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Heat flow is a geothermal parameter for indicating the heat source distribution and evaluating geothermal reservoirs. Only 1,230 heat flow points are distributed unevenly in China, mainly concentrated in high‐temperature geothermal and southeast regions. The Songliao Basin is a potential geothermal field in China. Still, only 20 measurement points are known, making evaluating the geothermal genetic mechanism difficult. Sparse data interpolation using deep learning methods is highly accurate and widely used in fields such as image processing. In this work, we propose a deep neural network for predicting heat flow in the Songliao Basin. More than 4,000 global heat flows and 23 geological and geophysical parameters are used as reference constraints for training. The uncertainty error of the prediction is estimated based on the correlation and distance‐based generalized sensitivity analysis. The results show that the maximum heat flow is 85 mW/m2, the average is 67.1 mW/m2, and the error with the measured data is 10.64%. The previous geophysical and geological interpretation results indicate that the heat flow is higher in the west and lower in the east, with high anomalies in the central region, which may be related to the uplift of the deep mantle and the depression of the shallow low‐velocity sedimentary layer. Some high‐temperature melt bodies are in the deep layers, forming the current potential geothermal field. The measured data validates that the DNN is an effective method for predicting regional‐scale heat flow, providing reliable heat source information for evaluating geothermal resources. Plain Language Summary: Geothermal has received extensive global attention as a new renewable energy resource. Geothermal heat flow (GHF) is crucial among various indicators for assessing potential geothermal fields, with over 20,000 measurement points worldwide. However, due to the expensive cost of drilling measurements, the spatial distribution of heat flow points could be more balanced, posing challenges to geothermal exploration and investigation. Traditional heat flow prediction methods rely on interpolation based on sparse measurement points to estimate the regional heat flow distribution. However, spatial variations in the measurement points often affect the prediction results, leading to lower accuracy. Therefore, this study proposes a deep learning approach to establish the complex relationship between heat flow and geological/geophysical features, aiming to estimate the heat flow distribution in the target area. The proposed method has been successfully applied to heat flow prediction in the Songliao Basin, accompanied by an uncertainty analysis technique that provides confidence intervals for the prediction results. These findings offer direct evidence for the geothermal formation mechanisms and thermal evolution laws in the Songliao Basin. Key Points: We propose a deep neural network (DNN) method for predicting terrestrial heat flowCorrelation and Distance‐based Generalized Sensitivity Analysis (DGSA) methods are used to evaluate the uncertainty of DNN predicted heat flow resultsThe prediction of terrestrial heat flow in the Songliao Basin provides direct evidence for deep heat source channels and geothermal genesis mechanisms [ABSTRACT FROM AUTHOR]

The spatial distribution of rock fractures during water injection in hot dry rock (HDR) geothermal is essential information for evaluating geothermal well fracturing. The rock fractures produce anisotropic electrical characteristics, which may facilitate geophysical methods for monitoring fracture distribution. This paper studies the anisotropic magnetotelluric (MT) response in water injection fracturing based on the finite difference method (FEM). It uses the residual phase tensor (PT) method to calculate the characteristics of fracture strike and dip angle. The feasibility analysis of the technique under a different angle and resistivity conditions in geothermal reservoir investigation is discussed in detail. The results demonstrate that the MT can explain the characteristics of anisotropic resistivity structure changes from the water injection fracturing of HDR at the 5 km depth. The time-lapse MT monitoring method can provide a reliable scientific basis for fracture distribution direction and spreading characteristics. [ABSTRACT FROM AUTHOR]

