Earthquake prediction system and method based on background earthquake noise
By using systems and methods based on background seismic noise in the field, noise signals are received and processed through seismic sensors, and tail wave velocities are extracted to determine the time range of seismic nucleation process, the problem of characterization of seismic nucleation process in the field is solved, and high-accurate seismic precursor prediction is achieved.
Patent Information
- Application Number
- CN202510281063.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-11
- Publication Date
- 2025-05-30
AI Technical Summary
The prior art is difficult to accurately characterize the seismic nucleation process in the field, and seismic precursor signals may fail in practical applications.
Using a system and method based on background seismic noise, the background seismic noise signal is received and correlated with each other through at least two seismic sensors, the tail band signal is extracted and its wave velocity is calculated, and statistical analysis is performed to determine the time range of the seismic nucleation process.
This method is easy to implement under field conditions, has high accuracy, and can accurately characterize the seismic nucleation process and provide earthquake precursor prediction.
Smart Images
Figure CN120065303A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of seismic exploration, and more particularly, to a seismic prediction system and method based on ambient seismic noise. Background Art
[0002] The key issue in earthquake prediction lies in how to find stable earthquake precursor signals, which can be determined through the earthquake nucleation process. Under field and laboratory conditions, there are a large number of monitoring indicators for pre-earthquake ground anomalies, such as tectonic strain, temperature, resistivity, water level, and gas diffusion. However, most of these indicators may fail in practical applications. In recent years, more and more scholars have focused on precursor monitoring methods based on seismic waves. The currently extracted seismic wave parameters mainly include the earthquake b-value, microseisms (foreshocks), and seismic wave velocity. The b-value represents the relative frequency-magnitude distribution during the earthquake activity process. Existing studies have found that the b-value sometimes shows a downward trend before the main earthquake. The expansion behavior of foreshocks (microseisms) is often associated with the fault precursor process, and its formation can be explained by aseismic slip and cascade processes. The medium wave velocity is commonly used in the analysis of fault periodic activities, and usually shows a decrease in the pre-earthquake stage.
[0003] Therefore, there is an urgent need for an accurate and easily implementable way in the field to characterize the earthquake nucleation process. Summary of the Invention
[0004] The problem solved by the present invention is how to accurately and easily implement the characterization of the earthquake nucleation process in the field.
[0005] To solve the above problems, the present invention provides a seismic prediction system and method based on ambient seismic noise.
[0006] In a first aspect, the present invention provides a seismic prediction system based on ambient seismic noise, including:
[0007] At least two seismic sensors configured to provide electrical signals in response to reflections of ambient seismic noise signals from a target geological fault;
[0008] A processor configured to:
[0009] Perform cross-correlation processing on the electrical signals from at least two seismic sensors to obtain a cross-correlation signal;
[0010] Determine the tail wave band signal in the cross-correlation signal and calculate the wave velocity of the tail wave band signal to obtain the tail wave velocity of the cross-correlation signal;
[0011] Perform statistical analysis on the tail wave velocity of the cross-correlation signal to obtain the change result of the tail wave velocity of the cross-correlation signal over time;
[0012] Determine the time range corresponding to the earthquake nucleation process in the target geological fault based on the change result of the tail wave velocity of the cross-correlation signal over time, so as to predict earthquake precursors.
[0013] Optionally, the cross-correlation processing of the electrical signals from at least two seismic sensors to obtain the cross-correlation signal includes:
[0014] Traverse the entire time series of the electrical signals from at least two seismic sensors with a sliding window having a preset time length at a predetermined step, and perform cross-correlation calculation on the signals in the sliding window to obtain the cross-correlation signal corresponding to each sliding window.
[0015] Optionally, the extracting the tail wave band signal from the cross-correlation signal and calculating the wave velocity of the tail wave band signal to obtain the tail wave velocity of the cross-correlation signal includes:
[0016] Determine the tail wave band signal in the cross-correlation signal corresponding to each sliding window, and perform wave velocity calculation on the tail wave band signal to obtain the tail wave velocity of the cross-correlation signal corresponding to each sliding window.
[0017] Optionally, before determining the tail wave band signal in the cross-correlation signal corresponding to each sliding window, it further includes:
[0018] Perform reference zero point alignment processing on the cross-correlation signal corresponding to each sliding window;
[0019] Perform superposition and noise reduction processing on the cross-correlation signal corresponding to each sliding window after reference zero point alignment processing to extract the Green's function;
[0020] Perform reference zero point alignment processing on the Green's function;
[0021] Perform normalization processing on the Green's function after reference zero point alignment processing.
