1. A system for aquifer mapping of a region using Vertical Electrical Sounding (VES) and artificial intelligence, comprising: a Vertical Electrical Sounding instrument configured to capture geoelectrical data from various depths in the region; a data storage module to store the captured geoelectrical data; an artificial intelligence processor trained on historical geoelectrical and aquifer data; and an output interface to display an aquifer map, wherein the artificial intelligence processor is configured to analyze the captured geoelectrical data to predict and display the aquifer map on the output interface.

2. The system of claim 1, wherein the Vertical Electrical Sounding instrument includes sensors capable of adjusting frequency and depth penetration based on real-time feedback from the artificial intelligence processor.

3. The system of claim 1, further comprising a cloud-based database that aggregates geoelectrical data from multiple VES instruments across various regions, enabling the artificial intelligence processor to refine and optimize its predictions.

4. The system of claim 1, wherein the output interface provides 3D visualizations of the aquifer map, with layers representing different depth intervals and aquifer properties.

5. The system of claim 1, wherein the artificial intelligence processor employs a deep learning neural network, trained on a diverse set of geoelectrical datasets, to ensure accurate aquifer mapping across varying geological terrains.

6. A method for creating an aquifer map of a region using Vertical Electrical Sounding and artificial intelligence, the method comprising: conducting Vertical Electrical Sounding to obtain geoelectrical data at various depths in the region; storing the obtained geoelectrical data in a data storage module; processing the stored geoelectrical data using an artificial intelligence processor trained on historical geoelectrical and aquifer data; and generating and displaying an aquifer map based on the processed data.

7. The method of claim 6, further comprising adjusting the frequency and depth penetration of the Vertical Electrical Sounding instrument based on real-time feedback from the artificial intelligence processor, optimizing data collection for the region's specific geological characteristics.

8. The method of claim 6, further comprising: uploading the obtained geoelectrical data to a cloud-based database; and accessing aggregated geoelectrical data from multiple regions from the cloud-based database to refine and optimize aquifer predictions.

9. The method of claim 6, wherein the step of displaying the aquifer map includes presenting 3D visualizations with different layers representing varying depths and aquifer properties.

10. The method of claim 6, wherein the artificial intelligence processing step involves using a deep learning neural network that has been trained on a diverse set of geoelectrical datasets, enhancing the accuracy of aquifer mapping predictions across diverse geological terrains. VERTICAL ELECTRICAL SOUNDING METHOD WITH ARTIFICIAL INTELLIGENCE TO CREATE AN AQUIFER MAP OF A REGION Abstract The invention relates to a system for aquifer mapping using Vertical Electrical Sounding (VES) combined with artificial intelligence. The system comprises a VES instrument designed to acquire geoelectrical data from varying depths, a storage module for said data, an AI processor trained on historical geoelectrical and aquifer datasets, and an output interface to visualize an aquifer map. Notably, the AI processor analyzes the acquired data to produce the aquifer map, which is rendered on the interface. Enhanced features include real-time adjustment capabilities for the VES instrument based on AI feedback, a cloud database for data aggregation from multiple VES instruments, 3D visualization of the aquifer map detailing depth intervals and aquifer attributes, and the use of a deep learning neural network by the AI processor for enhanced mapping accuracy across diverse geological contexts. , Claims:Claims :

1. A system for aquifer mapping of a region using Vertical Electrical Sounding (VES) and artificial intelligence, comprising: a Vertical Electrical Sounding instrument configured to capture geoelectrical data from various depths in the region; a data storage module to store the captured geoelectrical data; an artificial intelligence processor trained on historical geoelectrical and aquifer data; and an output interface to display an aquifer map, wherein the artificial intelligence processor is configured to analyze the captured geoelectrical data to predict and display the aquifer map on the output interface.

2. The system of claim 1, wherein the Vertical Electrical Sounding instrument includes sensors capable of adjusting frequency and depth penetration based on real-time feedback from the artificial intelligence processor.

3. The system of claim 1, further comprising a cloud-based database that aggregates geoelectrical data from multiple VES instruments across various regions, enabling the artificial intelligence processor to refine and optimize its predictions.

