Gravity measurement and calculation system

Abstract

The invention discloses a gravity measurement and calculation system, and relates to the technical field of geophysical measurement and precise instruments. The system comprises a gravity measurement module, a dynamic correction module, a calculation module and a result output and early warning module. The method comprises the following steps: acquiring 0.1 nGal-level gravity data through an atomic interference type quantum gravimeter array; dynamically updating an energy component correction term epsilon (g) in the empirical equation by using an adaptive learning algorithm based on a Lyapunov stability criterion, and ensuring asymptotic stability of the system under high-frequency data flow; combining a real-time density value of a CRUST1.0 density model after Bouge correction, and accurately calculating the earth radius variation; and finally, through 5G and satellite double-link redundant communication, triggering early warning when severe conditions that delta g is greater than or equal to 2 mu Gal for continuous 30 minutes, and SNR is greater than or equal to 6 dB are met. According to the invention, high-precision quantum sensing, a stability control algorithm and a real-time geophysical model are deeply fused, reliable monitoring of micron-sized change of the earth shape and effective early warning of geophysical events are realized, and the method has high precision, high stability and high engineering practicability.

Description

Technical Field

[0001] The invention relates to the field of geophysical measurement and precision instrument technology, specifically to a system for calculating and warning of changes in Earth's radius based on high-precision dynamic gravity monitoring and adaptive algorithms. Background Technology

[0002] Precise monitoring of minute changes in the Earth's shape is crucial for earthquake early warning, oil and gas exploration, and geodynamics research. Traditional methods, such as satellite altimetry and static gravity field models (e.g., EGM2008), suffer from inherent limitations, including long update cycles and an inability to reflect transient geophysical processes in real time. Although the advent of quantum gravimeters has improved gravity measurement accuracy to 0.1 nGal (1 nGal = 10⁻¹¹ m / s²), the stable and reliable conversion of high-frequency, high-precision gravity data streams into geophysically meaningful indicators of Earth deformation, and ultimately effective real-time early warning, remains a long-standing technical challenge in this field. Existing technologies generally lack an integrated system solution that deeply integrates high-performance sensors, dynamic geophysical modeling, and control algorithms to ensure long-term stable system operation. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a gravity measurement and calculation system. This system, through a unique dynamic correction module and a control algorithm based on Lyapunov stability, converts observation data from a quantum gravimeter into changes in Earth's radius with micrometer-level precision in real time and stably, enabling effective early warning of geophysical events.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A gravity measurement and calculation system includes the following interconnected modules: 1. Gravity Measurement Module: Employs an atomic interferometer quantum gravimeter array with a measurement accuracy better than 0.1 nGal and a sampling rate of no less than 10 Hz. It is used to continuously acquire surface gravitational acceleration data, denoted as g_observed.

[0005] 2. Dynamic Correction Module: This module is configured to perform the following operations: a) Receive g_observed output by the gravity measurement module, and obtain the theoretical gravity value g_model corresponding to the geographical location from the pre-stored benchmark Earth gravity field model; b) Calculate the gravity residual Δg = g_observed - g_model; c) Based on the gravity residual Δg, an energy component correction term ε(g) is updated in real time using an online machine learning algorithm. The energy component correction term ε(g) is a dimensionless empirical parameter used to compensate for local crustal material density variations and short-period geophysical effects not covered by the baseline gravity field model. The learning rate η(t) of the online machine learning algorithm is adaptively adjusted using the Lyapunov stability criterion to ensure that the correction loop does not diverge or oscillate during continuous operation. Specifically, the Lyapunov function V(e) = ½e² is used, where e = ε(t) - ε, and ε is the regionally optimal reference value calibrated offline using historical data. From this, the adaptive law η(t) = η0 / (1+αV(e)) is derived, where η0=0.01 and α=10. This mechanism theoretically guarantees the asymptotic stability of the system state.