[0022] Optionally, before the cross-correlation processing of the electrical signals from at least two seismic sensors to obtain the cross-correlation signal, it further includes:
[0023] Perform outlier processing on the electrical signals from at least two seismic sensors using a Hanning window.
[0024] Optionally, before performing outlier processing on the electrical signals from at least two seismic sensors using a Hanning window, it further includes:
[0025] Determine the dominant frequency band of the electrical signals from at least two seismic sensors.
[0026] Optionally, the seismic sensor is a piezoelectric ceramic sensor.
[0027] Optionally, it further includes:
[0028] A noise generating device, configured to generate a Gaussian white noise signal, and generate and transmit a noise signal to the target geological fault based on the Gaussian white noise signal, where the noise signal is used to simulate a background seismic noise signal.
[0029] Optionally, the noise generating device includes:
[0030] A waveform generator, used to generate and output the Gaussian white noise signal;
[0031] A noise generating device, used to generate and transmit a noise signal to the target geological fault based on the Gaussian white noise signal.
[0032] In a second aspect, the present invention provides a seismic prediction method based on background seismic noise, including:
[0033] Receiving electrical signals from at least two seismic sensors, where the electrical signals are provided by reflection of background seismic noise signals from the target geological fault;
[0034] Performing cross-correlation processing on the electrical signals from at least two seismic sensors to obtain a cross-correlation signal;
[0035] Determining the tail wave band signal in the cross-correlation signal, and calculating the wave velocity of the tail wave band signal to obtain the tail wave velocity of the cross-correlation signal;
[0036] Performing statistical analysis on the tail wave velocity of the cross-correlation signal to obtain the change result of the tail wave velocity of the cross-correlation signal over time;
[0037] According to the change result of the tail wave velocity of the cross-correlation signal over time, determining the time range corresponding to the earthquake nucleation process in the target geological fault to predict earthquake precursors.
[0038] The beneficial effects of the earthquake prediction system and method based on ambient seismic noise of the present invention are as follows: The earthquake prediction system includes at least two seismic sensors and a processor. The seismic sensors are configured to provide electrical signals in response to the reflection of ambient seismic noise signals from a target geological fault. The processor is configured to perform cross-correlation processing on the electrical signals from at least two seismic sensors to obtain a cross-correlation signal, determine the tail wave band signal in the cross-correlation signal, calculate the wave velocity of the tail wave band signal to obtain the tail wave velocity of the cross-correlation signal, and perform statistical analysis on the tail wave velocity of the cross-correlation signal to obtain the change result of the tail wave velocity of the cross-correlation signal over time. Due to the excitation and reception of ambient seismic noise signals, it is easy to implement both under laboratory conditions and field conditions, and is not affected by the environment with high accuracy, such that the tail wave velocity determined by the ambient seismic noise signal can accurately characterize the earthquake nucleation process and is also easy to implement under field conditions. BRIEF DESCRIPTION OF THE DRAWINGS
[0039] Figure 1 FIG. is a schematic structural diagram of an earthquake prediction system based on ambient seismic noise according to an embodiment of the present invention;
[0040] Figure 2 FIG. is a schematic flow diagram of an earthquake prediction method based on ambient seismic noise according to an embodiment of the present invention;
[0041] Figure 3 FIG. is a schematic diagram of a cross-correlation signal according to an embodiment of the present invention;
[0042] Figure 4 FIG. is a schematic flow diagram of processing a cross-correlation signal according to an embodiment of the present invention;
[0043] Figure 5 FIG. is a schematic diagram of the tail wave velocity, stress evolution, and earthquake nucleation process within a stick-slip cycle. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0044] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be given with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not used to limit the protection scope of the present invention.
[0045] It should be understood that the various steps recorded in the method embodiments of the present invention can be executed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this regard.
[0046] As used herein, the term "including" and its variations are open-ended, i.e., "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts such as "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.
[0047] It should be noted that the modification of "one" and "plural" mentioned in the present invention is illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly stated in the context, it should be understood as "one or more".
[0048] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only for illustrative purposes and are not used to limit the scope of these messages or information.
[0049] The following explains the terms related to the present invention:
[0050] Seismic nucleation refers to the process in which a geological fault evolves from local quasi-static rupture to dynamic rupture, including temporal and spatial processes.