4. The system of claim 1, wherein the output interface provides 3D visualizations of the aquifer map, with layers representing different depth intervals and aquifer properties.

5. The system of claim 1, wherein the artificial intelligence processor employs a deep learning neural network, trained on a diverse set of geoelectrical datasets, to ensure accurate aquifer mapping across varying geological terrains.

6. A method for creating an aquifer map of a region using Vertical Electrical Sounding and artificial intelligence, the method comprising: conducting Vertical Electrical Sounding to obtain geoelectrical data at various depths in the region; storing the obtained geoelectrical data in a data storage module; processing the stored geoelectrical data using an artificial intelligence processor trained on historical geoelectrical and aquifer data; and generating and displaying an aquifer map based on the processed data.

7. The method of claim 6, further comprising adjusting the frequency and depth penetration of the Vertical Electrical Sounding instrument based on real-time feedback from the artificial intelligence processor, optimizing data collection for the region's specific geological characteristics.

8. The method of claim 6, further comprising: uploading the obtained geoelectrical data to a cloud-based database; and accessing aggregated geoelectrical data from multiple regions from the cloud-based database to refine and optimize aquifer predictions.

9. The method of claim 6, wherein the step of displaying the aquifer map includes presenting 3D visualizations with different layers representing varying depths and aquifer properties.

10. The method of claim 6, wherein the artificial intelligence processing step involves using a deep learning neural network that has been trained on a diverse set of geoelectrical datasets, enhancing the accuracy of aquifer mapping predictions across diverse geological terrains.

