824results about "Seismology" patented technology

Systems and methods for monitoring and evaluating time-series real-time environment data to create a high-resolution, high-fidelity actual (e.g., nowcast) and predicted (e.g., forecast) representation of an environment of interest. In some aspects, the system comprises instructions to obtain a set of real-time environment measurements stored in a data repository corresponding to a time-series capture of environment data across an observational time period, identify one or more precursory signals within the set of real-time environment measurements, determine at least one anomalous precursory signal from the one or more precursory signals that exceeds the corresponding signal threshold, generate a time-series representation of an actual environment state across the observational time period based on the at least one anomalous precursory signal and the set of real-time environment measurements, and display, at a user interface, the generated time-series representation of the actual environment state.

The invention discloses an earthquake prediction method and system based on multi-physics coupling and quantum optimization, and relates to the technical field of earthquake prediction, and the method comprises the following steps: obtaining earth surface deformation observation data, geomagnetic observation data, gravity gradient data, and infrasonic wave monitoring data, and carrying out the preprocessing; performing multi-physics field coupling calculation on the preprocessed earth surface deformation observation data, geomagnetic observation data, gravity gradient data and infrasonic wave monitoring data; optimizing the fault network by adopting a D-Wave quantum annealing technology to find a lowest energy state to obtain a quantum optimization result; and generating an earthquake probability distribution diagram, an earthquake magnitude-time window table and a fault stress field three-dimensional model, thereby greatly expanding the breadth and depth of data, and remarkably improving the comprehensiveness of earthquake prediction.

The invention provides a high-negative-pressure gas extraction drill hole full-life-cycle efficiency evaluation method, and belongs to the technical field of mineral exploitation. A ground stress distribution model is established through microseism monitoring to determine drill hole arrangement parameters; a distributed optical fiber sensing system and a multi-parameter gas flow meter are used for monitoring the stress change around a drill hole and gas extraction data in real time, the coal fracture development degree is obtained in combination with sound wave testing, and all the data are input into a physical numerical value coupling model to calculate a permeability dynamic evolution curve. Establishing a permeability influence coefficient matrix and determining a key parameter weight, constructing an evaluation index system including the extraction amount, the permeability change rate, the drilling life and the coverage range, and performing multi-objective optimization configuration by applying a Pareto optimal solution theory. The technical problem that full-life-cycle dynamic monitoring and comprehensive efficiency evaluation of the high-negative-pressure gas extraction drill hole are lacked at present is solved.

The invention discloses a data center seismic isolation performance detection method based on AI intelligence, and particularly relates to the technical field of seismic isolation performance detection. Earthquake vibration characteristic data are collected in real time on a data center building and equipment, earthquake input characteristics are extracted in combination with a historical earthquake wave database, a seismic isolation performance prediction model is constructed and trained, a foundation shear wave velocity gradient abnormal value and a seismic isolation layer dynamic displacement response deviation ratio are further extracted, a deviation index is comprehensively calculated, and a seismic isolation performance prediction model is constructed and trained. And classification processing is carried out according to the deviation level, especially under the condition of a middle deviation level, foundation physical parameters are dynamically updated, and the model is optimized, so that the accuracy of the prediction model and the self-adaptive capability of the system are remarkably improved, and the shock isolation safety and the continuous operation capability of the data center in a strong shock environment are effectively guaranteed.

The invention relates to the technical field of geological disaster monitoring, in particular to a geological disaster monitoring method and system based on a distributed optical fiber vibration sensing system, and aims to solve the problem of how to solve the interference of the change of an optical fiber and the change of an external environment on a geological disaster prediction result in optical fiber transformation. In order to solve the problem, the geological disaster monitoring method provided by the invention comprises the following steps: determining an area to be expanded according to a monitoring demand and a system distribution path of an original optical fiber; determining an actual distribution path of newly added optical fibers according to the landform characteristics and the geological risk level of the to-be-expanded area; after the newly-added optical fiber is installed, correcting the characteristic data according to the loss condition of the original optical fiber and the transmission characteristics of the newly-added optical fiber, and constructing a prediction model according to the corrected data; and combining the prediction model with the environment information, and determining the type of a disaster occurring in the target area.