[0051] Background seismic noise signal refers to the noise signal generated under the action of human activities and natural phenomena and having an adverse impact on humans and the ecosystem. This kind of noise signal is usually closely related to physical processes such as ground vibration and acoustic wave propagation.
[0052] As Figure 1 shown, a seismic prediction system based on background seismic noise provided by an embodiment of the present invention may include: a noise generating device 110, at least two seismic sensors 120, and a processor 130, where:
[0053] The noise generating device 110 is configured to generate and transmit a random noise signal to a target geological fault, and the noise signal is used to simulate the background seismic noise signal. Generally, the background seismic noise signal is random in waveform. Therefore, the noise signal used to simulate the background seismic noise signal in this embodiment is also random in waveform.
[0054] Specifically, the target geological fault may be a geological fault structure in a laboratory scenario or a real geological fault in a field scenario, and this embodiment does not make any limitation thereto.
[0055] Specifically, whether in a laboratory scenario or a field scenario, the noise generating device 110 is arranged on one side of the target geological fault in its vertical direction, for example, the upper side. It can be understood that the closer the noise generating device 110 is to the target geological fault, the less the noise signal it emits is affected by non-geological faults.
[0056] Optionally, the noise generating device 110 includes: a waveform generator 110-1 and a noise source 110-2. The waveform generator 110-1 generates and outputs a Gaussian white noise to the noise source 110-2, and the noise source 110-2 performs some signal processing on the received Gaussian white noise to generate a noise signal. Specifically, the waveform generator 110-1 can be a commercial function generator.
[0057] Optionally, the noise source 110-2 can be a piezoelectric crystal sensor. In one embodiment, after the Gaussian white noise is output to the piezoelectric crystal sensor, the piezoelectric crystal sensor generates a vibration signal that acts on the target geological fault to generate a random noise signal.
[0058] In this optional embodiment, the piezoelectric crystal sensor uses natural or artificial crystals, such as quartz, which has excellent stability and high precision, good temperature stability, and is suitable for applications requiring high precision and long-term stability. Therefore, it can be used stably for a long time and generate high-precision noise signals.
[0059] The above is the simulated background seismic noise signal generated by the noise generating device 110.
[0060] In some other embodiments, the background seismic noise signal can also be the real noise generated in the target geological fault under the action of human activities and natural phenomena.
[0061] The seismic sensor 120 is used to provide an electrical signal in response to the reflection of the background seismic noise signal from the target geological fault.
[0062] Specifically, the seismic sensor 120 can be arranged on the lower side of the target geological fault in its vertical direction, but it can also be arranged on the upper side of the target geological fault. In some embodiments, there is a certain distance between the seismic sensor 120 and the noise generating device 110, and there is also a certain distance between multiple seismic sensors 120. For example, multiple seismic sensors 120 can be spaced 10-50 meters apart.
[0063] Optionally, the seismic sensor 120 can be a piezoelectric ceramic sensor.
[0064] The processor 130 can be implemented as software, hardware, firmware, or any combination thereof, and can use one or more application-specific integrated circuits (ASICs), one or more general-purpose integrated circuits, one or more microprocessors, one or more programmable logic devices, or any combination of the foregoing circuits and / or devices, or other suitable circuits or devices. Moreover, the processor 130 can control a seismic prediction system based on ambient seismic noise to execute the methods in various embodiments in this specification, such as Figure 2 As shown, a seismic prediction method based on ambient seismic noise includes the following steps:
[0065] S210: Receive electrical signals from at least two seismic sensors 120, where the electrical signals are signals provided by the reflection of ambient seismic noise signals from a target geological fault.
[0066] S220: Perform cross-correlation processing on the electrical signals from at least two seismic sensors 120 to obtain a cross-correlation signal.
[0067] Specifically, the seismic sensor 120 receives the signal after the ambient seismic noise signal is reflected by the target geological fault, and converts and processes the received signal into an electrical signal.
[0068] In some embodiments, since at least two seismic sensors 120 are located at different positions of the target position fault, the signals received by each seismic sensor 120 are different. Therefore, the electrical signals output after being processed by each seismic sensor 120 are also different, and cross-correlation calculation can be performed. Each seismic sensor 120 corresponds to one path of electrical signal. Performing cross-correlation calculation on at least two paths of electrical signals corresponding to at least two seismic sensors 120 can obtain a cross-correlation signal.