Description:VERTICAL ELECTRICAL SOUNDING METHOD WITH ARTIFICIAL INTELLIGENCE TO CREATE AN AQUIFER MAP OF A REGION
Field of the Invention
[0001] The present invention relates generally to the field of geophysical exploration and geoelectrical measurements. More specifically, the invention pertains to an enhanced method for aquifer mapping in a region using Vertical Electrical Sounding (VES) combined with artificial intelligence (AI) algorithms. The method seeks to provide accurate and detailed mapping of subsurface water-bearing formations, known as aquifers, based on the interpretation of geoelectrical data. Through the synergistic integration of traditional VES techniques and advanced AI algorithms, the method offers improved accuracy, efficiency, and comprehensiveness in aquifer mapping, facilitating better water resource management and exploration.
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] Vertical Electrical Sounding (VES) has been a staple in the domain of geophysical exploration for several decades. The method involves measuring the earth's resistivity using electrodes placed at the surface. By changing the distance between the electrodes, various depths beneath the surface can be probed, providing an electrically derived profile of subsurface layers. This data can reveal valuable information about the geological formations below, including the presence and characteristics of aquifers. Given the increasing need for fresh water resources, accurate identification and characterization of aquifers have become critically important.
[0004] Traditional VES methods rely on manual interpretation of the resistivity data, which requires significant expertise. Interpretation is typically done by comparing the measured data with theoretical curves or master curves. While effective, this approach can be time-consuming and may not always capture the intricacies of complex geological formations, leading to potential inaccuracies in aquifer detection and characterization.
[0005] With the advent of computing technology, attempts have been made to automate the interpretation process. For instance, U.S. Patent No. XYZ1234 describes a computer-assisted method for interpreting VES data, wherein a database of theoretical curves is used to match against collected data, aiding in the faster identification of subsurface formations. However, such methods, while faster than manual interpretations, still predominantly rely on predetermined datasets, limiting their flexibility.
[0006] The recent surge in artificial intelligence (AI) research has provided new avenues for improving VES interpretations. AI, with its ability to handle vast amounts of data and derive patterns from it, presents an opportunity to make VES more accurate and efficient. Machine learning, a subset of AI, has shown potential in various geophysical applications. In European Patent Application No. ABC7890, a method is described wherein machine learning is used to predict rock formations based on seismic data, demonstrating the potential of AI in geophysical exploration.
[0007] However, direct application of AI to VES for aquifer mapping has been relatively unexplored. There's a gap between traditional VES methods, which are primarily deterministic and model-driven, and modern AI techniques, which are data-driven. Marrying these two approaches can provide a synergistic solution where AI can refine and enhance traditional VES interpretations, leading to more accurate aquifer maps. The AI can be trained on vast datasets from various geographical regions, allowing it to make predictions even in areas where traditional VES might struggle due to lack of matching master curves.
[0008] Furthermore, with the increasing availability of cloud computing and big data analytics, there's a unique opportunity to create a global database of VES readings. Such a database, when combined with AI, can lead to a continually improving system where the AI learns from new data, making subsequent aquifer mappings more accurate.
[0009] In light of the foregoing, there is a need for an integrated method that combines the robustness and reliability of VES with the flexibility and predictive power of AI. Such a method would revolutionize aquifer mapping, making it more accurate, faster, and adaptable to various geological formations and regions.
[00010]
[00011] 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.
[00012] 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.
Summary
[00013] 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.
[00014] The present invention relates generally to the field of geophysical exploration and geoelectrical measurements. More specifically, the invention pertains to an enhanced method for aquifer mapping in a region using Vertical Electrical Sounding (VES) combined with artificial intelligence (AI) algorithms. The method seeks to provide accurate and detailed mapping of subsurface water-bearing formations, known as aquifers, based on the interpretation of geoelectrical data. Through the synergistic integration of traditional VES techniques and advanced AI algorithms, the method offers improved accuracy, efficiency, and comprehensiveness in aquifer mapping, facilitating better water resource management and exploration.
[00015] The innovative system for aquifer mapping marries the tried-and-true Vertical Electrical Sounding (VES) method with cutting-edge artificial intelligence to provide an unprecedented level of detail and accuracy in mapping groundwater reservoirs. At its core, the system is built around a VES instrument designed to probe the earth and capture geoelectrical data, which is indicative of the subsurface's composition. This instrument, unlike traditional counterparts, is equipped with smart sensors. These sensors have the capability to dynamically adjust their frequency and depth penetration based on real-time feedback from the system's AI processor, ensuring optimal data capture tailored to the region's specific geological characteristics.
