1. A system for earthquake detection using a geographically distributed sensors network, comprising: a plurality of seismic sensors located at geographically distributed locations, each configured to detect seismic waves and generate a seismic signal; a data processing unit in communication with the seismic sensors, configured to receive and process the seismic signals and generate a seismic event data set; a neural network module in communication with the data processing unit, configured to analyse the seismic event data set and identify potential earthquake events; and a user interface in communication with the neural network module, configured to display information related to potential earthquake events.

2. The system of claim 1, wherein the seismic sensors comprise accelerometers or seismometers.

3. The system of claim 1, wherein the neural network module comprises a deep learning neural network.

4. A method for earthquake detection using a geographically distributed sensors network and a neural network, comprising: deploying a plurality of seismic sensors at geographically distributed locations; configuring the seismic sensors to detect seismic waves and generate a seismic signal; transmitting the seismic signal to a data processing unit for signal processing and data storage; generating a seismic event data set from the processed seismic signals; analyzing the seismic event data set using a neural network module to identify potential earthquake events; and displaying information related to potential earthquake events on a user interface.

5. The method of claim 4, further comprising training the neural network module using a dataset of seismic events and their corresponding features.

6. The method of claim 4, further comprising optimizing the performance of the neural network module using a feedback mechanism.

7. The method of claim 4, wherein the neural network module comprises a convolutional neural network or a recurrent neural network.

8. The method of claim 1, further comprising adjusting the sensitivity of the seismic sensors and the neural network module to optimize earthquake detection efficiency.

9. The method of claim 4, wherein the seismic sensors are deployed in natural disaster-prone areas to provide early warning of potential earthquake events.

10. The method of claim 4, further comprising integrating the earthquake detection system with emergency response systems to enhance the response to natural disasters.   GEOGRAPHICALLY DISTRIBUTED SENSORS NETWORK TO DETECTING EARTHQUAKES Abstract The present invention relates to a system for earthquake detection using a geographically distributed sensors network This system may include a plurality of seismic sensors located at geographically distributed locations, each of which is designed to detect seismic waves and generate a seismic signal. In certain implementations, there is additionally a data processing unit that is in communication with the seismic sensors. This data processing unit is designed to receive and analyse seismic signals in order to produce a seismic event data set. A neural network module that is designed to evaluate the seismic event data set and detect possible earthquake occurrences may also be included in embodiments. The neural network module may be in communication with the data processing unit. A user interface that is set to show information relating to anticipated seismic occurrences may also be included in embodiments. Fig. 1 , Claims:Claims :

1. A system for earthquake detection using a geographically distributed sensors network, comprising: a plurality of seismic sensors located at geographically distributed locations, each configured to detect seismic waves and generate a seismic signal; a data processing unit in communication with the seismic sensors, configured to receive and process the seismic signals and generate a seismic event data set; a neural network module in communication with the data processing unit, configured to analyse the seismic event data set and identify potential earthquake events; and a user interface in communication with the neural network module, configured to display information related to potential earthquake events.

2. The system of claim 1, wherein the seismic sensors comprise accelerometers or seismometers.

3. The system of claim 1, wherein the neural network module comprises a deep learning neural network.

4. A method for earthquake detection using a geographically distributed sensors network and a neural network, comprising: deploying a plurality of seismic sensors at geographically distributed locations; configuring the seismic sensors to detect seismic waves and generate a seismic signal; transmitting the seismic signal to a data processing unit for signal processing and data storage; generating a seismic event data set from the processed seismic signals; analyzing the seismic event data set using a neural network module to identify potential earthquake events; and displaying information related to potential earthquake events on a user interface.

5. The method of claim 4, further comprising training the neural network module using a dataset of seismic events and their corresponding features.

6. The method of claim 4, further comprising optimizing the performance of the neural network module using a feedback mechanism.

7. The method of claim 4, wherein the neural network module comprises a convolutional neural network or a recurrent neural network.

8. The method of claim 1, further comprising adjusting the sensitivity of the seismic sensors and the neural network module to optimize earthquake detection efficiency.

9. The method of claim 4, wherein the seismic sensors are deployed in natural disaster-prone areas to provide early warning of potential earthquake events.