[0069] S230: Determine the tail wave band signal in the cross-correlation signal, and calculate the wave velocity of the tail wave band signal to obtain the tail wave velocity of the cross-correlation signal.
[0070] Specifically, the tail wave band signal is a waveform that shows attenuation in amplitude and appears at the tail. For the cross-correlation signal, the tail wave band signal of the cross-correlation signal is a section of signal where attenuation occurs, and the specific time range of the tail wave band signal can be adjusted according to actual situations.
[0071] S240: Perform statistical analysis on the tail wave velocity of the cross-correlation signal to obtain the change result of the tail wave velocity of the cross-correlation signal over time.
[0072] In some embodiments, the change result of the tail wave velocity over time can be a change curve of the tail wave velocity over time.
[0073] S250: Determine the time range corresponding to the earthquake nucleation process in the target geological fault based on the change result of the tail wave velocity of the cross-correlation signal over time, so as to predict earthquake precursors.
[0074] Since during the earthquake nucleation process, the tail wave velocity shows a downward trend over time, after obtaining the change result of the tail wave velocity over time, the peak position of the tail wave velocity can be recorded, and the time range starting from the peak position is the time range corresponding to the earthquake nucleation process.
[0075] In this embodiment, the excitation and reception of the background seismic noise signal are easy to implement both under laboratory conditions and field conditions, and are not affected by the environment with high accuracy, so that the tail wave velocity determined by the background seismic noise signal can accurately characterize the earthquake nucleation process and is also easy to implement under field conditions.
[0076] Optionally, the cross-correlation processing of the electrical signals from at least two seismic sensors to obtain the cross-correlation signal includes: using a sliding window with a preset time length to traverse the entire time series of the electrical signals from at least two seismic sensors at a predetermined step, and performing cross-correlation calculation on the signals in the sliding window to obtain the cross-correlation signal corresponding to each sliding window.
[0077] In some embodiments, the preset time length corresponding to the sliding window is related to the frequency components corresponding to the background environment where the target geological fault is located. In this embodiment, the preset time length of the sliding window is 1 ms, and the predetermined step is 0.5 ms.
[0078] In some embodiments, taking two seismic sensors 120 as an example, first convert the electrical signals from the two seismic sensors 120 into discrete signals to obtain two channels of discrete signals, and perform cross-correlation calculation on the two channels of discrete signals according to the following expression to obtain the cross-correlation signal:
[0079] R xy [k] = ∑ n x[n]·y[k + n + N - 1];
[0080] where n is the serial number of the discrete signal, N is the total number of discrete signals, x[] is one of the two discrete signals, y[] is the other discrete signal of the two discrete signals, k is the offset between the two discrete signals, and R xy [k] is the cross-correlation signal.
[0081] Optionally, extracting the tail wave band signal in the cross-correlation signal and calculating the wave velocity of the tail wave band signal to obtain the tail wave velocity of the cross-correlation signal includes: determining the tail wave band signal in the cross-correlation signal corresponding to each sliding window, and performing wave velocity calculation on the tail wave band signal to obtain the tail wave velocity of the cross-correlation signal corresponding to each sliding window.
[0082] Perform cross-correlation calculations on the N electrical signals output by N seismic sensors. The length of the cross-correlation signal obtained from the cross-correlation calculation is N times that of one electrical signal. For example, as Figure 3 shown, the cross-correlation signal is obtained by performing cross-correlation calculations on two electrical signals, and it includes a causal part and an anti-causal part. For the calculation of the tail wave velocity, only a part needs to be calculated. In this embodiment, the causal part is used for the calculation of the tail wave velocity.
[0083] In some embodiments, before performing cross-correlation calculations on at least two electrical signals, a Hanning window is added to reduce the influence of boundary outliers. In other embodiments, the Hanning window can be replaced with a Gaussian window.
[0084] In some embodiments, before using the Hanning window to process outliers of the electrical signals from at least two seismic sensors 120, it further includes: determining the dominant frequency band of the electrical signals from at least two seismic sensors 120.
[0085] Among them, the dominant frequency band of the electrical signals from at least two seismic sensors 120 is related to the working frequency band of the seismic sensors 120 themselves. By extracting the dominant frequency band of the electrical signals from at least two seismic sensors 120, the accuracy of subsequent cross-correlation calculations can be improved, and ultimately the accuracy of subsequent calculations of the tail wave velocity can be improved.