[00016] Once this data is collected, it's stored systematically in a dedicated data storage module, ensuring easy access for subsequent analytical procedures. This is where the system's artificial intelligence processor comes into play. Having been trained on historical geoelectrical and aquifer datasets, the AI is adept at interpreting the newly acquired VES data. It deciphers patterns and intricacies, leading to predictions about the presence, depth, and characteristics of aquifers in the mapped region.
[00017] To bolster its analytical prowess, the AI processor isn't working in isolation. The system also integrates a cloud-based database that accumulates geoelectrical data from a myriad of VES instruments deployed across diverse regions. This vast, aggregated dataset empowers the AI to refine and hone its predictions, leveraging insights from varied geological contexts.
[00018] A standout feature of the system is its output interface. Instead of offering just a flat, 2D representation, users are treated to an immersive 3D visualization of the aquifer map. This visualization vividly showcases aquifers at different depth intervals, offering a layered perspective that's invaluable for researchers, planners, and policymakers. Furthermore, ensuring that the system remains effective across a spectrum of terrains, the AI processor employs a deep learning neural network. Trained on a comprehensive and diverse array of geoelectrical datasets, this neural network ensures the system's predictions remain consistently accurate, irrespective of geological complexities.
[00019] In essence, this system represents a leap forward in aquifer mapping, combining traditional geophysical techniques with the power of artificial intelligence to provide deeper insights, greater accuracy, and a more intuitive user experience.
[00020] In the evolving realm of groundwater research and conservation, a groundbreaking method emerges, harmonizing the established Vertical Electrical Sounding (VES) technique with the advanced capacities of artificial intelligence. This method offers a transformative approach to constructing aquifer maps, shedding light on subsurface water reservoirs with heightened accuracy and detail.
[00021] The process begins with conducting Vertical Electrical Sounding across the chosen region, a non-invasive geophysical method renowned for its ability to extract geoelectrical data from different subterranean depths. The data obtained, which provides insights into subsurface layers and potential water-bearing formations, is systematically stored within a designated data storage module.
[00022] With the data in place, the method introduces the prowess of artificial intelligence. Utilizing an AI processor, which has been meticulously trained on a vast repository of historical geoelectrical and aquifer data, the method processes the VES data. But what sets this apart from conventional analysis is the AI's dynamic adaptability. Real-time feedback from the AI can inform the VES instrument to adjust its frequency and depth penetration, ensuring that the data captured is uniquely optimized for the geological characteristics of the region in question.
[00023] Further amplifying its precision, the method taps into the collective power of cloud-based databases. By uploading the fresh geoelectrical data to this cloud platform, the method gains access to aggregated geoelectrical information from myriad regions. This collective knowledge serves as a reservoir from which the AI can draw, refining and enhancing its predictions about aquifer locations and properties.
[00024] Visual representation is a crucial aspect of this method. Going beyond traditional 2D displays, the method culminates in a vivid 3D aquifer map. This multi-layered representation, segmented by depth and aquifer properties, provides an immersive and detailed insight into the subsurface water reservoirs.
[00025] Integral to the method's unparalleled accuracy is its embrace of deep learning. The AI doesn't merely process data; it learns from it. Employing a deep learning neural network, the AI has been trained on an extensive and diverse array of geoelectrical datasets. This ensures that regardless of the geological challenges or variations a region presents, the method's aquifer mapping predictions remain consistently accurate and insightful.
[00026] In essence, this method revolutionizes aquifer mapping, blending tried-and-true geophysical techniques with AI's adaptability and learning capabilities to offer an unrivaled view into the world beneath our feet.
[00027]
Brief Description of the Drawings
[00028] 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:
[00029] FIG. 1 represents an architectural overview of a system for aquifer mapping of a region using Vertical Electrical Sounding (VES) and artificial intelligence, according to some embodiments of the present disclosure.
[00030] FIG. 2 shows an exemplary detailed schematic flow diagram of a method for creating an aquifer map of a region using Vertical Electrical Sounding and artificial intelligence, according to some embodiments of the present disclosure.
[00031]
Detailed Description
[00032] 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.
[00033] 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.
[00034] Throughout the present disclosure, the term “network” relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations.
[00035] Throughout the present disclosure, the term “process”* relates to any collection or set of instructions executable by a computer or other digital system so as to configure the computer or the digital system to perform a task that is the intent of the process.
[00036] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.
[00037] 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.
[00038] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00039] The present invention relates generally to the field of geophysical exploration and geoelectrical measurements. More specifically, the invention pertains to an enhanced method for aquifer mapping in a region using Vertical Electrical Sounding (VES) combined with artificial intelligence (AI) algorithms. The method seeks to provide accurate and detailed mapping of subsurface water-bearing formations, known as aquifers, based on the interpretation of geoelectrical data. Through the synergistic integration of traditional VES techniques and advanced AI algorithms, the method offers improved accuracy, efficiency, and comprehensiveness in aquifer mapping, facilitating better water resource management and exploration.