The invention belongs to the technical field of data acquisition, and provides an underground pipeline detection data acquisition method and system, and the method comprises the following steps: collecting underground pipeline drawing data of a target region, and determining the information parameters of underground pipelines; performing field exploration, marking a ground outcrop point of the underground pipeline, and preliminarily judging the trend of the pipeline; detecting the underground pipeline through an electromagnetic method or a ground penetrating radar, and determining the position and the burial depth of the underground pipeline; reflected and scattered sound wave signals propagated through the internal fluid of the underground pipeline are collected through sound wave detection, and the state of the internal fluid is evaluated; fusing different detection data to generate an underground pipeline space model; by comprehensively using the electromagnetic method, the ground penetrating radar and the sound wave detection technology, the position, the burial depth and the internal fluid state information of the underground pipeline can be comprehensively collected, the physical position of the pipeline is determined, the state of the internal fluid of the pipeline is evaluated, and the accuracy and the reliability of the detection result are effectively improved.

The invention relates to a computer-implemented method for identifying at least one area of interest from a region of interest. The method comprises: obtaining a plurality of geo-information data over the region of interest for a geological time interval, the geo-information data comprising at least carbonate reference locations wherein marine carbonate sediments are observed, obtaining at least one paleo-digital elevation model of the region of interest, providing an Earth system model with a plurality of input sets of modeling parameters and obtaining output sets of paleoenvironmental parameters, determining a plurality of representative paleoenvironmental parameters for each carbonate reference location from the plurality of output sets of paleoenvironmental parameters, grouping at least a part of the carbonate reference locations into at least one group of carbonate reference locations defined as carbonate factory, and computing a susceptibility of occurrence of the carbonate factory.

The invention discloses an underground space surrounding rock excavation damage area testing method, which belongs to the technical field of rock mass damage testing, and comprises the following steps: sampling rocks with different depths, carrying out ultrasonic testing, and determining rock sample attenuation characteristics and wave velocity; determining a comprehensive damage index range of the rock samples with different damage grades; then, sensors are arranged in the excavation process to collect ultrasonic and acoustic emission data; then, an initial calculation model is constructed, and a surrounding rock three-dimensional wave velocity model is obtained through inversion of collected data; and finally, determining a damage grade according to the comprehensive damage index, and marking in the model to realize damage area testing. According to the method, the mapping relation between the damage degree and the wave velocity change is established, the comprehensive damage index range threshold value of different subareas is provided, quantitative and gradable classification is achieved, the three-dimensional wave velocity model is inversed based on test and monitoring data, and three-dimensional presentation and grading labeling of the damage area are achieved.

The invention discloses a drilling construction intelligent decision support and risk early warning method, device and equipment, and the method comprises the steps: collecting drilling sound wave response data, carrying out harmonic analysis to recognize a reservoir resonance node, and constructing a reservoir energy conduction network; a fluctuation propagation path is tracked based on an energy conduction network, and a construction risk area is identified through stress wave spectrum features; porosity and permeability data are extracted for phase difference analysis, a seepage-construction mismatch factor is generated, and a construction optimization window is actively generated; constructing a geological potential energy gradient field to determine an overflow influence range, and performing tensor coupling on multi-dimensional risk factors to generate a three-dimensional construction decision matrix; an optimal construction scheme is generated through energy minimization and risk-efficiency dynamic balance processing; construction parameter response changes are monitored in real time, abnormal parameter combination symptoms are recognized, a grading risk early warning instruction is generated, and intelligent decision support and risk early warning of drilling construction are achieved.

Disclosed are a device and method for detecting and identifying shallow-stratum foreign objects based on distributed acoustic sensing. The device includes a vibrating sensitivity-enhanced fiber optic cable, a distributed acoustic sensing demodulator, a vibrator system, a vibration data processing unit, a velocity structure inversion unit and an AI-aided locating and identification unit. The distributed acoustic sensing demodulator transmits vibration signals to the vibration data processing unit for preprocessing to obtain waveform signals; the velocity structure inversion unit performs inversion to obtain the shallow-stratum underground velocity structure; positions of shallow-stratum foreign objects are identified through the underground velocity structure and abnormal waveforms; and the types of shallow-stratum foreign objects are identified. A distributed acoustic sensing method for fiber optic cables is used to obtain vibration signals, velocity imaging is performed on a shallow-stratum velocity structure, and AI is utilized to achieve locating and type identification of shallow-stratum underground foreign objects.