[0086] Since the background seismic noise signal usually shows randomness in waveform, in addition, the differences between seismic sensors and noise generating devices also make the frequency components of the background seismic noise signal very complex, usually showing the characteristics of a wide frequency band. However, since the background seismic noise signal is common to the earth, there are still similarities in the signal waveforms recorded by different seismic sensors. To obtain these similar signals, in this embodiment, the signals after reflecting the background seismic noise signals received by different seismic sensors are subjected to cross-correlation calculations to obtain the similar parts of the background seismic noise signals; in addition, compared with other waveforms in the background seismic noise, the tail wave has the characteristics of carrying more medium information and being able to reflect medium anomalies in a larger area. Therefore, in this embodiment, the signals received by at least two seismic sensors are subjected to cross-correlation calculations to obtain a cross-correlation signal, and then the tail wave band signal is extracted from the cross-correlation signal, and the tail wave band signal is subjected to interference calculations to obtain the change result of the tail wave velocity over time to determine the accurate earthquake nucleation process.
[0087] In some embodiments, as Figure 4 shown, before determining the tail wave band signal in the cross-correlation signal corresponding to each sliding window, it further includes:
[0088] S410: Perform reference zero point alignment processing on the cross-correlation signal corresponding to each sliding window.
[0089] For example, the cross-correlation signal includes a causal part and an anti-causal part. The reference zero-point alignment takes the junction point of the causal part and the anti-causal part as the reference zero point, which can be referred to Figure 3 .
[0090] S420: Perform superposition noise reduction processing on the corresponding cross-correlation signals in each sliding window after the reference zero-point alignment processing to extract the Green's function.
[0091] Specifically, perform 1s superposition noise reduction processing on the causal part of the cross-correlation signal.
[0092] S430: Perform reference zero-point alignment processing on the Green's function. Specifically, align the Green's function at the zero-lag point.
[0093] S440: Perform normalization processing on the Green's function after the reference zero-point alignment processing.
[0094] Specifically, maximum normalization processing can be performed on the cross-correlation signal, that is, make the cross-correlation signal distributed between 0-1 or -1 to 1 to avoid errors caused by some weak signals or sensor differences.
[0095] In this optional embodiment, by performing noise reduction processing, reference zero-point alignment, and normalization processing on the cross-correlation signal, the accuracy of the processed cross-correlation signal is higher, so as to improve the accuracy of subsequent calculation of the tail wave velocity.
[0096] Please refer to Figure 5 , Figure 5 , which is a schematic diagram of the tail wave velocity, stress evolution, and earthquake nucleation process within a stick-slip cycle. Among them, curve g is the tail wave velocity curve at different positions of the fault, curve bl is the shear stress curve at different positions of the fault, indicating that the calculated tail wave velocity has a high accuracy. Curve b2 is the local normal stress curve at different positions of the fault, curve p is the macroscopic shear stress curve at different positions of the fault, used to reflect the shear stress of the entire fault, and curve b3 is the correlation coefficient curve. Since the correlation coefficient is large, it shows that the calculated wave velocity has a high accuracy. It can be seen that the moment when the peak of the tail wave velocity (curve g) appears at different positions of the fault is consistent with the moment when the peak of the corresponding position shear stress (curve b1) appears. In addition, after the shear stress peak point, the fault thickness (curve br) increases rapidly, which also indicates the occurrence of the dilation phenomenon during the nucleation stage.
[0097] A computer-readable storage medium provided by an embodiment of the present invention, on which a computer program is stored. When the computer program is executed by a processor, the earthquake prediction method based on ambient seismic noise as described above is implemented.
[0098] That is to say, a non-volatile computer-readable storage medium stores a computer program thereon. When the computer program is executed by a processor, the processor performs the following operations:
[0099] Receiving electrical signals from at least two seismic sensors, the electrical signals being provided by reflection of background seismic noise signals from a target geological fault;
[0100] Performing cross-correlation processing on the electrical signals from at least two seismic sensors to obtain a cross-correlation signal;
[0101] Determining a tail wave band signal in the cross-correlation signal and calculating a wave velocity of the tail wave band signal to obtain a tail wave velocity of the cross-correlation signal;
[0102] Performing statistical analysis on the tail wave velocity of the cross-correlation signal to obtain a change result of the tail wave velocity of the cross-correlation signal over time;
[0103] Determining a time range corresponding to a seismic nucleation process in the target geological fault according to the change result of the tail wave velocity of the cross-correlation signal over time to predict earthquake precursors.