[00040] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00041] Aquifers, the subsurface layers that contain groundwater, are critical to various human activities, especially agriculture, drinking water supply, and industries. Accurately mapping these aquifers is essential for efficient groundwater extraction and resource management. This invention is a comprehensive system 100 that leverages both Vertical Electrical Sounding (VES) technology and modern artificial intelligence (AI) capabilities to create precise aquifer maps.
[00042] According to a pictorial portrayal in FIG. 1, illustrating an architectural setup of the system 100 for aquifer mapping of a region using Vertical Electrical Sounding (VES) and artificial intelligence, comprising a Vertical Electrical Sounding instrument 102 configured to capture geoelectrical data from various depths in the region, a data storage module 104 to store the captured geoelectrical data, an artificial intelligence processor 106 trained on historical geoelectrical and aquifer data, and an output interface 108 to display an aquifer map, wherein the artificial intelligence processor is configured to analyse the captured geoelectrical data to predict and display the aquifer map on the output interface.
[00043] In an embodiment, the heart of the system is the VES instrument. This device is designed to probe beneath the earth's surface by injecting a current and measuring the resultant voltage differences. The earth acts as a resistor, and the voltage differences recorded can provide insights into the underlying layers' resistivities. Different materials, such as clay, rock, sand, and water, have different resistivities, making it possible to distinguish between them. Consider a region primarily consisting of rocky terrain with intermittent layers of aquifers. Traditional VES equipment would probe the region, and based on the resistivity values recorded, a geophysicist might deduce the presence of an aquifer. However, the new VES instrument in this system captures geoelectrical data with higher granularity and from various depths, ensuring comprehensive coverage of the region.
[00044] In an embodiment, the data captured by the VES instrument is vast and complex. The storage module acts as a repository for this data, organizing it in a manner that facilitates easy retrieval and analysis. The module stores resistivity values, depth information, geolocation data, timestamps, and other relevant meta-information. After surveying a 10 sq. km region, the VES instrument might produce multiple gigabytes of geoelectrical data. This data, segmented by location and depth, is systematically stored in the storage module, ensuring that subsequent data retrieval processes are efficient.
[00045] Traditional VES data interpretation relies on the expertise of geophysicists, who compare the recorded resistivity values against known models to infer subsurface structures. This invention revolutionizes this step by introducing an AI processor trained on vast historical geoelectrical and aquifer datasets. The AI not only identifies the aquifer layers but also predicts their properties, such as water quality, depth, and spread. For example, in a region previously known to have two primary aquifer layers at depths of 50m and 120m, the AI processor can refine its predictions based on new VES data. If the new data suggests slight variations in resistivity values at these depths, the AI, recalling patterns from historical data, might predict that the upper aquifer has expanded or that there's a new sediment layer affecting resistivity.
[00046] The results of the AI's analysis need to be comprehensible to end-users, whether they are geologists, water resource managers, or policymakers. The output interface displays an intuitive aquifer map, visually delineating regions with aquifers, their depths, and properties. Additionally, it offers tools for users to interact with the map, zooming in on areas of interest or querying specific data points. For example, a water resource manager, after surveying the map on the output interface, identifies a previously unknown aquifer located near a town facing water shortages. The manager can then focus on this new aquifer for potential groundwater extraction, significantly impacting the town's water supply challenges.
[00047] Modern VES instruments in this system are embedded with sensors that can adjust both the frequency of the injected current and the depth of probing. These adjustments, based on real-time feedback from the AI processor, ensure optimal data collection. If the AI identifies an area that might require more detailed probing, it communicates with the VES instrument, prompting it to adjust its parameters accordingly. For example, while probing a region, the AI processor detects patterns suggesting a complex interplay of rock and aquifer layers. To better understand this, it instructs the VES instrument to increase the probing depth and adjust the current's frequency. This feedback loop ensures more refined data capture.
[00048] Beyond the local storage module, the system is integrated with a cloud-based database. This database aggregates geoelectrical data from multiple VES instruments across diverse regions. Such an extensive data pool enables the AI processor to refine its algorithms, learning from new data patterns and enhancing its predictive accuracy. For example, a VES instrument in Region A detects a unique resistivity pattern. Simultaneously, another instrument in Region B, thousands of miles away, captures a similar pattern. The aggregated data in the cloud allows the AI to identify this recurring pattern and refine its prediction models, improving aquifer mapping accuracy in both regions.
[00049] The output interface, in advanced implementations, provides 3D visualizations of the aquifer map. These visualizations offer a layered view, with distinct depth intervals and properties. This multi-dimensional view gives users a comprehensive understanding of the subsurface structures. For example, a geologist, studying the 3D visualization, identifies that two aquifers in a region, initially thought to be separate, might be interconnected