[0006] 3. Calculation Module: This module is configured to substitute the updated ε(g) from the dynamic correction module into the following Earth radius solution equation to calculate the Earth radius R(g): R(g) = [ 3 / (4πρ_avg) · g_observed / G · 1 / (1+ε(g)) ]^(1 / 3) Wherein, ρ_avg is the real-time density value calculated to the geoid using a 1°×1° grid interpolation based on the CRUST1.0 global crust model and normalized to the geoid using the standard Bouguer correction formula according to the station elevation; G is the gravitational constant. This equation is derived from the uniform sphere gravity formula by introducing real-time density and empirical correction terms to adapt to the non-homogeneity of the actual Earth.

[0007] 4. Result Output and Early Warning Module: This module transmits data and early warning signals via dual-link redundant transmission of 5G and satellite communication; it is configured to transmit data and early warning signals only if the absolute value of the gravity residual Δg is continuously detected to be greater than or equal to 2 μGal (1 μGal = 10⁻⁻⁶) for 30 consecutive minutes. 8 When the signal speed (m / s²) and the signal-to-noise ratio (SNR) are greater than or equal to 6 dB, a geophysical event early warning signal is automatically generated and issued. This multi-condition joint triggering mechanism aims to balance detection sensitivity and reliability in engineering applications, significantly reducing the false alarm rate.

[0008] The beneficial effects of this invention are as follows: 1. High precision and high stability: Through the adaptive algorithm of Lyapunov stability control, the system correction loop is guaranteed in principle to not diverge or oscillate under high-frequency data flow, realizing reliable monitoring of micron-level changes in the Earth's radius.

[0009] 2. Strong engineering practicality: By setting reasonable warning threshold (2 μGal), minimum duration (30 minutes) and signal-to-noise ratio threshold (6 dB), high-frequency noise and transient interference are effectively filtered out, enabling the system to have both high detection sensitivity and low false alarm rate.

[0010] 3. Real-time early warning capability: By deeply integrating high-precision quantum sensing, stability control algorithms, real-time geophysical models and redundant communication technology, a complete technical closed loop from data acquisition to early warning issuance has been constructed, enabling effective early warning of geophysical events such as earthquakes. Detailed Implementation

[0011] The present invention will now be described in further non-limiting detail with reference to embodiments.

[0012] Example 1: System Hardware Configuration and Basic Workflow The core sensor of the system is a ColdQuanta HORIZON atomic interferometer gravimeter or its equivalent, forming a measurement array. Data processing and calculation tasks are completed on an industrial server equipped with an Intel Xeon processor or a processor of equivalent performance. The basic workflow of the system is as follows: the gravity measurement module continuously collects surface gravitational acceleration data g_observed; the dynamic correction module receives the data at a frequency of not less than 1 Hz and performs update calculations of ε(g); the calculation module synchronously receives the updated ε(g) and calculates the Earth's radius R(g); the result output and early warning module monitors the Δg sequence and signal-to-noise ratio (SNR) output by the calculation module in real time and determines whether to trigger an early warning based on preset conditions.

