A data processing system, data processing method, and computer-readable storage device connected to a seismometer via IoT

Abstract

A data processing system is disclosed, suitable for processing seismic wave data acquired by seismographs to locate geothermal resources. The system includes an Internet of Things (IoT) module and a processing module. The IoT module is coupled to at least one seismograph via a communication interface to acquire seismic wave data and is also coupled to the processing module. The processing module includes a debugging unit and a machine learning model. The processing module can remove duplicate or erroneous seismic wave data and filter out noise. The machine learning model, coupled to the debugging unit, compares the debugged seismic wave data with multiple known geothermal characteristic data and calculates the probability of geothermal reservoirs through seismic wave spectral analysis to generate analysis results.

Claims

1. A data processing system connected to a seismograph via the Internet of Things (IoT), suitable for processing seismic wave data obtained by a seismograph (12, 14) to locate geothermal activity, the data processing system (16) comprising: An Internet of Things (IoT) module (102) is coupled to the seismograph (12, 14) via a communication transmission interface to acquire the seismic wave data; a processing module (104) is coupled to the IoT module (102) and has an edge computing architecture to filter out unimportant seismic wave data; and the edge computing architecture has an error correction unit (112) that removes duplicate or erroneous seismic wave data based on the Global Positioning System (GPS) markers in the seismic wave data.

2. The data processing system as described in claim 1, wherein the processing module (104) further comprises an artificial intelligence engine (114) coupled to the debugging unit (112) for identifying abnormal seismic wave data with extreme values ​​and filtering out the abnormal seismic wave data.

3. The data processing system as described in claim 2, wherein the artificial intelligence engine (114) is a deep learning model.

4. The data processing system as described in claim 2, wherein the edge computing architecture has a machine learning model (116) coupled to the artificial intelligence engine (114) to compare the filtered seismic wave data with multiple known geothermal feature values ​​and calculate the probability of geothermal reservoirs through seismic wave spectrum analysis, thereby generating an analysis result.

5. The data processing system as described in claim 1, wherein the edge computing architecture includes: a high-frequency filtering unit for removing environmental noise from the seismic wave data; and a low-frequency filtering unit for removing long-period interference noise from the seismic wave data.

6. The data processing system as described in claim 1, wherein the communication interface is a LoRa or mobile communication network interface.

7. The data processing system as described in claim 1 further includes: A storage module (108) is coupled to the processing module (104) to store the processed seismic wave data and an analysis result generated by the processing module (104) through seismic wave spectrum analysis to calculate the probability of geothermal reservoirs.

8. A data processing method connected to a seismograph via the Internet of Things (IoT), suitable for processing seismic wave data collected by a seismograph (12, 14) to search for geothermal energy, the data processing method comprising at least the following steps: acquiring the seismic wave data through a communication interface; performing an edge computing procedure to filter out unimportant seismic wave data and noise; inputting the filtered seismic wave data into a machine learning model to compare the seismic wave data with multiple known geothermal characteristic values, and calculating the probability of geothermal reservoirs through seismic wave spectrum analysis to generate an analysis result; and generating a visualization report based on the analysis result; wherein the edge computing procedure further comprises at least the following steps: performing time synchronization processing on the seismic wave data based on the Global Positioning System (GPS) time stamp in the seismic wave data to remove duplicate or erroneous seismic wave data.

9. The data processing method as described in claim 8, wherein the communication interface is a LoRa or mobile communication network interface.

10. The data processing method as described in claim 8, wherein the edge computing procedure further includes at least the following steps: performing a high-frequency noise filtering process to remove environmental noise; performing a low-frequency noise filtering process to eliminate long-period interference noise; and using an artificial intelligence model engine to determine abnormal seismic wave data with extreme values, so as to filter out the abnormal seismic wave data.

11. A computer-readable storage device storing at least one instruction code, which, when read, performs at least the following steps: acquiring seismic wave data; performing an edge computing procedure to filter out unimportant seismic wave data and noise; inputting the filtered seismic wave data into a machine learning model to compare the seismic wave data with multiple known geothermal feature values, and calculating the probability of geothermal reservoirs through seismic wave spectrum analysis, thereby generating an analysis result, wherein performing the edge computing procedure further includes at least the following steps: performing time synchronization processing on the seismic wave data based on the Global Positioning System (GPS) time stamp in the seismic wave data to remove duplicate or erroneous seismic wave stamps.

12. The computer-readable storage device as described in claim 11, wherein the edge computing procedure further includes at least the following steps: performing a high-frequency noise filtering process to remove ambient noise; performing a low-frequency noise filtering process to eliminate long-period interference noise; and using an artificial intelligence engine to identify anomalous seismic wave data with extreme values, thereby filtering out the anomalous seismic wave data.

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