at deeper levels. Such insights can have profound implications for groundwater extraction strategies and aquifer recharge initiatives.
[00050] Deep learning, a subset of AI, is known for its ability to detect intricate patterns in vast datasets. The AI processor in this system employs a deep learning neural network, which is specially trained on diverse geoelectrical datasets. This ensures accurate aquifer mapping, even in regions with complex geological terrains. For example, in a geologically diverse region, with a mix of Rocky Mountains, sandy deserts, and clayey plains, traditional VES methods might struggle to provide accurate maps due to the contrasting terrains. However, the deep learning neural network, trained on varied datasets, can identify and understand the nuances of such diverse terrains, offering a highly accurate aquifer map.
[00051] Referring to one or more preceding embodiments, the described system 100 is a paradigm shift in aquifer mapping. By synergistically combining VES technology with artificial intelligence, the system offers unparalleled accuracy and efficiency in mapping subsurface water-bearing structures. With its real-time feedback mechanisms, cloud data aggregation, 3D visualizations, and deep learning capabilities, the system promises to significantly advance water resource exploration and management.
[00052] Aquifers, the natural reservoirs of groundwater, have long been the silent backbone of human civilization, quenching thirsts, irrigating fields, and facilitating industries. The method 200 described herein blends the traditional technique of Vertical Electrical Sounding (VES) with state-of-the-art artificial intelligence to pioneer a new age of aquifer mapping.
[00053] Figuratively depicted in FIG. 2, representing a flow diagram of the method 200 comprising steps of (at step 202) conducting Vertical Electrical Sounding to obtain geoelectrical data at various depths in the region, (at step 204) storing the obtained geoelectrical data in a data storage module, (at step 206) processing the stored geoelectrical data using an artificial intelligence processor trained on historical geoelectrical and aquifer data, and (at step 208) generating and displaying an aquifer map based on the processed data.
[00054] Vertical Electrical Sounding is an age-old technique that measures the earth's resistivity by injecting a current and measuring the resulting voltage differences. Different subsurface materials, such as rocks, sand, clay, and water, exhibit varied resistivities, which help in identifying their presence. For instance, consider a vast agricultural region looking to optimize its water resources. The first step in the method involves conducting VES across this area. As the current penetrates the ground, the resultant voltage differences, shaped by the underlying materials' resistivities, are recorded. High resistivity might indicate rocky layers, while low resistivity can signal the presence of water-bearing aquifers.
[00055] Once the VES data, rich with insights about the subsurface materials, is captured, it's essential to store it systematically. This ensures that the subsequent analytical processes can access and process the data efficiently. For instance, following the VES conducted across the agricultural region, a dataset comprising resistivity values, depth information, timestamp, geolocation data, and more, is compiled. This dataset is then methodically stored in the designated storage module, ready for the next steps in the mapping process.
[00056] Traditional interpretation of VES data typically relied on geophysicists who would manually correlate recorded resistivity values against known models. This method redefines this step by introducing an artificial intelligence processor. This AI system, seasoned with historical geoelectrical and aquifer data, combs through the stored VES data, predicting the location, depth, and properties of the aquifers. For instance, in our ongoing example, the AI processor analyses the stored data from the agricultural region. It identifies patterns suggesting aquifers at varying depths – perhaps one at 40 meters and another deeper one at 120 meters. Drawing from its training data, it can also predict the likely water quality and potential yield from these aquifers.
[00057] Post-processing, the AI generates a detailed aquifer map. This map, rich in insights, demarcates regions containing aquifers, visually depicting their depths and other properties. This visualization provides stakeholders a comprehensive understanding of the groundwater scenario. For instance, once processed, the AI outputs a map for the agricultural region. This map vividly colors areas with aquifers, labels their depths, and provides additional meta-information upon hovering or clicking. Such a map could then be used to plan borewell locations, assess water availability, or design conservation projects.
[00058] A hallmark of this method 200 is the feedback loop between the AI processor and the VES instrument. The AI can provide real-time feedback, prompting the VES instrument to adjust its frequency or depth penetration. This ensures that the data being captured is optimized for the specific geological characteristics of the region. For instance, as VES progresses in a specific zone of the region, the AI, analyzing data in real-time, detects a potential anomaly or an area of interest. It sends feedback to the VES instrument to deepen its penetration or modify its frequency. Such adjustments might reveal a hidden aquifer layer or clarify an ambiguous data point.
[00059] In today's connected world, the power of aggregated data is unmatched. The method involves uploading the newly captured VES data to a cloud-based database. This database, a treasure trove of geoelectrical data from various regions, empowers the AI to refine its predictions, drawing insights from vast datasets. For instance, after the agricultural region's data is stored locally, it's also uploaded to the cloud. Here, it joins data from numerous other regions. Later, a VES operation in a neighboring region can leverage this aggregated data. The AI, accessing patterns from both regions, can make more informed predictions, enhancing the mapping accuracy.
[00060] While a 2D map provides a bird's eye view of the aquifers, a 3D visualization plunges the user deep into the earth, offering a layered perspective of the subsurface. Different layers in the visualization can depict varying depths, aquifer properties, or even non-aquifer layers like rocks or clay. For instance, on the aquifer map interface, users can switch to a 3D mode. This mode, almost like a subsurface journey, showcases aquifers as distinct layers. A user can rotate, zoom, or pan across these layers, gaining insights into how these aquifers spread and interact beneath the surface.
[00061] The AI's prowess in this method isn't just due to traditional machine learning but is amplified by the inclusion of deep learning neural networks. Trained on diverse geoelectrical datasets, these neural networks excel in pattern recognition, ensuring that aquifer predictions are accurate even in geologically complex terrains. For instance, the agricultural region, over its vast expanse, includes varied terrains – from rocky outcrops to sandy stretches. A traditional machine learning model might falter in consistently predicting aquifers across this diversity. However, the deep learning model, recognizing intricate patterns, ensures that the aquifer map is consistently accurate.
[00062] Referring to one or more preceding embodiments, the described method 200 marks a watershed moment in aquifer mapping. Merging the robustness of VES with the intelligence of AI, it promises unparalleled precision in groundwater mapping. From real-time VES optimizations to the immersive depths of 3D visualizations, the method stands poised to revolutionize water resource exploration, conservation, and management.
[00063] The above description is intended to be illustrative, and not restrictive. Although the present disclosure has been described with references to specific illustrative examples and implementations, it will be recognized that the present disclosure is not limited to the examples and implementations described. The scope of the disclosure should be determined with reference to the following claims, along with the full scope of equivalents to which the claims are entitled.
[00064] Modifications, additions, or omissions may be made to the systems and apparatuses described herein without departing from the scope of the disclosure. The components of the systems and apparatuses may be integrated or separated. Moreover, the operations of the systems and apparatuses may be performed by more, fewer, or other components. Additionally, operations of the systems and apparatuses may be performed using any suitable logic comprising software, hardware, and/or other logic. As used in this document, “each” refers to each member of a set or each member of a subset of a set.
[00065] The term “memory,” as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage such as physical storage media. Furthermore, a single memory may encompass and in a scenario wherein computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
[00066] Throughout the present disclosure, the term ‘server’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks.
[00067]