10. The method of claim 4, further comprising integrating the earthquake detection system with emergency response systems to enhance the response to natural disasters.

Description:GEOGRAPHICALLY DISTRIBUTED SENSORS NETWORK TO DETECTING EARTHQUAKES
Field of the Invention
[0001] The invention relates to a method for detecting earthquakes and locating epicentre. More particularly, to a system and method for earthquake detection using a geographically distributed sensors network..

Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Earthquakes are natural disasters that can cause significant damage to infrastructure and human life. Detecting earthquakes in real-time is important for providing early warning to people in affected areas, facilitating emergency response efforts, and improving our understanding of these complex events.
[0004] There are a variety of techniques and methods that can be used to detect earthquakes, including seismometers, GPS sensors, and acoustic sensors. Seismometers are the most commonly used technique for detecting earthquakes and work by measuring the vibrations or seismic waves generated by earthquakes. Acoustic sensors can detect the sound waves generated by earthquakes. Few of exemplary documents are discussed below.
[0005] The EP3018497B1 (By: SCHREDER) - Described herein is method for the detection of seismic activity using a network of lights, and in particular, street lights (43) arranged over a number of streets (42). Each light includes a control module having the facility for both long- and short-distance communication, the control modules being grouped with other control modules and associated with a group controller to create a short-distance or mesh network. Each control module includes a sensor which is capable of detecting seismic activity and data relating to such activity may be transmitted to a central server via its group controller using long-distance communication. Even if the sensors are relatively inaccurate, the high number of such sensors present in the network makes it possible to detect and analyse the activity using geocoordinate information provided by the control modules at the server. Information relating to an epicentre of an earthquake can be determined and distributed to control modules in the vicinity of the detected seismic activity (50) to provide warning light signals for the population in that vicinity.
[0006] The US6356204B1 (By: SEISMIC WARNING SYSTEMS) - An apparatus and associated method for detecting impending earthquakes includes at least one sensor, and preferably multiple sensors, for mounting on a building or other like structure, and include a transducer for converting vibration signals to electronic impulses. The signals are transmitted to a solid state detection circuit, which distinguishes between extraneous signals and signals indicative of the P-waves which signal an impending earthquake. Discrimination between relevant and non-relevant may be achieved by selecting a minimum amplitude and duration of signals within a selected frequency range, and triggering an alarm when the selected minimums are exceeded. Where multiple sensors are deployed, temporal overlap between selected signals can be assessed for further discrimination.
[0007] The DE69829056T2 (By: SEISMIC WARNING SYSTEMS) - An apparatus and associated method for detecting impending earthquakes includes at least one sensor, and preferably multiple sensors, for mounting on a building or other like structure, and include a transducer for converting vibration signals to electronic impulses. The signals are transmitted to a solid state detection circuit, which distinguishes between extraneous signals and signals indicative of the P-waves which signal an impending earthquake. Discrimination between relevant and non-relevant may be achieved by selecting a minimum amplitude and duration of signals within a selected frequency range, and triggering an alarm when the selected minimums are exceeded. Where multiple sensors are deployed, temporal overlap between selected signals can be assessed for further discrimination.
[0008] The US4689997A (By: WINDISCH DAVID E) - A motion detector for detecting earthquakes or the like provides a warning signal when vibrations having a frequency of the order of the natural frequency of an earthquake tremor are detected. A preferred embodiment of the device employs a vertical spring barb member which is mounted on a suitable support on one end thereof. A coupler member is supported on the other end of the barb member. This coupler member is connected through a coil spring to an inertial mass which is vertically positioned generally in external concentricity with the barb and the coupler, the spring being either compressed or extended to provide a resilient coupling between the coupler member and the inertial mass. The spring and mass elements are chosen so as to have a natural resonant frequency at the frequency of an earthquake tremor (0.7-3 Hz), or other disturbance to be detected. An electrical switching circuit is provided so that when the disturbance to be detected occurs, motion of the inertial mass will cause the electrical switch to close thereby activating a suitable alarm device for providing a warning signal.
[0009] However, known techniques are non-efficient. Thus, there is need for tech advancement over known methodology.
[00010] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.