A fiber optic distributed sensing system and method. The fiber optic distributed sensing system includes: an optical fiber extending through an underwater gas field; an interrogator connected to the optical fiber so that light transmitted through the optical fiber is capable of being sensed by the interrogator; and a computer system configured to obtain sensor data captured by the interrogator and determine an amount or a characteristic of gas within the underwater gas field based on the sensor data. The method includes: capturing sensor data using a fiber optic distributed sensor extending through an acoustic field; performing a wavelet transform on the sensor data in order to obtain spatiotemporal data; and matching the spatiotemporal data to an event template of a predetermined set of event templates in order to identify a spatiotemporal event.

The invention provides a shallow-buried tunnel blasting surface vibration waveform prediction method and system, and relates to the technical field of surface vibration waveform prediction, and the method comprises the steps: collecting geological, blasting and environmental parameters of historical and current blasting, and forming a standardized parameter set through three-stage index storage, abnormal value screening and standardization processing; establishing a mapping relation between blasting parameters and a vibration waveform time domain sequence based on an LSTM neural network, and predicting a current blasting vibration waveform; fusing the geological condition influence factors, the environmental sensitivity influence factors and the predicted waveform features to generate a comprehensive safety assessment index; and according to a matching result of the index and a multi-level safety threshold value, the risk is divided into four levels of safety, low risk, medium risk and high risk, and differential management and control are executed, so that accurate prediction and dynamic regulation and control of the blasting vibration risk are realized. According to the invention, the vibration wave propagation process in the shallow tunnel blasting construction can be accurately simulated, so that the influence of blasting can be better evaluated.

The invention relates to the technical field of optical signal processing and analysis, and particularly provides a submarine optical fiber vibration event detection method based on credibility weighting, and the method comprises the steps: obtaining an optical pulse echo matrix according to a received signal, and carrying out the decomposition of the signal through a low-rank and sparse decomposition model. A credibility coefficient is introduced to measure the reliability of the data, an optimization model is established, and the credibility coefficient is set to be a tide credibility coefficient or a natural disaster credibility coefficient according to the marine environment. Solving the optimization model to obtain a sparse item estimation matrix corresponding to the sparse item; and detecting a natural disaster event and a vibration event in the received signal according to the sparse term estimation matrix. According to the method, reliability evaluation of the signals can be dynamically adjusted according to different marine environments, noise and real vibration events are effectively distinguished, high robustness is kept in the complex and multi-interference marine environment, the method is not prone to being affected by interference sources, and event detection precision and accuracy are good.

The invention belongs to the technical field of mine safety and engineering earthquake monitoring, and particularly relates to a mine earthquake earth surface vibration intensity prediction method and system based on an improved sine and cosine algorithm, and the method comprises the steps: firstly constructing a mine earthquake intensity prediction model containing geometric attenuation, inelastic attenuation and an earthquake source energy item, and then constructing a target function based on measured data, and optimizing model parameters by adopting an improved sine and cosine algorithm. The algorithm improvement comprises the following steps: adaptively adjusting a search step size through a dynamic amplitude exponential decay strategy, and balancing global exploration and local development capabilities; and a periodic random restart mechanism is introduced, and the position of the individual with the worst fitness is reset to avoid premature convergence. Through the combination of the physical model and the intelligent optimization algorithm, the problems of fixed function form and low parameter optimization efficiency of the traditional empirical model are solved, and high-precision prediction of the mining induced earthquake surface vibration intensity is realized.

The invention discloses an acoustic emission signal noise reduction and feature extraction method and system suitable for a deep roadway, and relates to the technical field of safety monitoring of deep mineral resource mining, and the method comprises the specific steps: arranging an acoustic emission sensor array along the deep roadway, collecting multi-source data, converting the multi-source data into digital signals, and storing the digital signals; identifying an interference type through a wavelet packet decomposition and environment correction algorithm; self-adaptive noise reduction is carried out by using a complexity sensitive penalty algorithm; time domain and frequency domain features are extracted, coupling parameters are calculated, and time domain and frequency domain features are obtained through Hilbert-Huang transform; and finally, through principal component analysis dimensionality reduction and mutual information entropy screening, constructing a feature vector and transmitting the feature vector to a safety early warning system. According to the invention, the processing precision and reliability of the acoustic emission signal are improved through the multi-source signal acquisition module, the interference identification module and the adaptive noise reduction module; signal characteristics are comprehensively described through algorithm optimization, key characteristics are output through characteristic optimization, real-time monitoring and early warning are achieved through a dynamic damage vector algorithm, and deep roadway construction safety is guaranteed.

