of rock samples were determined by the Changsha Uranium Geology Research Institute. The K content was measured using a flame atomic absorption spectrophotometer, with a lower detection limit of 0.17%, and the U and Th contents were analyzed using the LA-ICP-MS method, with lower detection limits of 0.003 ug/g and 0.8 ug/g, respectively.

However, this method is not suitable for rocks at depths that are difficult to acquire through drilling. The heat production rate of these rocks can be estimated based on the functional relationship between V _p and A _i (Rybach and Buntebarth, 1984): L n A = 13.7 − 2.17 V P , (7) where A is the heat production rate (μW/m³) and V _p is the primary seismic wave (P wave). In this formula, the in situ seismic velocities should be corrected according to laboratory conditions (100 MPa, room temperature) using the method proposed by Rybach and Buntebarth (1984).

The surface heat flow (q ₀ ) minus the crustal heat flow (q _i ) is the mantle heat flow (q _m ). The mantle heat flow and the q _i /q _m ratio are important for understanding deep geodynamic processes (Wang et al., 2015; Zhang et al., 2018).

Well logging can be used to directly measure the temperature of shallow strata that can be unearthed by boreholes. By contrast, the temperature of deep strata should be calculated using the following one-dimensional steady-state heat conduction formula (Lachenbruch, 1970; Liu et al., 2021): T Z = T 0 + q · D / k − A . D 2 / ( 2 . k ) , (8) where T _z (°C) is the temperature at depth z (m); T ₀ and q are the temperature and heat flow (mW/m²) at the top of each crust layer, respectively; and D, k (W/(mK)), and A are the thickness (km), thermal conductivity (µW·m⁻³), and heat production rate of each crust layer, respectively.

Temperature logging has been conducted for wells JGSDW-B006 and JGS-ZK02 in Huang’ao village and wells JGSDW-B002 and JGSDW-B004 in Qutan village (Table 1). The results are shown in Figure 2. Data fitting was completed using the linear least-squares regression method. The slope of the fitting line was numerically equal to the corresponding temperature gradient (Figure 3 and Table 2). Well JGSDW-B006 had the highest thermal gradient of 26°C·km⁻¹, while well JGS-ZK02 had the lowest thermal gradient of 7.3°C·km⁻¹. The geothermal gradients of the two wells in Qutan village were 9.3°C·km⁻¹ and 13.1°C·km⁻¹.

Five rock samples were collected in total to analyze thermal conductivity. These samples consisted of granites, metamorphic sandstones, slates, limestones, and quartz sandstones (Figure 3).

As shown in Table 3, the metamorphic sandstones had the highest thermal conductivity of 2.75–3.53 W/(mK) (average: 3.79 W/(mK)). Compared with metamorphic sandstones, the thermal conductivity of the Caledonian granites had a similar average of 3.42 W/(mK) but a relatively wider range of 2.06–5.15 W/(mK). The Yanshanian granites had the lowest thermal conductivity, with an average of 2.68 W/(mK). The limestones, sandstones, and slates had approximate thermal conductivity, with an average of 2.77 W/(mK), 2.86 W/(mK), and 2.72 W/(mK), respectively.

The heat flow was calculated based on the thermal gradient and thermal conductivity of the representative rocks (Table 4).

As presented in Figure 3 and Table 4, the strata at valid depths of 0–190 m mainly consisted of metamorphic sandstones and slates, whose corrected thermal conductivity determined using formulas (1) and (2) was 3.8 W/(mK) and 2.73 W/(mK), respectively. By combining their thicknesses, their weighted thermal conductivity was estimated to be 3.57 W/(mK). The surface heat flow of well JGSDW-B006, which equaled the product of the geothermal gradient (26°C·km⁻¹) and the weighted thermal conductivity, was calculated to be 83.52 mW/m². This surface heat flow is much higher than the average heat flow (60.4 ± 12.3 mW/m²) of continental China. This finding indicates a high geothermal background in the study area, which is genetically associated to the formation of geothermal resources. In addition, the surface heat flow of wells JGSDW-B002, JGSDW-B004, and JGS-ZK02 was all low (24.9–35.1 mW/m²), which was attributable to the disturbance of vertical water movement (Fan et al., 2014; Wang et al., 2015).

The surface heat flow value of well JGSDW-B006 (83.52 mW/m²) is plotted in Figure 1, with the others in Jiangxi province. As shown in Figure 1, Jinggangshan City is located in the depression of the upper mantle where the heat flow background is expected to be low; however, the measured heat flow value (83.52 mW/m²) there ranks second in all the measured values in Jiangxi Province. It is suggested that Huang’ao deep fault (NE trending) plays a key role in the high heat flow background. This deep fault is probably a channel of heat transfer from the heat source at a depth.