Summary
[00011] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00012] The following paragraphs provide additional support for the claims of the subject application.
[00013] The invention relates to a method for detecting earthquakes and locating epicentre. More particularly, to a system and method for earthquake detection using a geographically distributed sensors network.
[00014] Embodiments of the present disclosure may include a system for earthquake detection using a geographically distributed sensors network, including a plurality of seismic sensors located at geographically distributed locations, wherein each seismic sensor is configured to detect seismic waves and generate a seismic signal. Embodiments may also include a data processing unit in communication with the seismic sensors, configured to receive and process the seismic signals and generate a seismic event data set.
[00015] Embodiments may also include a neural network module in communication with the data processing unit, wherein the neural network module is configured to analyse the seismic event data set and identify potential earthquake events. Embodiments may also include a user interface in communication with the neural network module, wherein the user interface is configured to display information related to potential earthquake events.
[00016] In some embodiments, the seismic sensors may include accelerometers or seismometers. In some embodiments, the neural network module may include a deep learning neural network. In some embodiments, the method may include adjusting the sensitivity of the seismic sensors and the neural network module to optimize earthquake detection efficiency.
[00017] Embodiments of the present disclosure may also include a method for earthquake detection using a geographically distributed sensors network and a neural network, including deploying a plurality of seismic sensors at geographically distributed locations. Embodiments may also include configuring the seismic sensors to detect seismic waves and generate a seismic signal.
[00018] Embodiments may also include transmitting the seismic signal to a data processing unit for signal processing and data storage. Embodiments may also include generating a seismic event data set from the processed seismic signals. Embodiments may also include analyzing the seismic event data set using a neural network module to identify potential earthquake events. Embodiments may also include displaying information related to potential earthquake events on a user interface.
[00019] In some embodiments, the method may include training the neural network module using a dataset of seismic events and their corresponding features. In some embodiments, the method may include optimizing the performance of the neural network module using a feedback mechanism. In some embodiments, the neural network module may include a convolutional neural network or a recurrent neural network. In some embodiments, the seismic sensors may be deployed in natural disaster-prone areas to provide early warning of potential earthquake events. In some embodiments, the method may include integrating the earthquake detection system with emergency response systems to enhance the response to natural disasters.