Groundwater flow monitoring is crucial to groundwater management and pollution control. Traditional in-situ hydrological surveys monitor groundwater flow, pressure or temperature changes to infer the actual conditions of the underground, but there are characteristics of stratum destructiveness and difficult to obtain the regional variation characteristics of groundwater. Compared with geophysical methods, it has the advantages of non-destructive detection and regional quantitative interpretation, and indirect understanding of the physical properties of underground media and groundwater. In this paper, we combine the time-lapse electrical resistivity (ERT) and self-potential (SP) methods to monitor the groundwater flow. The ERT result establishes linkages between the resistivity and porewater content of the underground space, and captures the difference in resistivity through the change of underground water content. The SP method provides a direct way to predict groundwater flow according to its movement characteristics. Then, the joint resistivity and potential field are further used to estimate the soil's apparent-permeability. We carry out a real pumping experiment at a hydrological observation site to verify the proposed method. The time-lapse ERT and SP results reveal a significant increase in resistivity but a decrease in permeability during water pumping and groundwater recharge, representing the process of groundwater decline and recovery. The results also matched well with the groundwater level in-situ hydrological data. The study demonstrates that the joint ERT and SP data can provide a direct and reliable way to monitor groundwater flow or other time-lapse hydrogeological surveys in the vadose zone. • The ERT depicts the spatial resistivity related to soil moisture content. • SP method is used to monitor the groundwater based on its flow characteristic. • Joint ERT resistivity and SP data estimate the soil's apparent-permeability. • The combined ERT and SP data can explain time-lapse hydrogeological surveys. [ABSTRACT FROM AUTHOR]

Two-dimensional dense seismic ambient noise array techniques have been widely used to image and monitor subsurface structure characterization in complex urban environments. It does not have limitations in the layout under the limitation of urban space, which is more suitable for 3D S-velocity imaging. In traditional ambient seismic noise tomography, the narrowband filtering (NBF) method has many possible dispersion branches. Aliases would appear in the dispersive image, and the dispersion curve inversion also depends on the initial model. To obtain high-accuracy 3D S-velocity imaging in urban seismology, we developed a robust workflow of data processing and S-velocity tomography for 2D dense ambient noise arrays. Firstly, differing from the NBF method, we adopt the continuous wavelet transform (CWT) as an alternative method to measure the phase velocity from the interstation noise cross-correlation function (NCF) without 2π ambiguity. Then, we proposed the sequential dispersion curve inversion (DCI) strategy, which combines the Dix linear inversion and preconditioned fast descent (PFD) method to invert the S-velocity structure without prior information. Finally, the 3D S-velocity model is generated by the 3D spatial interpolation. The proposed workflow is applied to the 2D dense ambient seismic array dataset in Changchun City. The quality evaluation methods include residual iteration error, horizontal-to-vertical spectral ratio (HVSR) map, and electrical resistivity tomography (ERT). All tests indicate that the developed workflow provides a reliable 3D S-velocity model, which offers a reference for urban subsurface space exploration. [ABSTRACT FROM AUTHOR]

Grotto rock structure detection is precondition of the grotto protection and restoration. As a stable and non-destructive geophysical method, electrical resistivity tomography (ERT) is extensively used to detect the geological structure of the grottoes. However, the traditional ERT measurement scheme is challenging to satisfy the requirements of high accuracy and high resolution in the cliff region. Therefore, we propose a new borehole-to-surface-to-cliff ERT (BSC-ERT) scheme for grotto detection. The sensitivity patterns of five ERT schemes using three configurations (A-MN array, AB-MN array, and AM-BN array) are tested to show the detection capability of different configurations. Two numerical experiments show that the combination of borehole-to-surface ERT (BS-ERT) and surface-to-cliff ERT (SC-ERT) schemes using AM-BN array have better imaging effects and a more reasonable detection range, which is recommended to be implemented for future work. In addition, the numerical experiment also proves that the symmetric effect generated in the AM-BN array in BS-ERT scheme can be suppressed with the combination of the SC-ERT scheme. [ABSTRACT FROM AUTHOR]

Geothermal resource is indispensable as a clean, renewable, stable and cheap resource. Nowadays in China, the Gonghe Basin, located in northeastern Qinghai Province, has been thought to be a promising geothermal area. To explore geothermal energy potential in and around the Gonghe Basin, geophysical means including magnetic and gravity methods were used to plot distribution. Firstly, we inversed Moho depth and Curie point depth in and around the basin using gravity and magnetic data, respectively, through an improved Parker–Oldenburg algorithm. Secondly, seven different thermal models were established, considering radiogenic heat, basement depth, anomalous heat source and simulated corresponding temperature field and heat flow. These were analyzed numerically and we found the high heat flow in the Gonghe Basin co‐acted with radiogenic heat, an anomalous heat source and conductive heat. The distribution of seismic activities indicates that the Langshan–Wuwei–Gonghe Fault might have provided channels for transporting heat effectively. [ABSTRACT FROM AUTHOR]