Claims
I/We Claim:
1. A system for aquifer mapping of a region using Vertical Electrical Sounding (VES) and artificial intelligence, comprising:
a Vertical Electrical Sounding instrument configured to capture geoelectrical data from various depths in the region;
a data storage module to store the captured geoelectrical data;
an artificial intelligence processor trained on historical geoelectrical and aquifer data; and
an output interface to display an aquifer map, wherein the artificial intelligence processor is configured to analyze the captured geoelectrical data to predict and display the aquifer map on the output interface.
2. The system of claim 1, wherein the Vertical Electrical Sounding instrument includes sensors capable of adjusting frequency and depth penetration based on real-time feedback from the artificial intelligence processor.
3. The system of claim 1, further comprising a cloud-based database that aggregates geoelectrical data from multiple VES instruments across various regions, enabling the artificial intelligence processor to refine and optimize its predictions.
4. The system of claim 1, wherein the output interface provides 3D visualizations of the aquifer map, with layers representing different depth intervals and aquifer properties.
5. The system of claim 1, wherein the artificial intelligence processor employs a deep learning neural network, trained on a diverse set of geoelectrical datasets, to ensure accurate aquifer mapping across varying geological terrains.
6. A method for creating an aquifer map of a region using Vertical Electrical Sounding and artificial intelligence, the method comprising:
conducting Vertical Electrical Sounding to obtain geoelectrical data at various depths in the region;
storing the obtained geoelectrical data in a data storage module;
processing the stored geoelectrical data using an artificial intelligence processor trained on historical geoelectrical and aquifer data; and
generating and displaying an aquifer map based on the processed data.
7. The method of claim 6, further comprising adjusting the frequency and depth penetration of the Vertical Electrical Sounding instrument based on real-time feedback from the artificial intelligence processor, optimizing data collection for the region's specific geological characteristics.
8. The method of claim 6, further comprising:
uploading the obtained geoelectrical data to a cloud-based database; and
accessing aggregated geoelectrical data from multiple regions from the cloud-based database to refine and optimize aquifer predictions.
9. The method of claim 6, wherein the step of displaying the aquifer map includes presenting 3D visualizations with different layers representing varying depths and aquifer properties.
10. The method of claim 6, wherein the artificial intelligence processing step involves using a deep learning neural network that has been trained on a diverse set of geoelectrical datasets, enhancing the accuracy of aquifer mapping predictions across diverse geological terrains.

