阅读说明：本技术 一种航空重力测量数据的弱信息提取方法 (Weak information extraction method for aviation gravity measurement data ) 是由 王冠鑫 罗锋 周锡华 熊盛青 王林飞 于 2019-10-28 设计创作，主要内容包括：一种航空重力测量数据的弱信息提取方法,包括以下步骤：(S1)对DGPS数据和加速度数据进行改正；(S2)对改正后的DGPS数据和加速度数据进行卡尔曼滤波处理；(S3)对步骤(S2)中得到的结果进行卡尔曼平滑处理,得到重力异常数据。本发明一是将传统的频率域滤波转换成时间域滤波,避免了截止频率的选择及信号的丢失；二是从飞机运动的角度进行真实状态的估计,可以直接得到重力异常信号,简化了航空重力数据处理流程,提高了运算效率。三是给出了全新的航空重力异常提取的数学模型,避免了DGPS数据和加速度数据产生错位,即使针对起伏飞行数据,也可精确地提取出重力异常信号。(A weak information extraction method of aviation gravity measurement data comprises the following steps: (S1) correcting the DGPS data and the acceleration data; (S2) performing kalman filtering processing on the corrected DGPS data and acceleration data; (S3) kalman smoothing is performed on the result obtained in the step (S2) to obtain gravity anomaly data. The invention firstly converts the traditional frequency domain filtering into time domain filtering, thus avoiding the selection of cut-off frequency and the loss of signals; and secondly, the real state is estimated from the angle of the aircraft motion, so that the gravity anomaly signal can be directly obtained, the aviation gravity data processing flow is simplified, and the operation efficiency is improved. And thirdly, a brand-new mathematical model for extracting the aviation gravity anomaly is provided, the DGPS data and the acceleration data are prevented from being staggered, and even if the fluctuating flight data are targeted, the gravity anomaly signal can be accurately extracted.)

1. A weak information extraction method of aviation gravity measurement data is characterized by comprising the following steps: the method comprises the following steps:

(S1) correcting the synchronized DGPS data and acceleration data;

(S2) performing kalman filtering processing on the corrected DGPS data and acceleration data;

(S3) kalman smoothing is performed on the result obtained in the step (S2) to obtain gravity anomaly data.

2. The weak information extraction method of the airborne gravity measurement data according to claim 1, characterized in that: the method for correcting the synchronized DGPS data and acceleration data in step (S1) includes the following steps: eccentricity correction, ertvows correction, normal field correction, null shift correction, horizontal acceleration correction.

3. The weak information extraction method of the airborne gravity measurement data according to claim 1, characterized in that: the method of the kalman filter process in step (S2) is as follows:

(S2.1) constructing a mathematical model of the aviation gravity anomaly:

wherein Δ g is an abnormal value of gravity, f_ΣV is the vertical gravities (including normal field, height, horizontal acceleration, Erfou, null shift, base point) after each correction_uIs the vertical acceleration of the aircraft, q_ΣIs the sum of various types of noise;

(S2.2) constructing a Kalman filtering state equation:

in the formula (f)₁'、f₂'、f₃' are two measurements of horizontal and vertical acceleration; the error of vertical acceleration measured by gravimeter can be divided into low-frequency components delta f_TAnd a high frequency component δ f, where δ f_TDue to low frequency disturbances (e.g., angular acceleration due to aircraft pitch, roll motion); KF₁、KF₂The variable quantity of the platform installation error angle caused by material fatigue and temperature change;

(S2.3) constructing a Kalman filtering measurement equation:

h'＝h+δh (3)

in the formula, h' is the actual measurement height, h is the real height, and δ h is the error of the actual measurement height;

(S2.4) substituting the state equation established in the step (S2.2) and the measurement equation established in the step (S2.3) into kalman filter equations (4) and (5) to solve:

X_k＝Φ_k,k-1X_k-1+B_k-1u_k-1+Γ_k-1W_k-1 (4)

Z_k＝H_kX_k+V_k (5)

in the formula phi_k,k-1、B_k-1Is a constant matrix; u. of_k-1Is a control item; gamma-shaped_k-1Driving the array for system noise; w_kExciting a noise sequence for the system; v_kFor measuring noise sequences, H_kIs a measuring array; wherein the input value of Kalman filtering is f₁'、f₂'、f₃'、f_ΣH', output values h, v_u、KF₁、KF₂、Δg、