[0104] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc. In this application, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present invention. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.
[0105] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will all fall within the protection scope of the present invention.
Claims
1. An earthquake prediction system based on background seismic noise, characterized in that: include: at least two seismic sensors configured to provide electrical signals in response to reflections of background seismic noise signals from a target geological fault; The processor is configured as: Performing cross-correlation processing on electrical signals from at least two seismic sensors to obtain a cross-correlation signal; Determine the tail band signal in the cross-correlation signal, and calculate the wave velocity of the tail band signal to obtain the tail wave velocity of the cross-correlation signal; Performing statistical analysis on the coda wave velocity of the cross-correlation signal to obtain a change result of the coda wave velocity of the cross-correlation signal over time; According to the variation of the coda wave velocity of the cross-correlation signal over time, the time range corresponding to the earthquake nucleation process in the target geological fault is determined to predict the earthquake precursor.
2. The earthquake prediction system based on background seismic noise according to claim 1, characterized in that: The cross-correlation processing of the electrical signals from the at least two seismic sensors to obtain a cross-correlation signal comprises: A sliding window with a preset time length is used to traverse the entire time series of the electrical signals from the at least two seismic sensors with a predetermined step length, and cross-correlation calculation is performed on the signals in the sliding window to obtain a cross-correlation signal corresponding to each sliding window.
3. The earthquake prediction system based on background seismic noise according to claim 2, characterized in that: The step of extracting the tail band signal from the cross-correlation signal and calculating the wave velocity of the tail band signal to obtain the tail wave velocity of the cross-correlation signal comprises: The tail band signal in the cross-correlation signal corresponding to each sliding window is determined, and the wave velocity of the tail band signal is calculated to obtain the tail wave velocity of the cross-correlation signal corresponding to each sliding window.
4. The earthquake prediction system based on background seismic noise according to claim 3, characterized in that: Before determining the tail band signal in the cross-correlation signal corresponding to each sliding window, the method further includes: Performing reference zero point alignment processing on the corresponding cross-correlation signal in each sliding window; Performing superposition noise reduction processing on the corresponding cross-correlation signal in each sliding window after the reference zero point alignment processing to extract the Green function; Performing reference zero point alignment processing on the Green's function; The Green's function after the reference zero point alignment process is standardized.
5. The earthquake prediction system based on background seismic noise according to claim 1, characterized in that: Before the cross-correlation processing of the electrical signals from the at least two seismic sensors to obtain the cross-correlation signal, the method further comprises: A Hanning window is used to perform outlier processing on the electrical signals from the at least two seismic sensors.
6. The earthquake prediction system based on background seismic noise according to claim 5, characterized in that: Before using the Hanning window to process the outliers of the electrical signals from the at least two seismic sensors, the method further includes: A dominant frequency band of the electrical signals from the at least two seismic sensors is determined.
7. The earthquake prediction system based on background seismic noise according to any one of claims 1 to 6, characterized in that: The seismic sensor is a piezoelectric ceramic sensor.
8. The earthquake prediction system based on background seismic noise according to any one of claims 1 to 6, characterized in that: Also includes: The noise generating device is configured to generate a Gaussian white noise signal, and generate and transmit a noise signal to the target geological fault based on the Gaussian white noise signal, wherein the noise signal is used to simulate a background seismic noise signal.
9. The earthquake prediction system based on background seismic noise according to claim 8, characterized in that: The noise generating device comprises: A waveform generator, used to generate and output the Gaussian white noise signal; A noise source is used to generate and transmit a noise signal to the target geological fault based on the Gaussian white noise signal.
10. A method for earthquake prediction based on background seismic noise, characterized in that: include: receiving electrical signals from at least two seismic sensors, the electrical signals being signals provided by reflections of background seismic noise signals from a target geological fault; Performing cross-correlation processing on electrical signals from at least two seismic sensors to obtain a cross-correlation signal; Determine the tail band signal in the cross-correlation signal, and calculate the wave velocity of the tail band signal to obtain the tail wave velocity of the cross-correlation signal; Performing statistical analysis on the coda wave velocity of the cross-correlation signal to obtain a change result of the coda wave velocity of the cross-correlation signal over time; According to the variation of the coda wave velocity of the cross-correlation signal over time, the time range corresponding to the earthquake nucleation process in the target geological fault is determined to predict the earthquake precursor.