The geoscience transect from the Menyuan area in Qinghai Province to the Ningde area in Fujian Province is shown in Figure 4. Jinggangshan City is located to the east of the Tanlu (Tancheng-Lujiang) deep fault, where the boundaries of the upper and the middle crusts have blurred due to the strong thrusting process (Editorial Committee of Geoscience Transect C E a E, 1994; Wang et al., 1995). Therefore, the crust was divided into a sedimentary layer and an upper–middle and lower layer. Based on the distribution of the V _p as shown in Figure 4, the depth of the interface between the middle and lower crusts in the study area was determined to be 20.97 km (Table 5). Similarly, the depth of the Moho was 31.97 km, and the lower boundary of the sedimentary layer was 0.62 km.

Based on the Crust 1.0 model, the depth of the interface between the middle and lower crusts was 22.06 km and the Moho depth was 32.9 km. By averaging the results calculated based on the V _p distribution and the Crust 1.0 model, the final depth of the interface between the middle and lower crusts was 21.52 km and the final depth of the Moho surface was 32.44 km (Table 7).

As mentioned in Section 3.2.2, the layer-stripping method can be used to calculate the crustal and mantle heat flow. The three layers of the crust (i.e., the sedimentary layer, the upper-middle crust, and the lower crust) were further divided into several portions according to the well logging (Figure 3) and the isolines of P-wave velocity (V _p ; Figure 4). The thickness (D _i ) represents the vertical distance between the top and bottom isolines of V _p .

For the sedimentary layer (thickness: 620 m), the well logging of well JGSDW-B006 (depth: 500 m) showed that this layer mainly consisted of slates and metamorphic sandstones (Fan et al., 2014), with the former located at a depth of < 62 m and the latter distributed at a depth of >62 m. Therefore, the sedimentary layer was subdivided into a slate layer and a metamorphic sandstone layer. The heat production rates (A _i ) of these two sublayers were obtained based on the U, Th, and K contents of rock samples. Eleven samples were collected using a hammer to analyze the thermal conductivity (Table 6). The average heat production of the metamorphic slates and metamorphic sandstones was 3.63 μW/m³ and 4.17 μW/m³, respectively.

Regarding the upper–middle crust and the lower crust, V _p-layer was the average velocity of the top and bottom isolines of V _p (Table 7). The heat flow on the top surface of each layer was calculated using formulas (4), (5), and (7). It is noteworthy that the V _p-layer of the middle and lower crust was corrected by multiplying the correction factor of 1.01 according to the laboratory conditions (Rybach and Buntebarth, 1984). The heat flow of the mantle and crust was 45.63 mW/m² and 37.89 mW/m², respectively. Therefore, the q _c /q _m ratio was less than 1 (0.83), suggesting a hot-mantle–cold-crust type of lithospheric thermal structure. In other words, the heat flow mainly originated from the heat contribution of the mantle. Based on Table 7, a conceptual model is constructed for the lithospheric thermal structure in the study area (Figure 5). As shown in this model, the values of heat flow and heat production rate in each layer could be easily identified and their changes between each other. The heat production and heat flow values in the upper–middle crust is obviously higher than those of the lower crust. Their values have a decreasing trend with increasing depth.

According to Fan et al. (2014), the constant temperature zone in Jinggangshan City (depth: 20 m) has a temperature of 15°C. The temperature below the constant temperature zone was thereby calculated using formulas (1), (2), (3), and (8). As shown in Table 7, the crust was divided into 10 layers to estimate the deep temperature distribution. The calculated temperature of the Moho was 671.7°C, which is consistent with the estimated temperature of the Moho below the Tanlu deep fault (620–690°C (He et al., 2006)).

The Curie temperature isotherm is the basal surface of the magnetite crust consisting of magnetitic minerals and corresponds to the temperature at which magnetic minerals become paramagnetic (Xiong et al., 2016; Yu-fei et al., 2021). Ilmenites and pyrrhotites lose their ferromagnetism at a temperature of 300–350°C. The demagnetized temperature of magnetite is 585°C (Ross et al., 2006), while that of Co–Ni–Fe–bearing minerals is up to 760–800°C. The Curie temperature isotherm is an important parameter of the lithospheric thermal structure since it can be used to estimate the temperature at a depth (Mayhew, 1985; Ross et al., 2006; Yang, 2015). It is a supplement to heat flow in constructing the lithospheric thermal structure. The Curie point isotherm in China (Xiong et al., 2016) shows that the Curie surface in the study area is at a depth of 19–30 km. Based on the deep temperature distribution of the lithospheric thermal structure listed in Table 7, the Curie surface depth corresponding to the demagnetized temperature of magnetite (585°C) was estimated to be 27 km using the interpolation method. The consistency of the Curie surface depth further dominates the validity of the lithospheric thermal structure and deep temperature distribution in Jinggangshan City.