4. the weak information extraction method of the airborne gravity measurement data according to claim 3, characterized in that: applying the Kalman filter equation of state employed in said step (S2.4) to

X_k＝Φ_k,k-1X_k-1+B_k-1u_k-1+Γ_k-1W_k-1 (4)

Replacing the steps as follows:

X_k＝Φ_k,k-1X_k-1+B_ku_k+ΓW (6)

in the formula, X_kIs an estimated state; phi_k,k-1、B_k-1、B_kIs a constant matrix; u. of_k-1、u_kIs a control item; gamma-shaped_k-1Gamma is a system noise driving array; w_kAnd W is a system excitation noise sequence.

Technical Field

The invention belongs to the technical field of aviation gravity exploration, and relates to a weak information extraction method of aviation gravity measurement data.

Background

Gravity exploration is an important geophysical exploration means, mainly comprises ground gravity measurement and aviation gravity measurement, and is usually adopted to acquire gravity field data when the conditions that workers cannot pass through complicated mountainous regions, marshes, oceans and the like or rapid scanning measurement is required. The aviation gravity measurement takes an airplane as a carrying platform, and utilizes a gravity and positioning sensor combined system to measure and obtain aviation gravity original measurement data of a free space; then, resolving the obtained original measurement data to obtain space gravity anomaly data (namely weak information); finally, based on the information, useful geophysical information is deduced. In the actual flight measurement process, because the aircraft is simultaneously influenced by multiple aspects such as self vibration, fluctuation, turning flight, airflow action and the like, no matter the aircraft is subjected to flight measurement by adopting a strapdown gravimeter, a vector gravimeter or a platform gravimeter, the obtained original gravity signal contains a large amount of noise, and the noise-signal ratio is up to thousands or even tens of thousands. For an aviation gravity measurement result, the in-line coincidence accuracy of the engineering exploration work requirement is 0.8mGal, and for a newly developed domestic gravimeter, the in-line coincidence accuracy of the newly developed domestic gravimeter requirement is 0.6mGal, so that a data resolving technology is required to reach a very high level. On the other hand, the vertical acceleration information measured by the gravimeter contains gravity field information, the vertical acceleration of the airplane and high-frequency noise. The method is characterized in that a traditional frequency domain filter (such as fir100s filtering which is commonly adopted at present) is adopted, firstly, the vertical acceleration of the airplane contained in original measurement data cannot be removed, the vertical acceleration needs to be processed independently in the resolving process, secondly, the accurate cut-off frequency cannot be determined, only the low-frequency component of the original measurement data can be extracted approximately, and effective information contained in the high-frequency component is filtered out together, so that the later-stage data resolving and application effects are poor. Therefore, how to obtain the gravity anomaly data meeting the available precision from the original measurement data still remains a key technical problem restricting the deep development of the aviation gravity exploration field.

Disclosure of Invention

The invention aims to overcome the technical defects in the prior art and provides the method for extracting the weak information of the aviation gravity measurement data, which has the advantages of concise data processing process, high information extraction accuracy and precision and less hardware resources occupied in the calculation process.

In order to achieve the purpose, the invention adopts the following technical scheme:

a weak information extraction method of aviation gravity measurement data comprises the following steps:

(S1) correcting the DGPS data and the acceleration data;

(S2) performing kalman filtering processing on the corrected DGPS data and acceleration data;

(S3) kalman smoothing is performed on the result obtained in the step (S2) to obtain gravity anomaly data.

The method for correcting the DGPS data and the acceleration data in step (S1) includes the following steps: eccentricity correction, ertvows correction, normal field correction, null shift correction, horizontal acceleration correction.