The invention provides a TLF-GV signal correlation earthquake magnitude prediction method and system, and a medium, belongs to the technical field of earthquake prediction, and aims at solving the problems that a traditional magnitude prediction model lacks physical constraints, TLF-GV features are not fully utilized, the interpretability is poor, and cooperation with a preorder technology is lacked. Comprising the following steps: extracting a multi-source feature vector of a TLF-GV signal containing a gravity component absolute amplitude, a vibration component relative amplitude and a signal phase; based on the G-R law, constructing the positive correlation physical characteristics log10 (GVCIpeak) and BF * Tanomal of the abnormal intensity and the magnitude of the TLF-GV signal; weighting the features according to a Bayesian factor BF value; fitting according to historical samples to obtain a physical empirical formula of positive correlation of GVCI peak logarithm and magnitude as a physical constraint, and constructing a loss function by taking the physical constraint as a regularization item and combining with a mean square error of a magnitude prediction task; and constructing an XGBoost model, and training according to the training set to obtain a prediction model for earthquake magnitude prediction. According to the method, quantitative prediction of earthquake magnitude is realized based on multi-source features of TLF-GV signals in combination with a physically constrained XGBoost regression model.

The invention relates to the technical field of intelligent operation and maintenance of a power system, in particular to an intelligent early warning method and system for preventing external damage of a power transmission line. The method comprises the following steps: acquiring acoustic data of a power transmission line through an acoustic sensor; performing voiceprint feature sparse reconstruction according to the transmission line acoustic data to obtain voiceprint feature data; performing sparse causal graph matching on the voiceprint feature data to obtain sound source behavior causal graph data; performing double-domain attention cross recognition according to the sound source behavior causal atlas data to obtain sound source separation recognition data; and performing scene map generation on the sound source separation identification data to obtain sound source scene map data. According to the invention, by constructing the nested causal chain graph and fusing the propagation weight mechanism, the development evolution and multi-stage trigger relationship of the external damage event of the power transmission line can be accurately captured. Compared with a traditional single-point detection method, the method has higher chain reasoning ability and early risk identification ability, and the response foresight and causal interpretability of an early warning system are effectively improved.

The invention discloses a rock mass fracture risk early warning method, system and device based on acousto-optic data fusion and a storage medium, and the method comprises the steps: collecting acoustic emission energy, acoustic emission counting, DIC data and other acousto-optic monitoring data of a rock mass test piece, and calculating to obtain an acoustic emission b value, an acoustic emission energy variance, strain field differentiation data and other multi-dimensional precursor indexes; cUSUM-Pettitt is used for cooperatively checking and recognizing mutation points, confidence and physical mutation amplitude are combined to divide multiple early warning levels, a classification model is trained through a LightGBM algorithm, and accurate early warning of the rock mass fracture risk is achieved. Multi-source physical field information is fused, the limitation of single data monitoring is overcome, the early warning reliability and timeliness are improved, and the method is suitable for disaster prevention and control in the fields of mine safety, rock mass engineering and the like.

The invention discloses an earthquake forecasting and tsunami early warning method and system, and the method comprises the steps: monitoring the earth electromagnetic field data in real time, and recognizing and extracting a regional transient electromagnetic signal which accords with a preset abnormal feature as an earthquake precursor signal; taking the position of the precursor signal as a reference, and predicting the earthquake generation time, the magnitude and the potential influence area based on an earth crust stress wave propagation model; when the early warning condition is met, earthquake early warning information containing risk avoiding guidance is generated and issued, and if the prediction area relates to the coastal area and the earthquake magnitude reaches a threshold value, tsunami early warning information is synchronously generated and issued. The corresponding system comprises a geomagnetic monitoring module, a forecast analysis module and an early warning issuing module. According to the method, pre-earthquake prediction based on the physical precursor is realized, the prediction time is remarkably prolonged, the problems that earthquake and tsunami early warning information is separated and published lags are solved, and the disaster response capability is improved.