[0013] Example 2: Implementation of the core algorithm of the dynamic correction module The core algorithm of the dynamic correction module is implemented through software programming, and the pseudocode of its core logic is shown below: Python class EpsilonUpdater: def __init__(self, epsilon_star=0.0, eta0=0.01, alpha=10): self.epsilon = 0.0 # Initialize energy component correction terms self.epsilon_star = epsilon_star # Regionally optimal reference value for offline calibration self.eta0 = eta0 # Initial learning rate parameter self.alpha = alpha # Adaptive adjustment coefficient def update(self, g_observed, g_model): # Calculate the relative residuals to normalize the update magnitude residual = (g_observed - g_model) / g_model # Calculate the error between the current correction term and the optimal reference value e = self.epsilon - self.epsilon_star # Calculate the Lyapunov function value V(e) V = 0.5 * e * e # Dynamically calculate the current adaptive learning rate based on the Lyapunov function value learning_rate = self.eta0 / (1 + self.alpha * V) # Calculate the change in the correction term using the adaptive learning rate and gravity residuals delta_epsilon = learning_rate * residual # Update based on residual direction # Update the energy component correction term ε(g) self.epsilon += delta_epsilon # Apply boundary constraints to the updated correction term to prevent it from overflowing into an unreasonable numerical range. self.epsilon = np.clip(self.epsilon, -0.1, 0.1) # Return the updated corrections for use by the calculation module. return self.epsilon ``` Example 3: Real-time density acquisition and early warning triggering logic The key parameter ρ_avg (real-time density value) in the calculation module is obtained through the following steps: First, the system queries the CRUST1.0 global crustal model database and inputs the precise latitude and longitude coordinates of the station; second, a bilinear interpolation algorithm is used to obtain the initial crustal density value at that location in a 1°×1° grid; finally, the standard Bouguer correction formula is applied to reduce the initial density value to the geoid to eliminate the influence of the station's elevation and surrounding terrain undulations. Under local GPU acceleration, the above process takes less than 30 milliseconds for a single query-interpolation-correction cycle, meeting the system's requirement for 1 Hz real-time updates. The early warning module has a continuously running monitoring process that analyzes the Δg data stream output by the calculation module in real time. The early warning logic is activated only when the system detects that the three conditions—"duration reaches or exceeds 30 minutes," "absolute value of Δg is greater than or equal to 2 μGal," and "signal-to-noise ratio (SNR) is greater than or equal to 6 dB"—are simultaneously true. An early warning signal is then generated and simultaneously transmitted to the monitoring center via two independent communication links: 5G and satellite.

[0014] Example 4: System Stability Verification To verify the superiority of the adaptive algorithm employed in this invention, a numerical simulation comparison experiment was conducted. Simulation results show that, using the Lyapunov adaptive law η(t) = η0 / (1 + αV(e)) specific to this invention, the system can recover to a stable state within 50 iterations even with simulated Gaussian white noise and instantaneous jump signals, and the overshoot is controlled within 15%. In contrast, the algorithm using a fixed learning rate (η(t) = η0) exhibits continuous oscillations or significant divergence under the same test conditions, failing to achieve stable convergence. This comparison strongly demonstrates the significant effect and technical advantages of this invention in maintaining the long-term stability of the system.

Claims

1. A gravimetric computing system, comprising: The system comprises: a gravity measurement module employing an array of atomic interferometric quantum gravimeters configured to acquire surface gravity acceleration data g_observed at a sampling rate no less than 10 Hz with a measurement accuracy better than 0.1 nGal; a dynamic correction module configured to perform the following operations: a) receiving the gravity acceleration data g_observed and obtaining a theoretical gravity value g_model at the corresponding position from a reference Earth gravity field model; b) calculating a gravity residual Δg = g_observed - g_model; c) updating an energy component correction term ε(g) in real time based on the gravity residual Δg through an online machine learning algorithm; wherein the learning rate η(t) of the online machine learning algorithm is adaptively adjusted by a Lyapunov stability criterion, and the Lyapunov function used is V(e) = ½e², where e = ε(t) - ε, and ε is the regional optimal reference value calibrated offline; the adaptive law is η(t) = η0 / (1+αV(e)), where η0=0.01 and α=10 are preset constants; and a numerical boundary constraint of [-0.1, 0.1] is imposed on the updated ε(g); a calculation module configured to substitute the updated ε(g) into the following equation to calculate the Earth's radius R(g): R(g) = [ 3 / (4πρ_avg) · g_observed / G · 1 / (1+ε(g)) ]^(1 / 3) where ρ_avg is a real-time density value based on the CRUST1.0 global crust model, interpolated in a 1°×1° grid, and reduced to the geoid surface through Bouguer correction with station elevation; and G is the gravitational constant; a result output and early warning module configured to transmit data through 5G and satellite dual-link redundancy, and generate and issue a geophysical event warning signal when the absolute value of Δg is greater than or equal to 2 μGal and the signal-to-noise ratio SNR is greater than or equal to 6 dB within 30 consecutive minutes.

2. The system of claim 1, wherein, The reference Earth gravity field model is the EGM2008 model.

3. The system of claim 1, wherein, The update frequency of the dynamic correction module is no less than 1 Hz.

4. The system of claim 1, wherein, The system can detect gravity anomaly changes of no less than 2 μGal.