Further, the method of the kalman filter process in step (S2) is as follows:

(S2.1) constructing a mathematical model of the aviation gravity anomaly:

wherein Δ g is an abnormal value of gravity, f_ΣFor each corrected vertical gravity value, including normal field correction, height correction, horizontal acceleration correction, Ergo correction, null shift correction, base point correction, etc., v_uIs the vertical acceleration of the aircraft, q_ΣIs the sum of various types of noise;

(S2.2) constructing a Kalman filtering state equation:

in the formula (f)₁'、f₂'、f₃' are two measurements of horizontal and vertical acceleration; the error of vertical acceleration measured by gravimeter can be divided into low-frequency components delta f_TAnd a high frequency component deltaf, where δ f_TDue to low frequency disturbances, such as angular acceleration caused by aircraft pitch, roll motion; KF₁、KF₂The variable quantity of the platform installation error angle caused by material fatigue and temperature change;

(S2.3) constructing a Kalman filtering measurement equation:

h'＝h+δh (3)

in the formula, h' is the actual measurement height, h is the real height, and δ h is the error of the actual measurement height;

(S2.4) substituting the state equation established in the step (S2.2) and the measurement equation established in the step (S2.3) into kalman filter equations (4) and (5) to solve:

X_k＝Φ_k,k-1X_k-1+B_k-1u_k-1+Γ_k-1W_k-1 (4)

Z_k＝H_kX_k+V_k (5)

in the formula phi_k,k-1、B_k-1Is a constant matrix; u. of_k-1Is a control item; gamma-shaped_k-1Driving the array for system noise; w_kExciting a noise sequence for the system; v_kFor measuring noise sequences, H_kIs a measuring array; wherein the input value of Kalman filtering is f₁'、f₂'、f₃'、f_ΣH', output values h, v_u、KF₁、KF₂、Δg、

Δ g is a gravity abnormal value (namely weak information) which needs to be extracted in aviation gravity exploration;

further, the Kalman filter state equation adopted in the step (S2.4) is calculated by

X_k＝Φ_k,k-1X_k-1+B_k-1u_k-1+Γ_k-1W_k-1 (4)

Replacing the steps as follows:

X_k＝Φ_k,k-1X_k-1+B_ku_k+ΓW (6)

in the formula, X_kIs an estimated state; phi_k,k-1、B_k-1、B_kIs a constant matrix; u. of_k-1、u_kIs a control item; gamma-shaped_k-1Gamma is a system noise driving array; w_kAnd W is a system excitation noise sequence.

The invention discloses a method for extracting weak information of aviation gravity measurement data, which has the following beneficial effects: one is to convert the conventional frequency domain filtering into time domain filtering. The aviation gravity data belongs to the bit field data, the frequency of the gravity abnormal signal is distributed in the whole frequency band, the traditional frequency domain frequency filtering (such as the fir100s filtering commonly used in the current stage engineering) only extracts the low-frequency component of the signal, the high-frequency part is abandoned, and the loss of useful information is caused. The Kalman filtering selected in the technical scheme estimates the real state of the gravity sensor in flight according to the stress and motion conditions of the gravity sensor in flight, so that a gravity abnormal signal is extracted, and the selection of a cut-off frequency and the loss of the signal are avoided. And secondly, the aviation gravity data processing flow is simplified, and the operation efficiency is improved. The vertical acceleration generated by the movement of the airplane cannot be removed by adopting the traditional frequency domain filtering, and the vertical acceleration of the airplane needs to be processed independently. The Kalman filter adopted by the technical scheme carries out estimation on the real state from the angle of airplane motion, so that the gravity anomaly signal can be directly obtained without other processing. Thirdly, a general Kalman filtering equation with control items (see Kalman filtering and integrated navigation principle (3 rd edition), page 49, formula (2.2.35)) is improved, the invention provides a mathematical model for aviation gravity anomaly extraction, and the control item at the previous moment is modified into the control item at the current moment according to actual characteristics, so that the DGPS data and the acceleration data are prevented from being misplaced. The improved algorithm can accurately extract the gravity anomaly signal even aiming at the measurement data acquired in the fluctuating flight.

Drawings

FIG. 1 is a schematic flow chart of a weak information extraction method of airborne gravity measurement data according to embodiment 1;

fig. 2 is a processing result in embodiment 2 using a conventional fir100s filter;

FIG. 3 is the result of the treatment in example 2 using the method described in example 1;

FIG. 4 is the results of processing the pre and post heave flight data in example 2 using the band control term equation described in example 1.

Detailed Description

The following further describes a specific embodiment of the method for extracting weak information of airborne gravity measurement data according to the present invention with reference to the accompanying drawings. The weak information extraction method of the aviation gravity measurement data is not limited to the description of the following embodiments.