The invention discloses an optical fiber positioning method and system based on an environment compensation vector algorithm model. The method sequentially comprises the steps that a quantitative impact hammer is selected, and a distributed vibration sensor is calibrated; collecting vibration vectors of the optical fiber measurement points in different environments; learning the environment compensation vector algorithm model, and determining a vibration compensation vector and a burial depth prediction formula; a quantitative impact hammer is used on the spot to hit the ground along the optical fiber and the trend, and the optical fiber burial depth and the trend are calculated through an environment compensation vector algorithm model. The system comprises a quantitative impact hammer, a distributed vibration sensor, an environment compensation vector algorithm model mobile terminal APP and an optical cable routing management system. According to the invention, measurement errors caused by environmental factors are effectively corrected, so that the optical fiber positioning method and system can still maintain high precision in a complex and changeable environment, and the positioning reliability is remarkably improved; through cooperation among different facilities, a systematic solution is provided for communication optical cable measurement.

The invention provides a deep coalbed methane reservoir pore parameter prediction method based on geologic feature constraint, and relates to the technical field of coalbed methane reservoir development, and the method comprises the steps: building a deep coalbed methane reservoir rock physical model of which a crack is filled with calcite by using a DEM model based on the calcite content of the filled crack and calcite elastic parameters; elastic parameters of the deep coal bed gas reservoir rock physical model are obtained; based on the elastic parameters of the rock physical model of the deep coalbed methane reservoir, simulating and generating a seismic record of the target coalbed methane reservoir by using a response relationship between seismic wave propagation and the elastic parameters of the rock physical model of the deep coalbed methane reservoir; and based on the simulated seismic record, the actual seismic record and the prior data of the pore parameters, performing Bayesian iteration inversion by using a pre-constructed inversion objective function to obtain a prediction result of the pore parameters corresponding to the target coalbed methane reservoir. The prediction accuracy of the pore parameters is improved.

The invention provides a rock burst prediction method and device, computer equipment and a storage medium, and belongs to the technical field of rock burst monitoring, and the method comprises the steps: generating support resistance-micro-seismic time sequence data according to a daily micro-seismic index and a mine pressure index of a target area; extracting a time sequence characteristic index of the support resistance-microseismic time sequence data; analyzing the precursor response characteristics of the rock burst as physical early warning indexes; fusing the physical early warning index and the time sequence characteristic index to obtain a target characteristic; an LSTM-NOA time sequence early warning model is constructed; the LSTM-NOA time sequence early warning model is trained, and a rock burst prediction model is obtained; and inputting the target features into a rock burst prediction model to obtain a rock burst risk level prediction result. In this way, professional domain knowledge and historical experience summarization are fully fused with deep learning, and the accuracy and real-time performance of rock burst monitoring and early warning are improved.

The invention provides a high-stress rock mass disturbance catastrophe real-time early warning method based on a fracture comprehensive signal, which belongs to the technical field of geotechnical engineering, and constructs a comprehensive acoustic parameter ASCP by fusing various parameters such as amplitude, dominant frequency, energy distribution, crack fractal characteristics, rock mass fracture event density and the like in an acoustic emission signal. The calculation process is combined with contribution weights of different parameters to the destruction stage, and weight values are jointly calibrated through data analysis. On the basis of a large amount of engineering field monitoring and indoor true triaxial test data, the evolution law of the ASCP in the rock mass damage process is analyzed, multiple early warning thresholds of an ASCP evolution curve starting early warning threshold and an ASCP catastrophe early warning threshold are provided, and the early warning accuracy and timeliness are improved. According to the early warning method, various parameters of sound signals are integrated, a multi-threshold early warning method is established, the contribution degrees of various main sensitive parameters of high-stress rock mass disturbance damage sound emission are integrated, and the early warning accuracy, timeliness, high efficiency and economical efficiency are improved.

The invention provides a TLF-GV signal correlation earthquake three-element joint probability prediction method and system, belongs to the technical field of earthquake prediction, and aims to solve the problems that traditional three-element prediction lacks joint fusion, evidence conflicts are difficult to process, and weights are fixed. Comprising the following steps: extracting physical features of TLF-GV signals, carrying out preorder three-element prediction results and model credibility parameters, splicing the physical features into feature vectors, inputting the feature vectors into a dynamic weight prediction model constructed by a shallow MLP, and outputting time, magnitude and position weights; dividing the three elements into 3 * 3 * 3 = 27'time-magnitude-position 'propositions, generating basic probability assignment (BPA) of each proposition based on weights, and quantifying evidence contradictions through a conflict coefficient K; and adopting a Dempster combination rule to obtain a joint BPA, calculating a mean value of a reliability function and a likelihood function of each proposition as a comprehensive probability, and extracting a maximum probability proposition as a coupling result. According to the method, the scientificity and practicability of strong earthquake short-term and temporary prediction are remarkably improved.