Brief Description of the Drawings
[00020] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00021] FIG. 1 is a block diagram illustrating a system for earthquake detection using a geographically distributed sensors network, according to some embodiments of the present disclosure.
[00022] FIG. 2 is a modified block diagram further illustrating the system (from FIG. 1) for earthquake detection using a geographically distributed sensors network, according to some embodiments of the present disclosure.
[00023] FIG. 3 is a flowchart illustrating a method for earthquake detection using a geographically distributed sensors network, according to some embodiments of the present disclosure.
Detailed Description
[00024] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00025] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00026] Following are more detailed descriptions of various concepts related to, and implementations of, methods, apparatuses, and systems of present disclosure. The various concepts introduced above and discussed in greater detail below may be implemented in any of numerous ways.
[00027] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00028] The invention relates to a method for detecting earthquakes and locating epicentre. More particularly, to a system and method for earthquake detection using a geographically distributed sensor network.
[00029] FIG. 1 is a block diagram that describes a system 100 for earthquake detection using a geographically distributed sensors network, according to some embodiments of the present disclosure. In some embodiments, the system 100 may also include a plurality of seismic sensors 110 (collectively or individually referred as seismic sensors 110 or seismic sensor 110, respectively) located at geographically distributed locations, wherein each of the seismic sensor 110 can be configured to detect seismic waves and generate a seismic signal. The system 100 may also include a data processing unit 120 in communication with the seismic sensors 110, wherein the data processing unit 120 can be configured to receive and process the seismic signals and generate a seismic event data set. The system 100 may also include a neural network module 130 in communication with the data processing unit 120, wherein the neural network module 130 can be configured to analyse the seismic event data set and identify potential earthquake events. The system 100 may also include a user interface 140 in communication with the neural network module 130, wherein the user interface 140 can be configured to display information related to potential earthquake events. In some embodiments, the neural network module 130 may include a deep learning neural network. In some embodiments, the system 100 may adjust the sensitivity of the seismic sensors 110 and the neural network module 130 to optimize earthquake detection efficiency.
[00030] FIG. 2 is a modified block diagram that further describes the system 100 (from FIG. 1) for earthquake detection using a geographically distributed sensors network, according to some embodiments of the present disclosure. In some embodiments, the plurality of seismic sensors 110 may include accelerometers 212 and seismometers 214, which can be specialized low-noise accelerometers to detect seismic waves in planetary bodies
[00031] FIG. 3 is a flowchart that describes a method for earthquake detection using a geographically distributed sensors network, according to some embodiments of the present disclosure. In some embodiments, at 310, the method may include deploying a plurality of seismic sensors at geographically distributed locations. At 320, the method may include configuring the seismic sensors to detect seismic waves and generate a seismic signal. At 330, the method may include transmitting the seismic signal to a data processing unit for signal processing and data storage. At 340, the method may include generating a seismic event data set from the processed seismic signals. At 350, the method may include analyzing the seismic event data set using a neural network module to identify potential earthquake events. At 360, the method may include displaying information related to potential earthquake events on a user interface.
[00032] In some embodiments, the method may include training the neural network module 130 using a dataset of seismic events and their corresponding features. In some embodiments, the method may include optimizing the performance of the neural network module 130 using a feedback mechanism. In some embodiments, the neural network module 130 may comprise a convolutional neural network or a recurrent neural network. In some embodiments, the seismic sensors 110 may be deployed in natural disaster-prone areas to provide early warning of potential earthquake events. In some embodiments, the method may include integrating the earthquake detection system with emergency response systems to enhance the response to natural disasters.
[00033] A system for earthquake detection using a geographically distributed sensors network may be included in some embodiments of the present disclosure. This system may include a plurality of seismic sensors 110 located at geographically distributed locations, each of which is designed to detect seismic waves and generate a seismic signal. In certain implementations, there is the data processing 120 that is in communication with the seismic sensors 110. This data processing unit 120 is designed to receive and analyse seismic signals in order to produce a seismic event data set.
[00034] The neural network module 130 that is designed to evaluate the seismic event data set and detect possible earthquake occurrences may also be included in embodiments. The neural network module 130 may be in communication with the data processing unit 120. Theuser interface 140 that is set to show information relating to anticipated seismic occurrences may also be included in embodiments. This user interface 140 may be in contact with the neural network module 130.
[00035] Accelerometers and seismometers are two types of seismic sensors 110 that may be used in various implementations. Deep learning neural networks may be included in the neural network module 130. Adjusting the sensitivity of the seismic sensors 110 and the neural network module 130 may be an integral part of some implementations of the approach, with the end goal of maximising the accuracy of earthquake detection.
[00036] The current disclosure may also comprise a method for detecting earthquakes using a geographically distributed sensors network and a neural network. This technique may involve the deployment of a number of seismic sensors 110 at various geographically dispersed sites. Configuring the seismic sensors 110 to detect seismic waves and produce a seismic signal is another possible step that may be included in certain embodiments.
[00037] Transmission of the seismic signal to the data processing unit 120 for the purposes of signal processing and data storage is another possible aspect of embodiments. In certain embodiments, there is also the possibility of producing a seismic event data set by processing seismic signals. In certain embodiments, the identification of possible earthquake occurrences is accomplished by conducting an analysis of the seismic event data set using a neural network module. Displaying information on a user interface 140 relating to anticipated seismic occurrences is another aspect that may be included in embodiments.
[00038] In some implementations of the approach, it may be necessary to train the neural network module 130 by using a dataset of seismic occurrences and the attributes that are associated with them. The approach could, in certain implementations, include making use of a feedback system in order to bring about performance improvements in the neural network module. Convolutional neural networks and recurrent neural networks are both possible components of the neural network module, depending on the specific implementation. In some implementations, the seismic sensors 110 may be installed in regions that are more likely to be affected by natural disasters in order to offer an early warning of impending earthquake occurrences. In some implementations of the approach, it is possible for it to include linking the earthquake detection system with the emergency response systems in order to improve how quickly and effectively they react to natural catastrophes.
[00039] Variations of those preferred embodiments may become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventors expect skilled artisans to employ such variations as appropriate, and the inventors intend for the invention to be practiced otherwise than as specifically described herein. Accordingly, this invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the invention unless otherwise indicated herein or otherwise clearly contradicted by context
[00040] As used herein, the term “wireless communication network” or “network interface” refers to a network following any suitable wireless communication standards, such as LTE-Advanced (LTE-A), LTE, Wideband Code Division Multiple Access (WCDMA), High-Speed Packet Access (HSPA), and so on. Furthermore, the communications between network devices in the wireless communication network may be performed according to any suitable generation communication protocols, including, but not limited to, the first generation (1G), the second generation (2G), 2.5G, 2.75G, the third generation (3G), the fourth generation (4G), 4.5G, the fifth generation (5G) communication protocols, and/or any other protocols either currently known or to be developed in the future.
[00041] As used herein, the term “network device” refers to a device in a wireless communication network via which a terminal device accesses the network and receives services therefrom. The network device may refer to a base station (BS) or an access point (AP), for example, a node B (NodeB or NB), an evolved NodeB (eNodeB or eNB), a Remote Radio Unit (RRU), a radio header (RH), a remote radio head (RRH), a relay, a low power node such as a femto, a pico, and so forth, depending on the applied terminology and technology. The “network device” or “terminal device” or “computing device” may represent any suitable device (or group of devices) capable, configured, arranged, and/or operable to enable and/or provide a terminal device access to the wireless communication network or to provide some service to a terminal device that has accessed the wireless communication network. The terminal device may include, but not limited to, a mobile phone, a cellular phone, a smart phone, a tablet, a wearable device, a personal digital assistant (PDA), portable computers, image capture terminal devices such as digital cameras, gaming terminal devices, music storage and playback appliances, wearable terminal devices, vehicle-mounted wireless terminal devices and the like. In the following description, the terms “terminal device”, “terminal”, “user equipment”, “computing device”, “network device” and “UE” may be used interchangeably.
[00042] Processing device may be provided by one or more processors such as a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00043] In addition, the present disclosure may also provide a memory containing the computer program as mentioned above, which includes machine-readable media and machine-readable transmission media. The machine-readable media may also be called computer-readable media, and may include machine-readable storage media, for example, magnetic disks, magnetic tape, optical disks, phase change memory, or an electronic memory terminal device like a random access memory (RAM), read only memory (ROM), flash memory devices, CD-ROM, DVD, Blue-ray disc and the like. The machine-readable transmission media may also be called a carrier, and may include, for example, electrical, optical, radio, acoustical or other form of propagated signals—such as carrier waves, infrared signals, and the like.
[00044] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
[00045] All references to “a/an/the element, apparatus, component, means, step, etc.” are to be interpreted as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated. The discussion above and below in respect of any of the aspects of the present disclosure is also in applicable parts relevant to any other aspect of the present disclosure.
[00046] The wordings such as “include”, “including”, “comprise” and “comprising” do not exclude elements or steps which are present but not listed in the description and the claims.
[00047] It also shall be noted that as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. This invention can be achieved by means of hardware including several different elements or by means of a suitably programmed computer. In the unit claims that list several means, several ones among these means can be specifically embodied in the same hardware item. The use of such words as first, second, third does not represent any order, which can be simply explained as names.
[00048] Various techniques may be described herein in the general context of software, hardware elements, or program modules. Generally, such modules include routines, programs, objects, elements, components, data structures, and so forth that perform particular tasks or implement particular abstract data types. The terms “module,” “functionality,” and “component” as used herein generally represent software, firmware, hardware, or a combination thereof. The features of the techniques described herein are platform-independent, meaning that the techniques may be implemented on a variety of commercial computing platforms having a variety of processors
 