VERTICAL ELECTRICAL SOUNDING METHOD WITH ARTIFICIAL INTELLIGENCE TO CREATE AN AQUIFER MAP OF A REGION
Abstract
The invention relates to a system for aquifer mapping using Vertical Electrical Sounding (VES) combined with artificial intelligence. The system comprises a VES instrument designed to acquire geoelectrical data from varying depths, a storage module for said data, an AI processor trained on historical geoelectrical and aquifer datasets, and an output interface to visualize an aquifer map. Notably, the AI processor analyzes the acquired data to produce the aquifer map, which is rendered on the interface. Enhanced features include real-time adjustment capabilities for the VES instrument based on AI feedback, a cloud database for data aggregation from multiple VES instruments, 3D visualization of the aquifer map detailing depth intervals and aquifer attributes, and the use of a deep learning neural network by the AI processor for enhanced mapping accuracy across diverse geological contexts.
, Claims:Claims
I/We Claim:
1. A system for aquifer mapping of a region using Vertical Electrical Sounding (VES) and artificial intelligence, comprising:
a Vertical Electrical Sounding instrument configured to capture geoelectrical data from various depths in the region;
a data storage module to store the captured geoelectrical data;
an artificial intelligence processor trained on historical geoelectrical and aquifer data; and
an output interface to display an aquifer map, wherein the artificial intelligence processor is configured to analyze the captured geoelectrical data to predict and display the aquifer map on the output interface.
2. The system of claim 1, wherein the Vertical Electrical Sounding instrument includes sensors capable of adjusting frequency and depth penetration based on real-time feedback from the artificial intelligence processor.
3. The system of claim 1, further comprising a cloud-based database that aggregates geoelectrical data from multiple VES instruments across various regions, enabling the artificial intelligence processor to refine and optimize its predictions.
4. The system of claim 1, wherein the output interface provides 3D visualizations of the aquifer map, with layers representing different depth intervals and aquifer properties.
5. The system of claim 1, wherein the artificial intelligence processor employs a deep learning neural network, trained on a diverse set of geoelectrical datasets, to ensure accurate aquifer mapping across varying geological terrains.
6. A method for creating an aquifer map of a region using Vertical Electrical Sounding and artificial intelligence, the method comprising:
conducting Vertical Electrical Sounding to obtain geoelectrical data at various depths in the region;
storing the obtained geoelectrical data in a data storage module;
processing the stored geoelectrical data using an artificial intelligence processor trained on historical geoelectrical and aquifer data; and
generating and displaying an aquifer map based on the processed data.
7. The method of claim 6, further comprising adjusting the frequency and depth penetration of the Vertical Electrical Sounding instrument based on real-time feedback from the artificial intelligence processor, optimizing data collection for the region's specific geological characteristics.
8. The method of claim 6, further comprising:
uploading the obtained geoelectrical data to a cloud-based database; and
accessing aggregated geoelectrical data from multiple regions from the cloud-based database to refine and optimize aquifer predictions.
9. The method of claim 6, wherein the step of displaying the aquifer map includes presenting 3D visualizations with different layers representing varying depths and aquifer properties.
10. The method of claim 6, wherein the artificial intelligence processing step involves using a deep learning neural network that has been trained on a diverse set of geoelectrical datasets, enhancing the accuracy of aquifer mapping predictions across diverse geological terrains.