The invention discloses an anti-clogging rapid laying construction method for municipal drainage pipelines in a pluvial area, and relates to the technical field of municipal engineering. According to the method, through construction early-stage investigation and pretreatment, a high-pressure jet grouting pile and a steel sheet pile are combined to form a water interception curtain and vacuum well point dewatering is adopted, and a pipeline with a nanometer hydrophobic coating and a flow guide protruding edge is prefabricated; during rapid excavation, rapid hardening concrete is used for supporting, and a prefabricated module foundation with a permeable blind pipe is laid; laser positioning is adopted for hoisting, and double sealing is adopted for interfaces; graded gravel and a curing agent are used for backfilling, and ground penetrating radar is matched for detection; acceptance inspection is combined with a closed water test and an endoscope, and an intelligent monitoring device is installed to be connected into an intelligent system. The problems that construction efficiency is low and pipelines are prone to clogging in rainy areas are solved, the construction period is shortened by 30% or above, the initial clogging rate is reduced by 60%, and the method is suitable for drainage projects in areas with the annual precipitation being 800 mm or above.

To provide an S-wave velocity estimation system, a program, and a method for analysis which can rapidly and accurately estimate the average S-wave velocity from the ground surface to a depth of at least 30 meters.SOLUTION: An S-wave velocity estimation system includes: acquisition means for acquiring the horizontal-to-vertical spectrum ratio of a microtremor in a first position; estimation processing means for acquiring the average S-wave velocity from the ground surface to a depth of 30 meters in the first position, by providing the acquired horizontal-to-vertical spectral ratio of a microtremor in the first position and a microtopographic classification in the first position as inputs to a model trained by a machine learning to estimate the average S-wave velocity of the ground; and output means for outputting the aquired average S-wave velocity.SELECTED DRAWING: Figure 2

The invention relates to the technical field of roadway surrounding rock stability detection, and discloses a water immersion roadway surrounding rock loosening circle detection method and system, and the method comprises the steps: drilling small-angle inclined drill holes in two sides of a roadway, and injecting pressure water to form a water immersion saturated environment; sound time data are collected through an ultrasonic probe, and a wave velocity-hole depth curve is constructed; identifying a wave velocity gradient abrupt change area based on attenuation characteristics of surrounding rock fractures to sound waves; the inner boundary and the outer boundary of the loose circle are dynamically judged through the wave velocity gradient change rate, and finally a detection report containing the loose circle range is generated. By actively simulating the water immersion coupling environment and combining gradient change rate analysis, the problem of inaccurate boundary recognition caused by water influence of a traditional acoustic wave method is solved, and the accuracy and reliability of a detection result are remarkably improved.

To enable estimating a location of people requiring rescue during an earthquake without the people requiring rescue having to request rescue by themselves, and to achieve it by keeping costs down.SOLUTION: Provided is an earthquake rescue-requiring-person location estimation system 1 that estimates a location of people requiring rescue after an earthquake occurs, comprising: a building damage level estimation unit 35 for estimating a level of damage to buildings in each of a plurality of areas within a region which is an estimation target based on the intensity of seismic motion; a rescue-requiring-person number estimation unit 37 that estimates the number of people requiring rescue in each of the plurality of areas based on the level of damage to buildings in that areas and the number of people staying in that areas, which is the number of people whose positions have not changed; and a rescue-requiring-person information display unit 39 that displays information related to the number of people requiring rescue for each of the plurality of areas.SELECTED DRAWING: Figure 1

A method can include receiving property data for a simulation model; analyzing the property data with respect to geometry of the simulation model to select a matrix preconditioner scheme using a machine model; and executing a computational simulator that implements parallel processing based on application of the matrix preconditioner scheme to a matrix representing the simulation model to generate simulation results based on the property data.

The constructing of a natural fracture model of a secondary reservoir in a field by using the information from a primary reservoir within a stacked 3D geological model. A fracture model may be constructed for a primary reservoir that incorporates a specific paleo-stress regime and information from a stacked 3D geological model, a deformation model, and a geomechanical model. Parameters such as a deformation model, brittleness index, and stress model may be extracted from the fracture model for a primary reservoir. A fracture model for the secondary reservoir is then constructed from the extracted parameters, and the fracture model is used to identify locations in the secondary reservoir for drilling and other operations.