Claims
I/We Claim:
1. A system for earthquake detection using a geographically distributed sensors network, comprising:
a plurality of seismic sensors located at geographically distributed locations, each configured to detect seismic waves and generate a seismic signal;
a data processing unit in communication with the seismic sensors, configured to receive and process the seismic signals and generate a seismic event data set;
a neural network module in communication with the data processing unit, configured to analyse the seismic event data set and identify potential earthquake events; and
a user interface in communication with the neural network module, configured to display information related to potential earthquake events.
2. The system of claim 1, wherein the seismic sensors comprise accelerometers or seismometers.
3. The system of claim 1, wherein the neural network module comprises a deep learning neural network.
4. A method for earthquake detection using a geographically distributed sensors network and a neural network, comprising:
deploying a plurality of seismic sensors at geographically distributed locations;
configuring the seismic sensors to detect seismic waves and generate a seismic signal;
transmitting the seismic signal to a data processing unit for signal processing and data storage;
generating a seismic event data set from the processed seismic signals;
analyzing the seismic event data set using a neural network module to identify potential earthquake events; and
displaying information related to potential earthquake events on a user interface.
5. The method of claim 4, further comprising training the neural network module using a dataset of seismic events and their corresponding features.
6. The method of claim 4, further comprising optimizing the performance of the neural network module using a feedback mechanism.
7. The method of claim 4, wherein the neural network module comprises a convolutional neural network or a recurrent neural network.
8. The method of claim 1, further comprising adjusting the sensitivity of the seismic sensors and the neural network module to optimize earthquake detection efficiency.
9. The method of claim 4, wherein the seismic sensors are deployed in natural disaster-prone areas to provide early warning of potential earthquake events.
10. The method of claim 4, further comprising integrating the earthquake detection system with emergency response systems to enhance the response to natural disasters.
 

GEOGRAPHICALLY DISTRIBUTED SENSORS NETWORK TO DETECTING EARTHQUAKES
Abstract
The present invention relates to a system for earthquake detection using a geographically distributed sensors network This system may include a plurality of seismic sensors located at geographically distributed locations, each of which is designed to detect seismic waves and generate a seismic signal. In certain implementations, there is additionally a data processing unit that is in communication with the seismic sensors. This data processing unit is designed to receive and analyse seismic signals in order to produce a seismic event data set. A neural network module that is designed to evaluate the seismic event data set and detect possible earthquake occurrences may also be included in embodiments. The neural network module may be in communication with the data processing unit. A user interface that is set to show information relating to anticipated seismic occurrences may also be included in embodiments.

Fig. 1

, Claims:Claims
I/We Claim:
1. A system for earthquake detection using a geographically distributed sensors network, comprising:
a plurality of seismic sensors located at geographically distributed locations, each configured to detect seismic waves and generate a seismic signal;
a data processing unit in communication with the seismic sensors, configured to receive and process the seismic signals and generate a seismic event data set;
a neural network module in communication with the data processing unit, configured to analyse the seismic event data set and identify potential earthquake events; and
a user interface in communication with the neural network module, configured to display information related to potential earthquake events.
2. The system of claim 1, wherein the seismic sensors comprise accelerometers or seismometers.
3. The system of claim 1, wherein the neural network module comprises a deep learning neural network.
4. A method for earthquake detection using a geographically distributed sensors network and a neural network, comprising:
deploying a plurality of seismic sensors at geographically distributed locations;
configuring the seismic sensors to detect seismic waves and generate a seismic signal;
transmitting the seismic signal to a data processing unit for signal processing and data storage;
generating a seismic event data set from the processed seismic signals;
analyzing the seismic event data set using a neural network module to identify potential earthquake events; and
displaying information related to potential earthquake events on a user interface.
5. The method of claim 4, further comprising training the neural network module using a dataset of seismic events and their corresponding features.
6. The method of claim 4, further comprising optimizing the performance of the neural network module using a feedback mechanism.
7. The method of claim 4, wherein the neural network module comprises a convolutional neural network or a recurrent neural network.
8. The method of claim 1, further comprising adjusting the sensitivity of the seismic sensors and the neural network module to optimize earthquake detection efficiency.
9. The method of claim 4, wherein the seismic sensors are deployed in natural disaster-prone areas to provide early warning of potential earthquake events.
10. The method of claim 4, further comprising integrating the earthquake detection system with emergency response systems to enhance the response to natural disasters.